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Manual medical billing is full of long-standing problems that cause delays and errors. While automation seems like the ideal solution, it could do more harm than good if healthcare institutions don’t carefully consider integration-related ethical concerns. Fortunately, there are ways to ethically leverage the benefits of automation in health care. Automation’s Role in the Medical Billing Process Despite several ethical concerns, the healthcare industry leverages automation because it’s cost-effective and it improves productivity. These benefits are more than welcome amid shortages of medical professionals and rising operating expenses. Administrative costs account for 15%-25% of health expenditures in the United States — and billing and coding are the leading drivers. Another significant benefit of this technology is the elimination of human error. For example, autonomous coding software can assign codes to patients’ charts with 96% code-over-code accuracy for most outpatient specialties. These accuracy and efficiency improvements could improve patient outcomes and satisfaction. This technology automates repetitive processes, meaning it can automatically assign billing codes, identify record inconsistencies, detect unusual billing patterns, and remind patients of upcoming payments. Hospitals could substantially accelerate workflows with it. They can also save money by integrating this technology into their existing administrative processes. Examples of Medical Billing Automation Technology While the healthcare sector uses various automation technologies, the most common are: Robot Process Automation (RPA) Unlike the name suggests, RPA has nothing to do with robots. Rather, it’s a software technology following a predefined workflow. It completes its task repetitively or in response to specific triggers. It can handle various administrative tasks independently. Revenue Cycle Management (RCM) RCM is the process hospitals use to track patient-related financials. Seventy-five percent of hospitals and health systems in the U.S. deployed RCM technology during the COVID-19 pandemic. This technology automates everything from coding to collections. Artificial Intelligence (AI) AI is more versatile than RPA or RCM, as hospitals can tailor it to their needs. They can use it for backend administrative processes, deploy chatbots to answer frequently asked payment questions, or leverage machine learning (ML) for predictive forecasting. 5 Ethical Concerns of Automating Health Care Finance While automation technology could benefit hospitals, it may negatively affect patients. Healthcare institutions should examine several ethical considerations: System Apathy Automated tools replace humans in sensitive medical billing roles. Concerningly, they’re incapable of emotion and don’t care about context but interact with vulnerable people — those who have just had a medical scare, lost a loved one, or can’t afford to pay for their medication. Considering empathy contributes toward rebuilding patients’ trust and protecting companies’ reputations, automation technology may negatively affect hospitals just as much as patients. People generally dislike dealing with apathetic machines over humans. Algorithmic Bias Bias is a massive issue with AI systems since unconscious prejudices are often incorporated into training. Even if it’s unintentional, bias has long-lasting adverse impacts. For example, an ML model might disproportionately deny one demographic’s claims if it primarily trains on another. System Transparency As automation technology advances, tracing the logical framework it follows to reach conclusions becomes increasingly challenging. For instance, the black box issue with AI revolves around its inability to display a decision-making process, meaning every step before its output is essentially a mystery. A lack of transparency can worsen patient satisfaction and trust. Patient Privacy Industry experts worry automation will inadvertently expose patients’ personally identifiable information (PII), medical records, or financial details to bad actors. Considering that 30% of healthcare institutions automating with RCM use at least two third-party vendors, their fears aren’t unfounded. Outsourcing to run and maintain software can open them to data breach risks. Job Losses Medical billing professionals update patient information, generate invoices, process insurance claims, collect payments, and resolve disputes. Unfortunately, automation technology can take over their jobs almost entirely — meaning most face unemployment in the face of widespread adoption. How to Ensure Medical Billing Automation Is Ethical While medical billing automation aims to streamline administrative processes and improve interactions, it could inadvertently harm relationships and outcomes. You’d need to navigate ethical concerns carefully to increase accuracy and efficiency without compromising the equitable treatment of patients and workers. You can overcome automation’s ethical concerns with strategic integration and oversight. For example, you could develop an AI governance framework to ensure deployment and utilization stays transparent and moral. After all, ongoing management is vital to long-term success. Automated technology should also have a human element, whether someone is on standby for escalation or a professional audits the system’s output. This way, you can connect with patients and recognize how context may affect their ability to pay. Working with people can increase revenue while preserving reputation. Notably, even those not in charge of the technologies should have some training to ensure their occasional usage is equitable, safe, and secure. After all, you can only preserve patient privacy and help guarantee optimal outcomes if you fully understand how to navigate and manage the tools you leverage. The Moral Implications of Automated Medical Billing While you could increase accuracy and productivity with automation, it could also lower patient satisfaction and result in more disputes. Navigating the complex ethics around integration is time-consuming, but it is worth the effort to improve patient outcomes and preserve facilities’ reputations. Manual medical billing is full of long-standing problems that cause delays and errors. While automation seems like the ideal solution, it could do more harm than good if healthcare institutions don’t carefully consider integration-related ethical concerns. Fortunately, there are ways to ethically leverage the benefits of automation in health care. Automation’s Role in the Medical Billing Process Automation’s Role in the Medical Billing Process Despite several ethical concerns, the healthcare industry leverages automation because it’s cost-effective and it improves productivity. These benefits are more than welcome amid shortages of medical professionals and rising operating expenses. Administrative costs account for 15%-25% of health expenditures in the United States — and billing and coding are the leading drivers. 15%-25% of health expenditures 15%-25% of health expenditures Another significant benefit of this technology is the elimination of human error. For example, autonomous coding software can assign codes to patients’ charts with 96% code-over-code accuracy for most outpatient specialties. These accuracy and efficiency improvements could improve patient outcomes and satisfaction. with 96% code-over-code accuracy with 96% code-over-code accuracy This technology automates repetitive processes, meaning it can automatically assign billing codes, identify record inconsistencies, detect unusual billing patterns, and remind patients of upcoming payments. Hospitals could substantially accelerate workflows with it. They can also save money by integrating this technology into their existing administrative processes. Examples of Medical Billing Automation Technology While the healthcare sector uses various automation technologies, the most common are: Robot Process Automation (RPA) Unlike the name suggests, RPA has nothing to do with robots. Rather, it’s a software technology following a predefined workflow. It completes its task repetitively or in response to specific triggers. It can handle various administrative tasks independently. Revenue Cycle Management (RCM) RCM is the process hospitals use to track patient-related financials. Seventy-five percent of hospitals and health systems in the U.S. deployed RCM technology during the COVID-19 pandemic. This technology automates everything from coding to collections. deployed RCM technology deployed RCM technology Artificial Intelligence (AI) AI is more versatile than RPA or RCM, as hospitals can tailor it to their needs. They can use it for backend administrative processes, deploy chatbots to answer frequently asked payment questions, or leverage machine learning (ML) for predictive forecasting. 5 Ethical Concerns of Automating Health Care Finance While automation technology could benefit hospitals, it may negatively affect patients. Healthcare institutions should examine several ethical considerations: System Apathy Automated tools replace humans in sensitive medical billing roles. Concerningly, they’re incapable of emotion and don’t care about context but interact with vulnerable people — those who have just had a medical scare, lost a loved one, or can’t afford to pay for their medication. Considering empathy contributes toward rebuilding patients’ trust and protecting companies’ reputations, automation technology may negatively affect hospitals just as much as patients. People generally dislike dealing with apathetic machines over humans. contributes toward rebuilding patients’ trust contributes toward rebuilding patients’ trust Algorithmic Bias Bias is a massive issue with AI systems since unconscious prejudices are often incorporated into training. Even if it’s unintentional, bias has long-lasting adverse impacts. For example, an ML model might disproportionately deny one demographic’s claims if it primarily trains on another. System Transparency As automation technology advances, tracing the logical framework it follows to reach conclusions becomes increasingly challenging. For instance, the black box issue with AI revolves around its inability to display a decision-making process , meaning every step before its output is essentially a mystery. A lack of transparency can worsen patient satisfaction and trust. inability to display a decision-making process inability to display a decision-making process Patient Privacy Industry experts worry automation will inadvertently expose patients’ personally identifiable information (PII), medical records, or financial details to bad actors. Considering that 30% of healthcare institutions automating with RCM use at least two third-party vendors, their fears aren’t unfounded. Outsourcing to run and maintain software can open them to data breach risks. 30% of healthcare institutions 30% of healthcare institutions Job Losses Medical billing professionals update patient information, generate invoices, process insurance claims, collect payments, and resolve disputes. Unfortunately, automation technology can take over their jobs almost entirely — meaning most face unemployment in the face of widespread adoption. How to Ensure Medical Billing Automation Is Ethical While medical billing automation aims to streamline administrative processes and improve interactions, it could inadvertently harm relationships and outcomes. You’d need to navigate ethical concerns carefully to increase accuracy and efficiency without compromising the equitable treatment of patients and workers. You can overcome automation’s ethical concerns with strategic integration and oversight. For example, you could develop an AI governance framework to ensure deployment and utilization stays transparent and moral. After all, ongoing management is vital to long-term success. Automated technology should also have a human element, whether someone is on standby for escalation or a professional audits the system’s output. This way, you can connect with patients and recognize how context may affect their ability to pay. Working with people can increase revenue while preserving reputation. Notably, even those not in charge of the technologies should have some training to ensure their occasional usage is equitable, safe, and secure. After all, you can only preserve patient privacy and help guarantee optimal outcomes if you fully understand how to navigate and manage the tools you leverage. The Moral Implications of Automated Medical Billing While you could increase accuracy and productivity with automation, it could also lower patient satisfaction and result in more disputes. Navigating the complex ethics around integration is time-consuming, but it is worth the effort to improve patient outcomes and preserve facilities’ reputations.

The Ethics of Automation in Medical Billing

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Muhammed Yusuf Kocyigit, Boston University;
(2) Anietie Andy, University of Pennsylvania;
(3) Derry Wijaya, Boston University. Table of Links Abstract and Intro
Related Works
Data
Method
Analysis and Results
Conclusion
Limitations
Ethics Statement and References
Appendix: Toxicity Measurement
Appendix: Correlation Over Time
Appendix: Wikidata
Appendix: Hyperparameter Sensitivity Ethics Statement Language and context are not simple things especially considering we are working on a summarization of n-grams instead of sentences. Hence the results and analysis must be evaluated with this in mind. Also the identification of bias can sometimes inadvertently be affected by the researchers’ own biases, despite our best efforts to be rigorous in our methodology. A risk lies in potentially over-simplifying or misrepresenting complex socio-cultural dynamics. We have worked to minimize this risk through methodological transparency, iterative analyses, and peer review. The study inherently involves discussing racially biased language, which could cause distress to some readers, particularly those from communities affected by such biases. Our presentation aims to be sensitive, focusing on systemic patterns rather than individuals or specific works. Lastly, there is a potential risk of misuse of our findings. The purpose of our study is to raise awareness and prompt action towards eliminating bias, but we are aware that the same information could be used to support biased arguments or perpetuate harmful stereotypes even though the content of books are not a representation of reality but the representation of the perception of the authors which could be source of bias in some cases. On the positive side, our research contributes to a better understanding of systemic racial biases in historical and cultural contexts. This deeper awareness can help us understand what we perceive as normal or what the next generation will perceive as normal putting a external reality check on the culture we inherit without knowing. As a part of our ethical commitment, we will make our data and code public to invite critique, replication, and improvement. Our research is not an end but a beginning, providing a stepping stone for further inquiries into racial bias. References Akyurek, A. F.; Kocyigit, M. Y.; Paik, S.; and Wijaya, D. T. ¨ 2022a. Challenges in Measuring Bias via Open-Ended Language Generation. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), 76–76. Akyurek, A. F.; Paik, S.; Kocyigit, M.; Akbiyik, S.; Run- ¨ yun, S. L.; and Wijaya, D. 2022b. On Measuring Social Biases in Prompt-Based Multi-Task Learning. In Findings of the Association for Computational Linguistics: NAACL 2022, 551–564. An, J.; Kwak, H.; and Ahn, Y.-Y. 2018. SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment. ArXiv, abs/1806.05521. Arseniev-Koehler, A. 2021. Theoretical foundations and limits of word embeddings: what types of meaning can they capture? Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021a. Investigating the Role of Negatives in Contrastive Representation Learning. Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021b. Investigating the Role of Negatives in Contrastive Representation Learning. Awasthi, P.; Dikkala, N.; and Kamath, P. 2022. Do More Negative Samples Necessarily Hurt in Contrastive Learning? Bassignana, E.; Basile, V.; and Patti, V. 2018. Hurtlex: A Multilingual Lexicon of Words to Hurt. In Italian Conference on Computational Linguistics. Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In NIPS Farrell, T.; Araque, O.; Fern ´ andez, M.; and Alani, H. 2020. ´ On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere. 12th ACM Conference on Web Science. Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16). Gerbner, G. 1998. Cultivation analysis: An overview. Mass communication and society, 1(3-4): 175–194. Goldberg, Y.; and Orwant, J. 2013. A Dataset of SyntacticNgrams over Time from a Very Large Corpus of English Books. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 241–247. Atlanta, Georgia, USA: Association for Computational Linguistics. Hagiwara, N.; Alderson, C. J.; and Mezuk, B. 2016. Differential effects of personal-level vs group-level racial discrimination on health among Black Americans. Ethnicity & disease, 26(3): 453. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1489–1501. Berlin, Germany: Association for Computational Linguistics. Harris, Z. S. 1954. Distributional Structure. ¡i¿WORD¡/i¿, 10(2-3): 146–162. Joseph, K.; and Morgan, J. H. 2020. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? In Annual Meeting of the Association for Computational Linguistics. Kozlowski, A. C.; Taddy, M.; and Evans, J. A. 2019. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review, 84(5): 905–949. Loon, A. v.; Giorgi, S.; Willer, R.; and Eichstaedt, J. 2022. Negative Associations in Word Embeddings Predict Antiblack Bias across Regions–but Only via Name Frequency. Proceedings of the International AAAI Conference on Web and Social Media, 16(1): 1419–1424. Lucy, L.; Tadimeti, D.; and Bamman, D. 2022. Discovering Differences in the Representation of People using Contextualized Semantic Axes. ArXiv, abs/2210.12170. Martinc, M.; Kralj Novak, P.; and Pollak, S. 2020. Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 4811–4819. Marseille, France: European Language Resources Association. ISBN 979-10-95546-34-4. Sorato, D.; Zavala-Rojas, D.; and del Carme Colominas Ventura, M. 2021. Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, 34–46. Online: Australasian Language Technology Association. Tripodi, R.; Warglien, M.; Sullam, S. L.; and Paci, D. 2019. Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914. In LChange@ACL. Valentini, F.; Slezak, D. F.; and Altszyler, E. 2022. The Dependence on Frequency of Word Embedding Similarity Measures. ArXiv, abs/2211.08203. Wijaya, D. T.; and Yeniterzi, R. 2011. Understanding semantic change of words over centuries. In Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, 35–40. Xu, H.; Zhang, Z.; Wu, L.; and Wang, C.-J. 2019. The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books. PLOS ONE, 14(11): e0225385. This paper is available on arxiv under CC 4.0 license. Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. Table of Links Abstract and Intro Related Works Data Method Analysis and Results Conclusion Limitations Ethics Statement and References Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Wikidata Appendix: Hyperparameter Sensitivity Abstract and Intro Abstract and Intro Related Works Related Works Data Data Method Method Analysis and Results Analysis and Results Conclusion Conclusion Limitations Limitations Ethics Statement and References Ethics Statement and References Appendix: Toxicity Measurement Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Correlation Over Time Appendix: Wikidata Appendix: Wikidata Appendix: Hyperparameter Sensitivity Appendix: Hyperparameter Sensitivity Ethics Statement Language and context are not simple things especially considering we are working on a summarization of n-grams instead of sentences. Hence the results and analysis must be evaluated with this in mind. Also the identification of bias can sometimes inadvertently be affected by the researchers’ own biases, despite our best efforts to be rigorous in our methodology. A risk lies in potentially over-simplifying or misrepresenting complex socio-cultural dynamics. We have worked to minimize this risk through methodological transparency, iterative analyses, and peer review. The study inherently involves discussing racially biased language, which could cause distress to some readers, particularly those from communities affected by such biases. Our presentation aims to be sensitive, focusing on systemic patterns rather than individuals or specific works. Lastly, there is a potential risk of misuse of our findings. The purpose of our study is to raise awareness and prompt action towards eliminating bias, but we are aware that the same information could be used to support biased arguments or perpetuate harmful stereotypes even though the content of books are not a representation of reality but the representation of the perception of the authors which could be source of bias in some cases. On the positive side, our research contributes to a better understanding of systemic racial biases in historical and cultural contexts. This deeper awareness can help us understand what we perceive as normal or what the next generation will perceive as normal putting a external reality check on the culture we inherit without knowing. As a part of our ethical commitment, we will make our data and code public to invite critique, replication, and improvement. Our research is not an end but a beginning, providing a stepping stone for further inquiries into racial bias. References Akyurek, A. F.; Kocyigit, M. Y.; Paik, S.; and Wijaya, D. T. ¨ 2022a. Challenges in Measuring Bias via Open-Ended Language Generation. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), 76–76. Akyurek, A. F.; Paik, S.; Kocyigit, M.; Akbiyik, S.; Run- ¨ yun, S. L.; and Wijaya, D. 2022b. On Measuring Social Biases in Prompt-Based Multi-Task Learning. In Findings of the Association for Computational Linguistics: NAACL 2022, 551–564. An, J.; Kwak, H.; and Ahn, Y.-Y. 2018. SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment. ArXiv, abs/1806.05521. Arseniev-Koehler, A. 2021. Theoretical foundations and limits of word embeddings: what types of meaning can they capture? Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021a. Investigating the Role of Negatives in Contrastive Representation Learning. Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021b. Investigating the Role of Negatives in Contrastive Representation Learning. Awasthi, P.; Dikkala, N.; and Kamath, P. 2022. Do More Negative Samples Necessarily Hurt in Contrastive Learning? Bassignana, E.; Basile, V.; and Patti, V. 2018. Hurtlex: A Multilingual Lexicon of Words to Hurt. In Italian Conference on Computational Linguistics. Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In NIPS Farrell, T.; Araque, O.; Fern ´ andez, M.; and Alani, H. 2020. ´ On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere. 12th ACM Conference on Web Science. Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16). Gerbner, G. 1998. Cultivation analysis: An overview. Mass communication and society, 1(3-4): 175–194. Goldberg, Y.; and Orwant, J. 2013. A Dataset of SyntacticNgrams over Time from a Very Large Corpus of English Books. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 241–247. Atlanta, Georgia, USA: Association for Computational Linguistics. Hagiwara, N.; Alderson, C. J.; and Mezuk, B. 2016. Differential effects of personal-level vs group-level racial discrimination on health among Black Americans. Ethnicity & disease, 26(3): 453. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1489–1501. Berlin, Germany: Association for Computational Linguistics. Harris, Z. S. 1954. Distributional Structure. ¡i¿WORD¡/i¿, 10(2-3): 146–162. Joseph, K.; and Morgan, J. H. 2020. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? In Annual Meeting of the Association for Computational Linguistics. Kozlowski, A. C.; Taddy, M.; and Evans, J. A. 2019. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review, 84(5): 905–949. Loon, A. v.; Giorgi, S.; Willer, R.; and Eichstaedt, J. 2022. Negative Associations in Word Embeddings Predict Antiblack Bias across Regions–but Only via Name Frequency. Proceedings of the International AAAI Conference on Web and Social Media, 16(1): 1419–1424. Lucy, L.; Tadimeti, D.; and Bamman, D. 2022. Discovering Differences in the Representation of People using Contextualized Semantic Axes. ArXiv, abs/2210.12170. Martinc, M.; Kralj Novak, P.; and Pollak, S. 2020. Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 4811–4819. Marseille, France: European Language Resources Association. ISBN 979-10-95546-34-4. Sorato, D.; Zavala-Rojas, D.; and del Carme Colominas Ventura, M. 2021. Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, 34–46. Online: Australasian Language Technology Association. Tripodi, R.; Warglien, M.; Sullam, S. L.; and Paci, D. 2019. Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914. In LChange@ACL. Valentini, F.; Slezak, D. F.; and Altszyler, E. 2022. The Dependence on Frequency of Word Embedding Similarity Measures. ArXiv, abs/2211.08203. Wijaya, D. T.; and Yeniterzi, R. 2011. Understanding semantic change of words over centuries. In Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, 35–40. Xu, H.; Zhang, Z.; Wu, L.; and Wang, C.-J. 2019. The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books. PLOS ONE, 14(11): e0225385.

A Novel Method for Analysing Racial Bias: Ethics Statement and References

GenAI in Elections: Reshaping Campaigns, Raising Ethical Concerns

নির্বাচনে GenAI: প্রচারাভিযান পুনর্নির্মাণ, নৈতিক উদ্বেগ উত্থাপন

GenAI bei Wahlen: Kampagnen neu gestalten, ethische Bedenken aufwerfen

GenAI en las elecciones: remodelando campañas, planteando preocupaciones éticas

GenAI dans les élections : remodeler les campagnes et soulever des préoccupations éthiques

चुनावों में GenAI: अभियानों को नया स्वरूप देना, नैतिक चिंताएं बढ़ाना

選挙における GenAI: 選挙運動の再構築、倫理的懸念の高まり

선거에서의 GenAI: 캠페인 재편, 윤리적 우려 제기

GenAI nas eleições: remodelando campanhas, levantando preocupações éticas

ГенИИ на выборах: изменение избирательных кампаний, рост этических проблем

Seçimlerde GenAI: Kampanyaları Yeniden Şekillendirmek, Etik Kaygıları Artırmak

GenAI trong bầu cử: Định hình lại các chiến dịch, nâng cao mối quan ngại về đạo đức

GenAI 在选举中的应用：重塑竞选活动，引发道德担忧

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps 6 Conclusion Understanding how recommender systems shape our behaviour is essential to avoid manipulation. In this paper, we investigated the Amazon recommender system concerning whether it has made it harder to change preferences. By analysing the Barrier-to-Exit (Rakova & Chowdhury, 2019) of more than 50,000 users, we found a highly significant growth in Barrier-to-Exit over time, which indicates that it has indeed become harder to change preferences for the analysed users. However, sampling bias induced by the calculation of Barrier-to-Exit makes it difficult to draw conclusions about the general population of Amazon customers. This highlights the dilemma of portability in measuring socio-technical systems (Selbst et al., 2019): accurately evaluating a concept like ”changing preferences” requires adapting to the context of the system, which makes it more difficult to generalise (and compare) to other systems. Comparing recommender systems is necessary for ensuring that these respect human autonomy (Varshney, 2020) and live up to new regulations such as the EU AI Act (Kop, 2021). Further work, should aim to create auditing procedures and metrics that allow third parties to measure potential preference manipulation in a way that fits within the context of the industry and allows for comparisons between different systems. This will help assess the pressures of Surveillance Capitalism (Zuboff, 2019) on human autonomy. References Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of memory and language, 59 (4), 390–412. 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Ensuring Ethical AI: Lessons from Amazon's Barrier-to-Exit Analysis

Recommendations by Concise User Profiles from Review Text: Ethics Statement and References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Jinrui Yang, School of Computing & Information Systems, The University of Melbourne (Email: jinruiy@student.unimelb.edu.au); (2) Timothy Baldwin, School of Computing & Information Systems, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence, UAE (Email: (tbaldwin,trevor.cohn)@unimelb.edu.au); (3) Trevor Cohn, School of Computing & Information Systems, The University of Melbourne. Table of Links Abstract and Intro Background and Related Work Multi-EuP Experiments and Findings Language Bias Discussion Conclusion, Limitations, Ethics Statement, Acknowledgements, References, and Appendix 6 Conclusion In this paper, we introduce Multi-EuP, a novel dataset for multilingual information retrieval across 24 languages, collected from European Parliament debates. The demographic information provided by the Multi-EuP dataset serves a dual purpose: not only does it contribute to multilingual retrieval tasks, but it also holds significant potential for advancing research in the realm of fairness and bias. This dataset can play a pivotal role in investigating issues of equitable representations and mitigation of biases within document ranking settings. Multi-EuP facilitates diverse information retrieval (IR) scenarios, encompassing one-vs-one, one-vs-many, and many-vs-many settings. We demonstrated the utility of Multi-EuP as a benchmark for evaluating both monolingual and multilingual IR. Our study reveals the presence of language bias in multilingual IR when employing BM25. We further validate the effectiveness of mitigating this bias through the strategic implementation of whitespace as a language tokenizer. We propose to conduct future work in three main areas. First, we intend to expand our investigation of language bias to encompass a broader range of ranking methods, including neural methods such as mDPR (Zhang et al., 2021), mColBERT (Lawrie et al., 2023) and PLAID-X(Santhanam et al., 2022). Second, we will expand the dataset by developing an automated API to retrieve data published by the European Parliament (EP), thereby ensuring realtime synchronization of our dataset. Lastly, our current experiments have explored language bias only, but we plan to further investigate gender bias, age bias, and nationality bias. Limitations The limitations of the Multi-EuP dataset are notable but navigable. Primarily, the temporal coverage of the dataset is confined to the past three years. This temporal constraint arises due to the fact that, preceding 2020, documents released by the EU were predominantly available in mono-lingual versions only. However, a potential remedy lies in the amalgamation of the Europarl (Koehn, 2005) collection, enabling a more comprehensive and holistic MultiEuP dataset. Furthermore, it is worth noting the domain skew of the dataset, in that Multi-EuP inevitably centers on political matters. While this presents challenges, particularly in terms of the intricate nuances of political language, it inherently serves as an excellent foundational stepping stone for delving into the intricacies of multilingual retrieval. We believe, however, that this dataset can serve as a launching pad for broader explorations encompassing crossdomain and open-domain transfer learning scenarios, thus contributing to the broader landscape of language understanding and retrieval. Ethics Statement The dataset contains publicly-available EP data that does not include personal or sensitive information, with the exception of information relating to public officeholders, e.g., the names of the active members of the European Parliament, European Council, or other official administration bodies. The collected data is licensed under the Creative Commons Attribution 4.0 International licence. [8] Acknowledgements This research was funded by Melbourne Research Scholarship and undertaken using the LIEF HPCGPGPU Facility hosted at the University of Melbourne. This facility was established with the assistance of LIEF Grant LE170100200. We would like to thank George Buchanan for providing valuable feedback. References Luiz Henrique Bonifacio, Israel Campiotti, Roberto de Alencar Lotufo, and Rodrigo Frassetto Nogueira. 2021. mMARCO: A multilingual version of MS MARCO passage ranking dataset. CoRR, abs/2108.13897. Ilias Chalkidis, Manos Fergadiotis, and Ion Androutsopoulos. 2021. MultiEURLEX - a multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6974–6996, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. 8 https://eur-lex.europa.eu/cont Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020. TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. Transactions of the Association for Computational Linguistics, 8:454–470. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for opendomain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781, Online. Association for Computational Linguistics. Omar Khattab and Matei Zaharia. 2020. Colbert: Efficient and effective passage search via contextualized late interaction over BERT. CoRR, abs/2004.12832. Philipp Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In Proceedings of Machine Translation Summit X: Papers, pages 79–86, Phuket, Thailand. Dawn Lawrie, Eugene Yang, Douglas W. Oard, and James Mayfield. 2023. Neural approaches to multilingual information retrieval. arXiv cs.IR 2209.01335. Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, JhengHong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations. https://github.com/ castorini/pyserini. Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. CoRR, abs/1611.09268. Ella Rabinovich, Raj Nath Patel, Shachar Mirkin, Lucia Specia, and Shuly Wintner. 2017. Personalized machine translation: Preserving original author traits. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1074–1084, Valencia, Spain. Association for Computational Linguistics. Razieh Rahimi, Azadeh Shakery, and Irwin King. 2015. Multilingual information retrieval in the language modeling framework. Information Retrieval Journal, 18:246–281. Keshav Santhanam, Omar Khattab, Christopher Potts, and Matei Zaharia. 2022. PLAID: An efficient engine for late interaction retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM ’22, page 1747–1756, New York, NY, USA. Association for Computing Machinery. Jörg Tiedemann and Santhosh Thottingal. 2020. OPUSMT – building open translation services for the world. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 479–480, Lisboa, Portugal. European Association for Machine Translation. Eva Vanmassenhove and Christian Hardmeier. 2018. Europarl datasets with demographic speaker information. In Proceedings of the 21st Annual Conference of the European Association for Machine Translation, page 391, Alicante, Spain. Denny Vrandeciˇ c and Markus Krötzsch. 2014. ´ Wikidata: A free collaborative knowledge base. Communications of the ACM, 57:78–85. Konrad Wojtasik, Vadim Shishkin, Kacper Wołowiec, Arkadiusz Janz, and Maciej Piasecki. 2023. BEIRPL: Zero shot information retrieval benchmark for the Polish language. arXiv cs.IR 2305.19840. Peilin Yang, Hui Fang, and Jimmy Lin. 2017. Anserini: Enabling the use of lucene for information retrieval research. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1253–1256. Xinyu Zhang, Xueguang Ma, Peng Shi, and Jimmy Lin. 2021. Mr. TyDi: A multi-lingual benchmark for dense retrieval. arXiv cs.CL 2108.08787. A. Appendix [8] https://eur-lex.europa.eu/content/ legal-notice/legal-notice.html

Multi-EuP: Analysis of Bias in Information Retrieval - Conclusion, Limitations, and Ethics Statement

These days, social media plays an integral part in life, influencing how we communicate, obtain information, and share information with each other. Although less than 30 years old [1], we have seen a huge evolution of platforms like Facebook, Twitter, TikTok, and LinkedIn: Facebook is now Meta and owns WhatsApp, Instagram, and Threads; Twitter is X. Both are owned by billionaires who have anticipated technological trends, capitalized on these opportunities and pioneered the way for a rapidly innovative industry. The biggest driving success of social media, from a business point of view, is consumer data. A large majority of social media platforms are free of charge and are able to operate this way because you’re allowing them access to your data in return. With this, companies like Meta have been able to shape social media content and interactions. This is something that can be viewed as beneficial or unethical depending on the intent and use case, with issues like privacy and manipulation far from uncommon now. In this article, we will be delving into the ethics of using and algorithms in social media, examining how they help personalize content, potentially leading to bias and echo chambers, and the future of AI in social media platforms. AI A Brief History of Algorithms and Their Increasing Use Cases in Social Media ‘An algorithm is a series of instructions designed to solve specific problems, perform tasks, or make decisions’ [2]. In social media, these instructions govern user experiences such as content ranking, filtering, and personalization, typically based on trends for different consumer groups. This allows you as the user to find content that will interest you much faster, as it is likely to appear on For You Pages without you having to search for it. Algorithms haven’t always been used in social media. It was in 2009 that Tumblr and Facebook introduced ranking metrics and personalized feeds. In 2012, more followed suit. Facebook upped its algorithmic use in its news feed and introduced sponsored content [3]. began a “semi-structured feed,” and YouTube introduced a ranking algorithm, prioritizing watch time over quantity [4]. Since then, algorithms’ roles in such social media have only continued to increase. By 2015, machine learning was starting to play a role in sorting and filtering algorithms, underpinned by developments in Big Data efforts. 2016’s political events like the Trump election and Brexit referendum (and a pivotal year in the Cambridge Analytica scandal) then brought to public attention some of the ethical dilemmas surrounding such heavy and increasing use of algorithms and user data collection in social media. LinkedIn These days, AI-powered algorithms are built so precisely they recommend content based on your specific previous interactions on the platform, with growing user data allowing social media companies to eliminate a significant majority of any guesswork. Documentaries like The Social Dilemma [5] and The Great Hack [6] give some insight into just how engineered some of these algorithms are, often with the end goal of keeping a user on their platform for longer and driving interaction with the platform. While increased user interaction means more data for social media companies to collect, tweak, and repeat this process, this goal does not take into consideration what’s best for the user and wider society with regard to social media consumption. Ethical Issues Stemming from Algorithmic Bias Algorithms can perpetuate harmful biases, and in the context of social media, this often manifests as the reinforcement of stereotypes and limited exposure to diverse viewpoints. Culminated over time, this can fuel discrimination, polarise electorates, and even contribute to extremism and right-wing/autocratic populism, which exploit societal divisions. At a low level of impact, examples of algorithmic bias could include job postings for certain industries, e.g., LinkedIn being shown more to men than women, because the training data sets the algorithms were modeled on predict that more men are likely to work in those roles. While this doesn’t necessarily have an immediately obvious harmful impact, it creates a negative feedback loop that works against society’s goals to improve equality and representation across workplaces and specific industries. In this way, gender bias is perpetuated as a result of algorithmic bias by limiting opportunities for women to see those ads. Now, to delve deeper into the higher-level implications of algorithmic bias, we will examine the 2016 Trump election as a case study. Case Study: How Algorithms Affected Polarization/Echo Chambers in Trump 2016 Election Throughout the course of the 2016 US presidential election, research has shown that algorithms may have exacerbated political polarization by amplifying echo chambers and limiting the electorate’s exposure to diverse viewpoints. Many think it was this mechanism that allowed Trumpism to run rampant and also why so few people predicted his victory [7], as these dynamics were not fully understood nor visible during the election period. Even on social media, therefore, it seems true that ‘birds of a feather flock together’ [8]. Algorithms tailor content to align with users' preferences, shielding them from opposing views and opinions, and creating ‘filter bubbles’. In turn, some individuals within these filter bubbles continue the positive reinforcement of their views by further isolating themselves within digital environments where their beliefs and opinions are echoed and even more so reinforced. These are called ‘echo chambers’. Essentially, social media fosters an environment in which we all ingest such extremely personalized news and content feeds that we become blind to others’ differing perspectives. Currently, it is not clear from research whether social media only serves as a platform allowing echo chambers to emerge or whether its use of algorithms goes so far as to play a role in creating these echo chambers[8]. The 2016 Trump win shows how social media companies’ agendas can have widespread consequences affecting millions of people, which, while unintended, are still hugely impactful and also unethical. We know that social media algorithms are designed to maximize user engagement and retention by serving content tailored to individual preferences. Now, though, we also see how this applies even to political conversations, leveraging users’ political leanings and vulnerabilities. Who knows how the election would have played out if algorithms weren’t impacting people’s personal political beliefs? And how does this affect democracy and political stability? If people are not aware of these manipulations, can the outcomes be considered fair and valid? The Future of AI in Social Media The role of algorithms and AI in social media is only continuing to evolve alongside our innovations within those fields. From AI-generated content to AI-powered algorithms allowing the precision we touched on earlier, the ethical challenges that come with its integration into social media will similarly need to be increasingly considered. This needs to start with addressing algorithmic biases and promoting algorithmic transparency in social media platforms. Future policy considerations and regulations will play a crucial role in shaping the trajectory of AI use in social media. There is already growing attention being given to the need for algorithmic regulation and protection from the harm these invisible biases can cause, especially in the US. So far, initiatives include[9]: The “Algorithmic Accountability Act of 2022” The “Eliminating Bias in Algorithmic Systems Act of 2023” The “AI Bill of Rights” While there are more frameworks currently being drawn up in the proposal stages, there is still insufficient regulation of such powerful technologies when we consider the impact they already have. Striking a delicate balance between innovation and safeguarding user interests will require a collaborative approach involving technology companies, policymakers, and users alike. Social media does have the power to be a force for good, uniting communities and fostering inclusivity; however, without adequate frameworks and regulations protecting user rights, its use of AI and algorithms also has the power to be destructive to individuals and wider society. References [1] . The Evolution of Social Media: How Did It Begin, and Where Could It Go Next? | Maryville Online [2] Everything You Need to Know About Social Media Algorithms [3] Facebook's News Feed is 10 years old. This is how the site has changed | World Economic Forum [4] a history of - social media algorithms [5] Watch The Social Dilemma | Netflix Official Site [6] Watch The Great Hack | Netflix Official Site [7] The 'Filter Bubble' Explains Why Trump Won and You Didn't See It Coming [8] How social media echo chambers emerge (and why all your friends think Trump will lose) [9] Eliminating Bias in Algorithmic Systems Act of 2023

AI in Social Media: Ethical Considerations of AI and Algorithms in Shaping Social Media Interactions

সোশ্যাল মিডিয়াতে এআই: সোশ্যাল মিডিয়া ইন্টারঅ্যাকশন গঠনে এআই এবং অ্যালগরিদমের নৈতিক বিবেচনা

KI in sozialen Medien: Ethische Überlegungen zu KI und Algorithmen bei der Gestaltung von Interaktionen in sozialen Medien

IA en las redes sociales: consideraciones éticas de la IA y los algoritmos para dar forma a las interacciones en las redes sociales

L'IA dans les médias sociaux : considérations éthiques sur l'IA et les algorithmes pour façonner les interactions avec les médias sociaux

सोशल मीडिया में एआई: सोशल मीडिया इंटरैक्शन को आकार देने में एआई और एल्गोरिदम के नैतिक विचार

ソーシャルメディアにおける AI: ソーシャルメディアのインタラクションの形成における AI とアルゴリズムの倫理的考慮

소셜 미디어의 AI: 소셜 미디어 상호 작용 형성 시 AI 및 알고리즘에 대한 윤리적 고려 사항

IA nas mídias sociais: considerações éticas de IA e algoritmos na formação de interações nas mídias sociais

ИИ в социальных сетях: этические аспекты ИИ и алгоритмы формирования взаимодействия в социальных сетях

Sosyal Medyada Yapay Zeka: Sosyal Medya Etkileşimlerini Şekillendirmede Yapay Zeka ve Algoritmalara İlişkin Etik Hususlar

AI trong truyền thông xã hội: Những cân nhắc về mặt đạo đức đối với AI và thuật toán trong việc định hình các tương tác trên truyền thông xã hội

社交媒体中的人工智能：人工智能和算法在塑造社交媒体互动方面的伦理考量

Six months ago, we launched our startup Spheria.ai, a platform where people can create and host the AI version of themselves. As founders and consumers, we knew that from day 1, we wanted to build a product that would let people claim back their personal data from the abuse of tech giant, and protect user privacy… Today, I'm sharing our experience of having a Head of AI Ethics as the very first employee for your startup and how he turned our naive good intentions into actual science and a foundational framework so we can build a legitimate platform that people trust to create the AI version of themselves. Realizing how little we knew about AI Ethics As most founders, we are focused on delivering a great product and growing the user base while trying to stick to our moral compass. At the very first meeting, Alejandro, our new Head of AI Ethics, brought to us a framework that would help organize the big questions around Privacy and Ethics. Instead of just creating an unorganized list of principles, he immediately cross-referenced ethical frameworks that he had seen across his research and deployed them in existing organizations. Our Head of AI Ethics introduced us to a concept called Procedural Fairness, a framework used by the World Bank to make sure Fairness is at the center of its decisions and policies. So the biggest win (right after the second meeting!) was for us to graduate from a chaotic list of good intentions to using Ethics frameworks that are actually used by researchers in international organizations. Right after that second meeting, we defined four pillars for Spheria to use as our foundation for AI Ethics: Transparency, Fairness and Accountability, and Privacy. Using this framework allowed us to visualize the relations between each pillar and see how one idea can have ripples and implications across multiple pillars. The consequences were immediate: these pillars would immediately lead to asking ourselves the right questions and bring a new dimension of awareness: How do we evaluate Fairness for our product?
How do we make sure all features we create are inclusive?
As a platform that creates new AI based on real ideas, are we accountable for the perpetuation of discrimination?
How are we transparent and accountable when making an arbitrary decision? It's okay not to have all the answers, but we must ask the difficult questions. During our first month, every meeting with our Head of AI Ethics felt like opening Pandora's box in a good way: a million questions arrived around freedom of speech, bias, inclusion, and censorship, and each one felt as legitimate and urgent to answer as the other. “The goal..” Alejandro put it, was to “elevate ourselves to a higher level of confusion”. This mindset that it’s okay to work towards the right answers, as long as we do so in a transparent and inclusive way, would become the foundation of our ethical policy. It became clear this would take time and a lot of consideration. So, we started an internal document to list every question that was brought up during meetings. We needed to keep track of all the ideas. We would write the questions as they came, then spend one minute evaluating if the question was properly asked and what tension it created around which ethical concept, and then where and how that tension would exist in the product or how it's being used. Finally, we would evaluate if this question could be broken down into smaller bits to bring more granularity. For example, the spontaneous question “What is our moderation policy?” needed to be broken down into sub-questions like: “in what cases do we need a moderation policy?”


and “are there laws that want to prevent someone from adding into their own AI's knowledge sensitive or illegal content?”


all the way to: should we filter the input, ie, blocking the owner of an AI from adding illegal content into it? Or should we block the output, ie, the AI from sharing information about illegal content?”


to finally touch the point of tension: “how hard do we need to prevent the perpetuation of immoral content, that is (let's be real) available elsewhere on the internet?” That list of open questions today is significant, but it's also great as it lays our foundations as a company, a moral entity, to give a real direction for the team to build a future we believe in. Be accountable to our users - actions speak louder than words Most companies and startup want users to trust them and will create a nice catch-phrase around “we love privacy”, “we are ethical” and get away with it. After our launch, we saw that our privacy page was the most visited page after our landing page. Users are indeed creating their AI Double, often importing their data from other existing platforms like Instagram, LinkedIn, etc… We knew we had to do more than just a privacy policy, but at the time, we didn't necessarily know how or what to do. Having our Head of AI Ethics in our Team allowed us to act on this and to show our real and tangible work. We created our AI Ethics Hub to show our dedication and our efforts to be transparent and allow users to follow our progress. By creating our AI Ethics Hub we feel we are doing right by our users, especially considering what we're building with Spheria, which is to let people create the AI version of themselves. Our users don't think about all of this when they create their official AI double, but privacy, ethical rules, and transparency are so tightly imbricated into creating your digital second self that, as the makers and founders, it's our job to be transparent, protect privacy and provide ethical rules. We hope this helps in highlighting the difference between startups that are actively engaged in Privacy and Ethics, and those that just put pretty words on their landing page. Setting the foundations for our company's culture Having our Head of AI Ethics join our team so early was the best possible catalyst to help build the right culture at Spheria. De-facto, it put the right values and principles we talked about above at the foundation of our startup, and these foundations will always be present to support the future we're building. “Stay scrappy” is what an investor told me a few months ago. While scrappy does not mean unethical, having a full-time Head of AI Ethics makes us accountable to all our users and accountable to each team member who may speak up against lines being bent. The goal for us is to avoid any future (embarrassing and possibly dishonest) situation similar to when OpenAI's CTO replied, “I don't know” to the journalist asking what data was used to train the new Sora video model. So I'm happy to say that us being on this track definitely helps me sleep at night. It brings me some reassurance and a little boost of confidence to face the thousands of hurdles of growing a startup. It's also a strong signal for users, future hires, and investors to judge us in the light of our actions. Six months ago, we launched our startup Spheria.ai, a platform where people can create and host the AI version of themselves. As founders and consumers, we knew that from day 1, we wanted to build a product that would let people claim back their personal data from the abuse of tech giant , and protect user privacy… create and host the AI version of themselves. claim back their personal data from the abuse of tech giant Today, I'm sharing our experience of having a Head of AI Ethics as the very first employee for your startup and how he turned our naive good intentions into actual science and a foundational framework so we can build a legitimate platform that people trust to create the AI version of themselves. I'm sharing our experience of having a Head of AI Ethics as the very first employee for your startup Realizing how little we knew about AI Ethics Realizing how little we knew about AI Ethics As most founders, we are focused on delivering a great product and growing the user base while trying to stick to our moral compass. At the very first meeting, Alejandro, our new Head of AI Ethics, brought to us a framework that would help organize the big questions around Privacy and Ethics. Instead of just creating an unorganized list of principles, he immediately cross-referenced ethical frameworks that he had seen across his research and deployed them in existing organizations. Our Head of AI Ethics introduced us to a concept called Procedural Fairness, a framework used by the World Bank to make sure Fairness is at the center of its decisions and policies. So the biggest win (right after the second meeting!) was for us to graduate from a chaotic list of good intentions to using Ethics frameworks that are actually used by researchers in international organizations. Right after that second meeting, we defined four pillars for Spheria to use as our foundation for AI Ethics: Transparency, Fairness and Accountability, and Privacy. Using this framework allowed us to visualize the relations between each pillar and see how one idea can have ripples and implications across multiple pillars. Transparency, Fairness and Accountability, and Privacy. The consequences were immediate: these pillars would immediately lead to asking ourselves the right questions and bring a new dimension of awareness: How do we evaluate Fairness for our product? How do we make sure all features we create are inclusive? As a platform that creates new AI based on real ideas, are we accountable for the perpetuation of discrimination? How are we transparent and accountable when making an arbitrary decision? How do we evaluate Fairness for our product? How do we make sure all features we create are inclusive? As a platform that creates new AI based on real ideas, are we accountable for the perpetuation of discrimination? How are we transparent and accountable when making an arbitrary decision? It's okay not to have all the answers, but we must ask the difficult questions. It's okay not to have all the answers, but we must ask the difficult questions. During our first month, every meeting with our Head of AI Ethics felt like opening Pandora's box in a good way: a million questions arrived around freedom of speech, bias, inclusion, and censorship, and each one felt as legitimate and urgent to answer as the other. “The goal..” Alejandro put it, was to “elevate ourselves to a higher level of confusion”. This mindset that it’s okay to work towards the right answers, as long as we do so in a transparent and inclusive way, would become the foundation of our ethical policy. It became clear this would take time and a lot of consideration. So, we started an internal document to list every question that was brought up during meetings. We needed to keep track of all the ideas. We would write the questions as they came, then spend one minute evaluating if the question was properly asked and what tension it created around which ethical concept, and then where and how that tension would exist in the product or how it's being used. Finally, we would evaluate if this question could be broken down into smaller bits to bring more granularity. For example, the spontaneous question “What is our moderation policy?” needed to be broken down into sub-questions like: “in what cases do we need a moderation policy?” and “are there laws that want to prevent someone from adding into their own AI's knowledge sensitive or illegal content?” all the way to: should we filter the input, ie, blocking the owner of an AI from adding illegal content into it? Or should we block the output, ie, the AI from sharing information about illegal content?” to finally touch the point of tension: “how hard do we need to prevent the perpetuation of immoral content, that is (let's be real) available elsewhere on the internet?” “in what cases do we need a moderation policy?” “in what cases do we need a moderation policy?” and “are there laws that want to prevent someone from adding into their own AI's knowledge sensitive or illegal content?” and “are there laws that want to prevent someone from adding into their own AI's knowledge sensitive or illegal content?” all the way to: should we filter the input, ie, blocking the owner of an AI from adding illegal content into it? Or should we block the output, ie, the AI from sharing information about illegal content?” all the way to: should we filter the input, ie, blocking the owner of an AI from adding illegal content into it? Or should we block the output, ie, the AI from sharing information about illegal content?” to finally touch the point of tension: “how hard do we need to prevent the perpetuation of immoral content, that is (let's be real) available elsewhere on the internet?” to finally touch the point of tension: “how hard do we need to prevent the perpetuation of immoral content, that is (let's be real) available elsewhere on the internet?” That list of open questions today is significant, but it's also great as it lays our foundations as a company, a moral entity, to give a real direction for the team to build a future we believe in. Be accountable to our users - actions speak louder than words Most companies and startup want users to trust them and will create a nice catch-phrase around “we love privacy”, “we are ethical” and get away with it. After our launch, we saw that our privacy page was the most visited page after our landing page. Users are indeed creating their AI Double, often importing their data from other existing platform s like Instagram, LinkedIn, etc… creating their AI Double, often importing their data from other existing platform We knew we had to do more than just a privacy policy, but at the time, we didn't necessarily know how or what to do. Having our Head of AI Ethics in our Team allowed us to act on this and to show our real and tangible work. We created our AI Ethics Hub to show our dedication and our efforts to be transparent and allow users to follow our progress. AI Ethics Hub AI Ethics Hub By creating our AI Ethics Hub we feel we are doing right by our users, especially considering what we're building with Spheria, which is to let people create the AI version of themselves. Our users don't think about all of this when they create their official AI double, but privacy, ethical rules, and transparency are so tightly imbricated into creating your digital second self that, as the makers and founders, it's our job to be transparent, protect privacy and provide ethical rules. We hope this helps in highlighting the difference between startups that are actively engaged in Privacy and Ethics, and those that just put pretty words on their landing page. Setting the foundations for our company's culture Having our Head of AI Ethics join our team so early was the best possible catalyst to help build the right culture at Spheria. De-facto, it put the right values and principles we talked about above at the foundation of our startup, and these foundations will always be present to support the future we're building. “Stay scrappy” is what an investor told me a few months ago. While scrappy does not mean unethical, having a full-time Head of AI Ethics makes us accountable to all our users and accountable to each team member who may speak up against lines being bent. The goal for us is to avoid any future (embarrassing and possibly dishonest) situation similar to when OpenAI's CTO replied, “I don't know” to the journalist asking what data was used to train the new Sora video model. OpenAI's CTO replied, “I don't know” So I'm happy to say that us being on this track definitely helps me sleep at night. It brings me some reassurance and a little boost of confidence to face the thousands of hurdles of growing a startup. It's also a strong signal for users, future hires, and investors to judge us in the light of our actions.

Why Your AI Startup Should Hire a Head of AI Ethics on Day 1

) continues to develop as a transformative force across many spheres of life, already beginning to revolutionize industries and reshape the way we live and work. The topic of AI in warfare will require increasing attention from governments, policymakers, and international organizations. A large part of this is due to significant advancements in the development of autonomous weapons systems (AWS), which use algorithms to operate independently and without human supervision on the battlefield. More broadly, AI in its many forms has the potential to enhance a range of military activities, from the likes of robotics and weaponry to intelligence gathering and decision-making. Artificial Intelligence (AI With such a diversity of potential applications comes a unique set of ethical dilemmas. The benefits of AI in warfare are increased precision, reduced human casualties, and even deterrence against entering into armed conflict in the first place akin to the threat of nuclear war. However, this would mean giving machines the ability to make deliberate life-and-death decisions, blurring the lines of accountability, and possibly going against the fundamental principles of morality in warfare. A Brief Overview of AI in Warfare As the Stockholm International Peace Research Institute outlines, AI has become a crucial part of military strategies and budgets, contributing to the wider ‘arms race’[1]. Combined with the likes of nuclear and atomic threats, geopolitics must therefore question the ethics of the continued weaponization of technology. Some believe that these advancements will ultimately lead to zero-sum thinking dominating world politics. This logic is not new; Alfred Nobel hoped the destructive power of dynamite would put an end to all wars[2]. AI has already started to be incorporated into warfare technology such as in drone swarms, guided missiles, and logistical analysis. Autonomous systems have been incorporated into defensive weaponry for even longer, e.g. antivehicle and antipersonnel mines. Future developments will continue to aspire to increasing levels of autonomy. The US is testing AI bots that can self-fly a modified version of the F-16 fighter jet; Russia is testing autonomous tanks; and China too is developing its own AI-powered weapons[3]. The goal is to protect human life by continuing to mechanize and automate battlefields. “I can easily imagine a future in which drones outnumber people in the armed forces pretty considerably[3]” said Douglas Shaw, senior advisor at the Nuclear Threat Initiative. So, instead of soldiers being deployed on the ground we saved lives by putting them in planes and arming them with missiles. Now with AI, militaries hope to spare even more human life from its forces. Moral Implications of AI in Warfare This sounds great so far. Save lives by using AI to direct drones. Save lives by using AI to launch missiles. The difference between this technological jump in warfare and past innovations is the lack of human input in decision-making. With AWS and lethal autonomous weapons systems (LAWS), we are handing the power to kill a human being over to an algorithm that has no intuitive humanity. Several ethical, moral, and legal issues arise here. Is it fair that human life should be taken in war without another human being on the other side of that action? Does the programmer of an algorithm in a LAWS have the same responsibility in representing their country as a fighter pilot, and/or the same right to contribute to taking enemy life? Like with the ethical dilemmas surrounding autonomous vehicles[4], is it morally justifiable to delegate life-and-death decisions to AI-powered algorithms? From a technological point of view, this will come to depend in part on the transparency of the programming of AWS: training, datasets used, coded preferences, and errors like bias, in these models. Even if we reach an adequate level of accuracy and transparency, should AWS and LAWS be considered moral in warfare? Moral Implications of Just War Theory Just War Theory, credited to St Augustine and Thomas Aquinas in the 13th century[5], evaluates the morality of warfare and ethical decision-making in armed conflict. Across guidelines for (justice of war) and (justice in war), the most notable considerations are: jus ad bellum jus in bello Proportionality: The use of force must be proportional to the objective being pursued and must not cause excessive harm or suffering relative to the anticipated benefits. Discrimination: Also known as non-combatant immunity, this principle requires that combatants distinguish between combatants and non-combatants, and only target the former while minimizing harm to the latter. It could be argued that the use of AI-powered weapons and LAWS do not guarantee adherence to these conventions. On proportionality, AI-backed weaponry would possess the ability to deliver force with greater speed, power, and precision than ever before. Would this level of force necessarily match the threat posed/military objective, especially if used against a country with less technologically advanced weaponry? Similarly, what if a LAWS is fed erroneous intel, or hallucinates and creates an inaccurate prediction? This could lead to the formation and execution of unnecessary military force and disproportionate actions. On the point of discrimination, these technologies are not 100% accurate. When firing a missile at an enemy force, what happens if facial recognition[6] technologies cannot distinguish civilians from combatants? This would undermine the moral distinction between legitimate military targets and innocent bystanders. Case Study A Panel of UN Experts reported the possible use of a LAWS - STM Kargu-2 - in Libya in 2020, deployed by the Turkish military against the Haftar Affiliated Forces (HAF)[7]. Described as being “programmed to attack targets without requiring data connectivity between the operator and the munition” [8], the drone units were eventually neutralized by electronic jamming. The involvement of this remote air technology though changed the tide for what had previously been “a low-intensity, low-technology conflict in which casualty avoidance and force protection were a priority for both parties”[7]. While causing significant casualties, it is not clear whether the unmanned attack drones caused any fatalities[8]. Still, it highlights issues with unregulated, unmanned use of combat aerial vehicles and drones. HAF units were not trained to defend against this form of attack, had no protection from the aerial attacks (which occurred despite the drones being offline), and even in retreat continued to be harassed by the LAWS. This alone begins to breach the principle of proportionality, and even more so when considering that the STM Kargu-2s changed the dynamic of the conflict. Reports go so far as to suggest that “the introduction by Turkey of advanced military technology into the conflict was a decisive element in the… uneven war of attrition that resulted in the defeat of HAF in western Libya during 2020”[7]. International Cooperation and Regulation of AI in Military Applications Since 2018, the UN Secretary-General António Guterres has maintained that LAWS are both politically and morally unacceptable[9]. In his 2023 New Agenda for Peace, Guterres has called for this to be formalized and actioned by 2026. Under this, he suggests a complete ban on the use of AWS which functions without human oversight and does not comply with international law, and regulation of all other AWS. This type of international cooperation and regulation will be necessary to help overcome the ethical concerns we have discussed. For now, the use of AWS without human oversight will cause the most immediate issues. The lack of a human decision-maker creates issues of responsibility. Without a chain of command who takes responsibility for the malfunctioning or general fallibility of an AI-powered system? Moreover, there would be an ensuing lack of accountability. Especially in traditional warfare where there are defined moral principles like the Just War Theory, here there would be no culpable agent for actions taken by autonomous systems. Finally, while there are benefits to increasingly adopting AI in military applications, how these technologies end up being used will define whether it becomes a utopic solution or a proliferation of the already politically destabilizing arms race. Therefore, continued discussion around international, legally binding frameworks for ensuring accountability in AI warfare will arguably be one of the most crucial areas of AI regulation in the near future. References Weaponizing Innovation: Mapping Artificial Intelligence-enabled Security and Defence in the EU War and Technology - Foreign Policy Research Institute How AI Will Revolutionize Warfare Ethical AI and Autonomous Vehicles: Championing Moral Principles in the Era of Self-Driving Cars | HackerNoon Just War Theory | Internet Encyclopedia of Philosophy Bias in Facial Recognition Tech: Explore How Facial Recognition Systems Can Perpetuate Biases | HackerNoon Libya, The Use of Lethal Autonomous Weapon Systems | How does law protect in war? - Online casebook The Kargu-2 Autonomous Attack Drone: Legal & Ethical Dimensions - Lieber Institute West Point Lethal Autonomous Weapon Systems (LAWS) – UNODA

The Ethics of AI in Modern Warfare: Balancing Innovation with Moral Accountability

আধুনিক যুদ্ধে এআই-এর নীতিশাস্ত্র: নৈতিক জবাবদিহিতার সাথে উদ্ভাবনের ভারসাম্য

Die Ethik der KI in der modernen Kriegsführung: Innovation und moralische Verantwortung in Einklang bringen

La ética de la IA en la guerra moderna: equilibrar la innovación con la responsabilidad moral

L'éthique de l'IA dans la guerre moderne : équilibrer l'innovation et la responsabilité morale

आधुनिक युद्ध में एआई की नैतिकता: नैतिक जवाबदेही के साथ नवाचार का संतुलन

現代の戦争における AI の倫理: イノベーションと道徳的責任のバランス

현대전에서의 AI 윤리: 혁신과 도덕적 책임의 균형

A Ética da IA na Guerra Moderna: Equilibrando Inovação com Responsabilidade Moral

Этика ИИ в современной войне: баланс между инновациями и моральной ответственностью

Modern Savaşta Yapay Zeka Etiği: İnovasyonu Ahlaki Sorumlulukla Dengelemek

Đạo đức của AI trong chiến tranh hiện đại: Cân bằng giữa đổi mới với trách nhiệm đạo đức

现代战争中的人工智能伦理：平衡创新与道德责任

The concept of (AVs) has long sparked debate as to the ethics of transferred decision-making autonomy on roads. The Society of Automotive Engineers sets out 6 Levels of autonomy used officially across the industry to differentiate AV capability[1]. autonomous vehicles Levels 0-2 AV already exist in commercial markets. Level 3 is the first significant jump in capability. It describes vehicles that can self-drive for short periods, but require a human driver to be ready to intervene if the system requests it. Levels 4-5 then go beyond environmental detection. These encompass cutting-edge technologies which obviate human override altogether[2]. Level 4 AVs can complete an entire journey without human intervention under specific conditions. Level 5 can complete entire journeys under any circumstances. Level 5 would be associated with vehicles that don’t even need steering wheels or pedals, for example. The moral and ethical dilemmas that emerge surrounding these two higher levels of autonomy arise from the loss of almost all direct decision-making power. Correct functioning of core technologies, ability to value human life and principles, trade-offs, and accountability then all become issues under both ethical and legal frameworks. We will explore these, starting with the infamous Trolley Problem. The Trolley Problem The Trolley Problem is a thought experiment created within the branch of philosophy called virtue ethics and discusses how foreseeable consequences compare to intended consequences on a moral level. The main variation, devised by British philosopher Philippa Foot (1967)[3], is as follows: A trolley is running along a set of tracks, out of control and unable to break. 5 people are tied onto these tracks though, and the trolley is fast approaching them. You are stood off the tracks next to a lever, which if pulled would divert the trajectory of the trolley to a different set of tracks. This alternative track has only one person tied to it, so the trolley will currently kill 5 people, but this could be reduced to just one if you act. Do you pull the lever? Ethical Frameworks in Decision-Making The Trolley Problem can be viewed under many ethical frameworks. Consequentialists would argue it’s better to reduce overall harm in the outcome by any means necessary. Deontologists would argue that the act of pulling the lever and actively killing one person is more morally wrong than letting the trolley continue its due course. Utilitarians would argue that the most ethical choice creates the greatest amount of good for the greatest number of people. Rawlsians would argue that all lives are equal, and to achieve justice and act most fairly one must prevent the greater harm. Rights-based ethics would argue that the right to life is absolute and should not be violated or sacrificed for any trade-off. Whichever ideology, our duty to minimize harm to others directly conflicts with our duty to choose the morally correct action. It’s the ability to value decisions and trade-offs like these that many question in autonomous vehicles[4]. For example, if an AV was about to crash, should passengers of the vehicle be prioritized over pedestrians/other vehicles? It isn’t just the ability to make tough decisions that must be considered in the ethics of autonomous vehicles though. When humans ourselves cannot agree on which ethical framework would best answer the Trolley Problem, how are we meant to program self-driving cars to weigh up trade-offs like these under one ideology? What basic values and principles should we be programming into AI? Should we want it to prioritise positive duties: the number of lives saved, or negative duties: minimising active harm done? Case Study In 2018, Uber tested Level 3 AV in Arizona, resulting in a tragic pedestrian fatality – the first caused by AV ever[5]. Being Level 3, there was a backup driver present in the vehicle, but it wasn’t enough. With the environmental detection system struggling to correctly identify the obstacle – here a pedestrian with a bike, the possibility of harm was not recognized by the car’s alert systems fast enough. By the time the backup driver was finally alerted to take control, the vehicle was already 0.2 seconds to impact, and traveling at 39mph[6]. This example does not necessarily discuss the trade-off of direct harm to AV passengers versus pedestrians external to the vehicle, as the backup driver was never at risk of harm herself. However, it does bring to light whether we can and should be relying on AI sensory detection over our own and whether manual override is a feasible backup in such high-pressure, pressed-for-time scenarios. It also highlights the issue of transferring autonomy even temporarily to an AV, via the lack of a moral agent culpable for the killing. In this case, Uber retracted more than 90 other Level 3 AVs it had been testing in Arizona and settled with the victim’s family. The backup driver on the other hand was charged with negligent homicide[7]. Was blame correctly placed on her or should it have been the vehicle – is the latter even possible? Ethical AI UNESCO outlines that AI ethical frameworks should prioritise avoiding harm and respecting human rights[8]. Safety and non-discrimination should underpin machine learning principles. Human oversight, control, and accountability should also be considered essential alongside responsible AI. Additional concepts of fairness, and ‘for the greater good’, suggest that we want AI to use a utilitarian ideology for decision-making. On the other hand, ‘respecting human rights’ plays into the moral rightness of actions themselves, i.e. deontology. Transparency will of course also be paramount in understanding how decisions end up being calculated by AVs. For evaluating harm caused or prevented in the case of an AV accident, we will need to understand how and why the underlying AI technology reaches a certain conclusion. Public trust in AVs will require understanding accountability, and making sure the right frameworks are being adhered to. Ethical Automated Decision-Making The European Parliamentary Research Service recognizes the ethical, legal, and economic concerns which must be addressed in developing and deploying automated decision-making AI[9]. This includes research into how to develop ethical principles in the underlying algorithms, and how to bring global policy and regulations up to speed with the exponential rate of AI innovation. In terms of human rights, human agency is also being prioritized, with research bodies wanting to protect the ‘right of end users not to be subject to a decision based solely on automated processing’[9]. On the technology side, cybersecurity standards will become more important to ensure secure and reliable systems. Ethical AI requires trustworthy software. Conclusion While we do not currently have the general public using Level 3+ AVs on roads in the UK, or any such vehicles available in domestic markets yet[10], major players in the industry like BMW, Tesla, and Mercedes aim to launch these by 2025 using technologies like Traffic Jam Pilot to do so[11]. If AVs get the ethics of decision-making right, there are great benefits to be seen. Some estimates predict a 90% reduction in traffic-related accidents with them on the roads[5]. Still, it is clear that we do not yet have quantifiable ethical and legal frameworks outlining how decisions should be made and trade-offs prioritised when it comes to the technologies that underpin AVs. AV players will therefore need to further outline what ‘minimise harm’ means, and which ethical ideology should dictate decision-making. As we saw with Uber’s accident in 2018[7], accountability and agency will also have to be clarified. All of these, how they are handled, and which direction we progress in, will have long-term ethical implications for society. References Navigating Ethical Dilemmas in Autonomous Vehicles: A Case Study | by Helenjoy | Medium The Current State Of Play In Autonomous Cars | Hackaday The Trolley Problem — Origins. The Trolley Problem is a thought… | by Sara Bizarro | Medium Uber AV road fatality poses questions | News | Insurance Times Ethical Decision Making in Autonomous Vehicles: The AV Ethics Project | Science and Engineering Ethics (springer.com) ‘I’m the Operator’: The Aftermath of a Self-Driving Tragedy | WIRED Navigating Autonomous Vehicles Levels: The Vasquez Case and the Debate Between Level 3 and Level 4 Autonomy – Arizona State Law Journal AI Ethical Framework (rootstrap.com) EU guidelines on ethics in artificial intelligence: Context and implementation (europa.eu) Electric and Autonomous Vehicles: The Future Is Now | Bain & Company The future of autonomous vehicles (AV) | McKinsey

Ethical AI and Autonomous Vehicles: Championing Moral Principles in the Era of Self-Driving Cars

নৈতিক এআই এবং স্বায়ত্তশাসিত যানবাহন: স্ব-ড্রাইভিং গাড়ির যুগে নৈতিক নীতিগুলিকে চ্যাম্পিয়ন করা

Ethische KI und autonome Fahrzeuge: Verfechtung moralischer Prinzipien im Zeitalter selbstfahrender Autos

IA ética y vehículos autónomos: defender los principios morales en la era de los vehículos autónomos

IA éthique et véhicules autonomes : défendre les principes moraux à l’ère des voitures autonomes

नैतिक एआई और स्वायत्त वाहन: सेल्फ-ड्राइविंग कारों के युग में नैतिक सिद्धांतों का समर्थन

倫理的な AI と自動運転車: 自動運転車の時代における道徳原則の擁護

윤리적인 AI와 자율주행차: 자율주행차 시대의 도덕적 원칙 옹호

IA ética e veículos autônomos: defendendo princípios morais na era dos carros autônomos

Этический искусственный интеллект и автономные транспортные средства: отстаивание моральных принципов в эпоху беспилотных автомобилей

Etik Yapay Zeka ve Otonom Araçlar: Sürücüsüz Araçlar Çağında Ahlaki İlkelerin Savunulması

Trí tuệ nhân tạo có đạo đức và phương tiện tự lái: Ủng hộ các nguyên tắc đạo đức trong kỷ nguyên xe tự lái

道德人工智能和自动驾驶汽车：在自动驾驶汽车时代倡导道德原则

This ethical model checklist was developed by Amy Shah, MD, MBA, MS to help individuals and small groups determine if AI used for their project or work is beneficial and uses ethical principles. The first item in the checklist is to determine the goal of the work/project and if AI is suitable for that goal. Goal: What is the goal that I will be using AI for in my project/work? To the best of my ability, can I determine if the AI was developed with ethical principles in mind? Is the AI transparent and clear about what inputs and decision making that Explainability - went into its recommendations? Does this AI appear to treat individuals and/or groups of individuals equitably? Fairness - Does this AI minimize security risks and prevent security attacks that might Robustness - cause private information to be leaked? Can individuals and groups see how the AI works and evaluate its function? Transparency - Can this AI be trusted or be trustworthy? Does this AI prioritize and safeguard consumers’ privacy and data rights and provide Privacy- explicit assurances to users about how their personal data will be used and protected? To the best of my ability, can I determine if the AI treats individuals and groups in an ethical manner? Does the AI have consistency of values and actions so that what is advised and what
is done are in harmony? Integrity - Does the AI acknowledge the inherent value of all individuals and treat all with
dignity? Respect - Can the use of this AI be considered responsible? Are we able to have
awareness that our actions have consequences that can be positive or negative? Is there a way
we could be held accountable for the actions of this AI? Responsibility - Are the ethical decisions of the AI unbiased? Do the actions of the AI treat everyone Fairness - equally regardless of status, position, or personal attributes? Does the use of the AI account for empathy and understanding towards others?
Do all stakeholders have equal say? Are we keeping in mind that there is a human element in
every decision we make? Compassion - Does use of AI allow for respectful disagreement? Courage means doing what is
right despite the idea being risky or unpopular. It means standing up for what you believe in. Is
there room for dissenting thought in your project or goal? Courage - Have you made AI related decisions based on being informed on the issues and
facts while also taking into account the stakeholders? Wisdom - To the best of my ability, have I determined if AI involvement will
affect human medical decision making? If so, have I considered the
following principles? Does the AI cause any harm to individuals or groups? Non- Non-maleficence - maleficence means “do no harm.” Does the AI actively help others welfare and health outcomes? Does the AI allow an environment that maximizes health for the Beneficence - Health maximization - individual and opportunities for health of the public? Does the AI use scarce medical resources efficiently? Efficiency - Does the AI allow for the patient’s right to determine what will happen
to the and encourage their ability to make their own medical choices. Respect for autonomy - Does the AI allow for equity for all people so that all people can have health care at
the same quality? Justice - Does the use of AI weigh the individual needs versus the needs of the greater
good in a beneficial and equitable way? Proportionality - This checklist for individuals and groups is meant to help with taking careful consideration about the safety of AI in one’s work project or goal. This was developed based on the following reference materials.  For more information, please see this article: https://hackernoon.com/human-mental-health-and-artificial-intelligence References: Peter Schröder-Bäck, et al. Teaching seven principles for public health ethics: towards a curriculum for a short course on ethics in public health programmes. Schröder-Bäck et al. BMC Medical Ethics 2014, 15:73 http://www.biomedcentral.com/1472-6939/15/73 https://axies.digital/ethical-decision-making/ https://www.ibm.com/policy/ibm-artificial-intelligence-pillars/ Images created by DALL-E/ChatGPT

AI Ethical Checklist for Small Group and Individual Use Advisement
Developed March 21, 2024

As a CTO, I've spent decades navigating the ever-evolving landscape of technology. The pace of change has become dizzying, pushing the boundaries of what's possible seemingly every day. But with this incredible progress comes a growing responsibility – the responsibility to ensure that technological advancements serve humanity, not the other way around. This is where the concept of ethical tech leadership takes center stage. Evolution of Ethical Tech Leadership Ethical tech leadership isn't just about ticking compliance boxes. It's about striking a delicate balance between pushing the boundaries of innovation and ensuring ethical considerations remain at the forefront of decision-making. Data from indicates a clear correlation between businesses with higher levels of technological maturity and those that prioritize ethical considerations in their approach to technology. Harvard Business Review It's about more than just compliance; it's about cultivating a moral compass within the organizational culture. Leaders are tasked with fostering a commitment to responsibility and accountability, setting the stage for a tech landscape driven by ethical principles. The landscape of ethical concerns in technology is constantly shifting. Just a few years ago, the biggest debate might have been around "screen time" for children. Today, we grapple with far more complex issues like algorithmic bias in AI, the erosion of user privacy through data collection practices, and the potential for weaponization of emerging technologies like facial recognition. Let’s look at everything that falls into the realm of ethical tech leadership and how tech leaders are navigating evolution. Responsible AI: Navigating the Ethical Challenges The ascent of artificial intelligence (AI) has brought forth unprecedented challenges, especially concerning bias and transparency. Recently, Elon Musk’s decision to was met with severe backlash, predicting the lack of regulation the platform will be subjected to in the near future. fire Twitter’s ethical AI team There is a lot of concern around how datasets used to train AI algorithms can inadvertently reflect societal biases, leading to discriminatory outcomes. According to , AI datasets often contain a gender and racial bias which can lead to prejudiced results. For example, an AI used in the criminal justice system might be more likely to recommend harsher sentences for people of color if the training data contained such a bias. TIME magazine A company demonstrating strong leadership in addressing AI bias is IBM. They have outlined that guide the development and deployment of their AI solutions. These principles focus on transparency, accountability, fairness, and explainability IBM Artificial Intelligence Pillars Likewise, tech leaders must lead the charge in implementing ethical AI frameworks, ensuring algorithms are fair and decision-making processes are transparent. By continuously monitoring and evaluating AI systems, they can pave the way for a more responsible and accountable tech future. Data Privacy: Safeguarding User Trust in the Digital Era Data has become the lifeblood of the digital age, fueling innovation and personalization across countless industries. However, with this immense power comes a profound responsibility – the responsibility to safeguard user privacy. Tech leaders find themselves as custodians of data ethics, establishing robust protection policies and transparent data handling practices. The stakes for protecting user privacy have never been higher. Consumers are increasingly worried about how their data is collected, used, and potentially shared. According to a , Americans cite their personal data being collected as stressful, with 79% of participants being concerned about companies, and 64% about how the government uses their personal data. Pew Research survey This erosion of trust can have a significant impact on a company's bottom line.  Data breaches can lead to hefty fines, reputational damage, and a decline in customer loyalty. The infamous of 2013 caused hackers to steal data associated with 3 billion accounts. It almost derailed the platform’s acquisition by Verizon and led to a slew of lawsuits leading to the company having to pay to its users. Yahoo data breach $117.5 million in settlements To build trust, ethical tech leaders must establish robust data protection policies. These policies aim to clearly outline data collection, usage, and sharing practices, while also detailing user rights aligned with regulations like GDPR. Transparency is key, with user-friendly privacy policies explaining data practices and offering clear opt-out options for third-party sharing. Finally, ethical leaders must  navigate the evolving regulatory landscape to ensure compliance with local and international data privacy laws. This comprehensive approach fosters trust and a responsible tech ecosystem that respects user privacy. Sustainable Technology Practices: Nurturing a Greener Future While technology has propelled progress, it has also left an environmental footprint. A 2019 by the Shift Project showed that the world’s collective digital carbon footprint accounted for nearly 3.7 percent of all greenhouse emissions. This is comparable to aviation industry emission levels. study From the vast energy consumption of data centers to the e-waste generated by constantly evolving devices, the tech industry has a significant impact on the planet. This is where the role of ethical tech leaders becomes crucial. They are called upon to spearhead the adoption of sustainable technology practices, ensuring that technological advancements don't come at the expense of our environment. Ethical tech leaders must champion eco-friendly solutions across their organizations. This includes implementing measures to reduce energy consumption in data centers, such as utilizing renewable energy sources or employing energy-efficient cooling systems.  They should also encourage the development of hardware and software designed for lower power consumption. By prioritizing sustainability, ethical tech leaders not only contribute to the fight against climate change but also position their organizations as pioneers in responsible tech development.  This commitment to a greener future attracts environmentally conscious consumers and investors, fostering a competitive advantage in the long run. The Future of Ethical Tech Leadership As we stand at the crossroads of technological evolution, the role of tech leaders and CTOs as ethical leaders has never been more crucial. As technologies like augmented reality and virtual reality become more prominent, new questions around privacy and user experience will need to be addressed. Ethical tech leaders will be instrumental in navigating this uncharted territory. They will need to be adaptable, forward-thinking, and committed to fostering a dialogue about the ethical implications of emerging technologies. By proactively addressing these challenges, ethical tech leaders can help ensure that technology remains a force for good in the world.

The Rise of Ethical Tech Leadership

“ ” Nephrologist: Hello. We've got a set of failing kidneys. Please, how soon can we get a new set created and ready to implant? “ . Biotechician: We’ll come for the patient's biomaterials and have them ready in the next two months” That could have saved the life of a family friend after her kidneys failed 5 years ago—worst, she had a rare blood type. After being on the waiting list for 3 years undergoing excruciatingly painful dialysis, she caved into the verdict she dreaded the most. However, this article is not just a sob story of a friend. This is a revelation of how regenerative medicine, which you might have thought belonged to the fictitious world of Sci-fi, could be the savior of many lives like hers now and in the future. I’m talking about “3D Bioprinting”. The Back Story The number of humans with failing organs on the waiting list for organ transplants is overwhelming for the medical field. Organdonor.gov states that in the U.S. alone, over 103 thousand men, women, and children are on the national transplant waiting list. Every 10 minutes, one person is added to the list and 17 people die each day waiting for an organ transplant. In 2023, out of 88,551 awaiting kidney donors, only about 24% received transplants. That is in the U.S. alone, we can only try to imagine what the numbers could be in other parts of the world.. Or can we even imagine the reality in third-world countries with no structured organ donation system? Dr. Jennifer Lewis, a professor at Harvard University's Wyss Institute for Biologically Inspired Engineering, in an interview with CNN, has an insight into the problem. In her statement, she mentioned that on a yearly average, there are only around 6,000 living donors open to offering their organs and about 8,000 deceased donors who could each provide approximately 3.5 organs. Dead donors are people who die from catastrophic health events or accidents, making some of their organs salvageable before they completely stop living. However, because of such events, their organs may not be of high quality for a donation or they may not even be on the organ donor list or it may be hard to find a good match. For living donors, there is a scare as to what their life is worth, if it is the right choice to make if they are willing to let go and embrace the possible complications that may arise from donating their organs and the surgery involved - especially if the patient is not related to them. There is a two-step delirium that raises the most fear for a patient clinging onto the last thread of hope. First, finding a donor match, then hoping the patient's body doesn't reject the donated organ. And from all the premises stated above, this is a tough call. A wait list could just as well be a death row. What if it could be different? What if organs could be printed from biodata, materials, and cells of a patient instead of waiting for months to years to find a donor that would match? What if regenerative medicine is the solution staring into our eyes? A handful of medical conditions would meet their end if this could happen. Ordering a newly generated body part from the cells of the patient to replace the old one could easily be more effective and faster than organ donations. This brings us to the 3D organ bioprinting. What is 3D Bioprinting? The idea of 3D bioprinting is like that of conventional 3D printing. However, it is premised on the use of organ regenerative actions which involve the manufacturing and development of organs, tissues, and body parts such as the liver, skin, heart, eyes, bones, etc. While conventional 3D printers use computer files to extrude and create objects with complex structures, 3D bioprinters make use of bioinks containing living viable cells, biomaterials, and biomolecules to create bioartificial organs of a human. This works by assembling layer-by-layer multiple cell types, biomaterials and molecules, and other growth factors that would be a replica of the natural prototypes. In this case, it is beyond just mere perception that creating a new component for a body by using its cells is much more reliable than taking a component with a completely different cell structure from another body to replace a component in a body. This way, a patient, on-demand, can have personalized replacement body parts with a very low possibility of their body rejecting the new parts. This technology is driven by the need to find the accurate organ and tissue types that would be a match for patients who need organ replacement and is opening new avenues for future organ transplantation programs. How Does 3D Bioprinting Work? For bioprinting, every procedure starts with the biological cells of the person involved and applies the concepts of biomimetics and biomimicry. This simply means using a patient's cells to grow the needed organ. Biotechicians choose cells based on their physiology and functions. They consider the cells’ differentiation and proliferation by paying attention to features such as the cell type, size, and shape These different types of cells are rendered into a printable bioink, which involves mixing them up in materials like alginate or gelatin; before they are loaded into a nozzle. To continue, each cell is placed, layer by layer, in precise locations to form a cellular attachment and build a complex tissue that would develop the organ. For the cell's culture to grow, it is transferred to a scaffold where blood vessels are integrated into the tissue and the setup is connected to a pump that would deliver the right amount of oxygen and nutrients to keep it alive. With time, the tissue will begin to develop on its own, forming an organ that will increase both in maturity and function. This is a phase of 3D tissue engineering and although this is dramatically simplified, the process requires more technicalities only applicable to experts in the biotech field and could evolve with time. From the looks of things, 3D bioprinting is already here for good. Cases of 3D Bioprinting It is no longer a myth or a fictional idea; this regenerative medicine technology has become a reality. In this decade, research institutes, researchers, and doctors have dedicated time and resources, working actively on experimenting with organ bioprinting—some are already recording success. The First Ear Bioprint In 2022, the U.S. witnessed the implantation of a new ear on a 20-year-old lady born without her right ear. Of course, a new ear could have been artificially constructed and placed, but this time, Dr. Arturo Bonilla, a pediatric microtia surgeon, changed the rule of the game by developed out of the cartilage cells of the lady's left ear. implanting a 3D bioprinted ear Imagine multi-layered skin, cartilage, and muscle structures born out of laboratory-grown cells to form an ear. Amazing! This was a worthwhile revelation that bioprinting could change the way new body parts and organs were sourced. Ongoing Research on Bioprinting The race to find more potentially relieving 3D bioprinted tissues and organs surged even more. Still in 2022, and Dr. Michal Wszola, creator of Bionic Pancreas, revealed that some researchers in Poland were able to achieve the within a two-week window period, enabling stable blood flow in these pigs. this abstract 3D bioprint of pancreas in pigs Hope is alive as the National Institute of Health’s Advanced Research Projects Agency for Health (ARPA-H), in September 2023, awarded some Stanford researchers about to bioprint a human heart and transplant it into a pig in the next five years. $26 million in contract However, the with 4,000 kilometers of capillaries and 200 million alveoli, capable of carrying out oxygen exchange in animal models, which is a critical step towards the development of tolerable, transplantable lungs has been achieved by United Therapeutics Corporation. The goal is to have the lungs cleared for human trials within five years. 3D bioprinting of human lungs Another research institute with impressive strides is the Wake Forest Institute for Regenerative Medicine. Researchers in this institute have been able to develop the following: A skin bioprinting system for patients suffering from skin cancers, burns, and other non-healing or deprecated kin conditions. A 3D bioprinting skeletal muscle system was shown to effectively contract in rodents and enable an 80% regain of lost muscular function in an anterior leg muscle within a space of 8 weeks. Printing of tubular structures like blood vessels or hollow, non-tubular organs like a bladder. 3D biocreation of a mini-kidney and a mini-liver. The challenge for Wake Forest scientists is the creation of larger and more solid organs that can fully mimic the functions of their prototypes. This is because these large organs, like the brain and heart, have so many cells per centimeter, and there is the challenge of vascularity and nutrition. The brain is a very complex tissue that is difficult to repair or replace when damaged. However, some Oxford researchers believe that the bioprinting of stem cells can reverse brain damage and injuries. These researchers have gone on to prove this verdict by creating 3D bioprinted stem cells which developed a biomimicry of the complex structure of the human cerebral cortex, having six layers (two “deep” layers and four “upper” layers), each with its type of neuron. When the tissues were implanted into slices of mouse brains, they successfully integrated into the mouse tissue, structurally and functionally. The Problems and Ethical Considerations Surrounding the Creation of Bioprinted Body Parts and Organs As much as this concept looks like a lifesaver for many hopeless medical cases in the world today, there are still some problems this could pose. The bioprinting of organs is based on individual medical care rather than a basic universal treatment plan like that of organ donation. There is an assumption that due to its personalized medicine status, the cost of this service may just be more expensive than a public treatment plan would be, and this could prevent financially unstable people from getting access to this service. Is it safe? We can't say for sure. The 3D printing of organs and parts of the body using bioink is not considered neutrally ethical and even if it is generated from the patient's cell, there are still problematic aspects in terms of cell reprogramming, differentiation, proliferation, and of course mutation which may usher in the much-dreaded apocalypse. There is the risk of tumorigenicity, which is the property of a cell to form tumors when inoculated. While bioprinting is considered an incoming savior of the sick and deprived, this technology cannot be rushed. The efficacy of its procedures has to be tried and tested severally before it can be rolled out as clinically applicable. If this is not done, it can lead to several deaths that may be beyond control. There are also additional ethical and legal concerns related to patients' genetic information collection, which also include the storage and use of these cells. Advantages of Bioprinting and Designer Bodies Every technology has a downside, but it so happens that compared to the downsides of this technology, if it is put through all processes of long years of research and experiment with a high success rate, 3D organ bioprinting could nearly nullify the cause of concern. There are issues the bioprinting of organs and body parts would solve: The endless waiting list that could spell ‘doomsday is awaiting’. The torture that comes with getting a transplant; only for the patient's antigens to reject the organ. The need for immunosuppression to prevent organ rejection, which leaves patients at higher risk of infection. The risk of a living donor (even though willing) experiencing some possible complications that could arise during surgery. Body parts manufactured through bioprinting can be enhanced to improve performance. Bones can be made stronger and more flexible, the brain can be made smart and alert, the lungs can be made to increase oxygen efficiency in the blood, etc. In terms of cost, while critics claim that the price of bioprinted parts would be expensive, the fact remains that compared to waiting years for an organ to finally get to a patient who is at risk of losing their life any moment, this is fair. Most times these donated organs come at a very high cost (although sometimes illegal) because of the demand. However, the price of a bioprinted organ could reduce considerably when they clinically pass trials and patients wouldn't have to wait for several months, to a couple of years to get them. How close are we to the future? Brain, kidneys, muscles, skins, livers, pancreas, and so much more are in the laboratory phase of 3D experimentation. In the next 5 to 10 years, there is a high expectation of breakthroughs in these experiments, some passing clinical trials and being fully implementable. Needless to say, a few decades from now, people like Cynthia  (my late friend whose story I narrated at the beginning of this article) won't have to accept the cruel death verdict while waiting for the right organ match.

Bioprinting Organs on Demand: From Sci-fi to Reality and the Ethical Dilemma of Designer Bodies

Bias Leads to Intelligence Artificial Released from the corners of the boardroom for decision support, AI has now become a team player in the modern digital enterprise. And for the right reasons - according to a McKinsey survey, 66% of AI adopters report an increase in their revenues. Beyond money matters, AI can elevate the customer experience through various use cases. However, if done irresponsibly, AI wonders can quickly turn into AI disasters - and history is our accomplice in this observation. From introducing gender bias in hiring algorithms and racial bias in facial recognition algorithms to charging higher interest rates for underprivileged communities in loan decisioning use cases, AI disasters are commonplace and cost businesses more than just a few hundred million dollars, according to Harvard Business Review. Beyond the need for mitigating AI's downside impact on a business, building fair and responsible AI algorithms is as much a socio-ethical imperative for businesses that intend to stay in business. Take a look at how bias creeps into AI models and a framework to address bias in your enterprise AI strategy. Tracing Sources of Bias in Your AI Strategy Most enterprises have moved towards a standard framework for building business-driven use cases of AI. However, within the larger AIOps cycle, bias can creep into a model from any of the five stages that lead to the development of an AI model. Here are some of these sources: : AI models often make use of - consider a dataset of loan decisions, hiring decisions, or a list of buyers from a shopping mall. Bias within datasets datasets that carry the footprints of human bias within them Such datasets can inject bias into a model if their predictors are labeled erroneously, if certain types of communities are under-represented, or if the data sampling strategy was itself biased, to begin with. : AI models are built through extensive processes, which include feature selection, model tuning, and dataset splitting. Sometimes, excluding some features or conducting train-test splits without adequate oversight can inject data into the model. For instance, training a model with data from the earlier quarter of the year for a variable that follows an annual trend can lead to a biased model. Bias in model development : Some algorithms are prone to overfitting, while others are to underfitting. Again, understanding the model's needs from its real-world application can help data scientists identify the right level of complexity that minimizes the total error. Injecting bias during validation Moreover, preventing leakage between test and training data can be another source of bias during the model testing and validation phase. : NLP-powered bots that generate engagement and interaction within communities can pick biases from the humans they interact with. These algorithms can amplify the racial and gender biases that are to be found in the human voices on the platform. Acquiring interaction bias : Some models are biased in the very purpose they are built for. Consider the example of Google News, which returns similar stories when a user runs a search. Such programs can land the user in an information bubble built by the intent and parameters of their search query. Bias in purpose Recognizing the source of biases is the first step to building fair and bias-free AI models - followed by which enterprises should consider the following framework for mitigating bias from their AI strategy. A Roadmap to Achieving Fairness in AI Today, technical communities are making considerable efforts to mitigate bias in AI models. But three key levers are crucial to pay attention to. These include: Promoting responsible algorithm development, Doing standard checks during data collection and modeling, and Working with your CSR teams for responsible AI development. Look at these in detail, and consider checking out the hyperlinked resources for getting started. Promoting Responsible Algorithm Development The algorithm development process is prone to absorbing biased influences at multiple points. Here is a checklist for promoting enterprise-wide responsible algorithm development: Brainstorm the purpose of an algorithm, and optimize the point of equity-efficiency tradeoff collectively. Document the development process ( which teams are developing the model, potential risks of bias in the model, and privacy implications of the data used. here is a way to do that ), Hold meetings with developers to identify if an algorithm needs human oversight to mitigate risks of bias. Engage communities served by the system/algorithm, explain the model to them, and ask for their suggestions to improve model fairness. Conduct multiple audits on a developed model and provide the auditors with complete documentation. Lastly, promote explainability and run black-box auditing where design decisions and data models have been internalized into the algorithm. The above steps can help bring the risk zones into sight and mitigate bias-associated risks during algorithm development early on. Data Collection and Modeling: Standard Checks Data is the most significant source of bias in AI model building. Therefore, focus on data collection and modeling processes for eliminating bias, mitigating risk, and maximizing the business value of an AI model from the get-go. Consider the following steps for curating bias-free datasets across the enterprise: Run diversity and inclusivity tests on labels that describe datasets based on . comprehensive DEI lexicons like this one Document the sources of data and build checklists to identify possibilities of under or over-representation of a community. Define the standards for using third-party datasets by and implement guardrails to help developers follow these standards. leveraging data risk checkers Circulate comprehensive guidelines around what features are appropriate to be analyzed and before being used by a model in production. define privacy checks that a dataset should comply with Lastly, principles and processes to ensure responsible use of data should be internalized within the organization's data strategy. This can be achieved by enforcing technological guardrails that cannot be overridden by your users, and . using data from a variety of vetted sources Activate CSR for Responsible AI Development CSR teams often help an organization move the needle of equity across the larger fabric of society. Therefore, CSR teams can play a key role in advancing an organization’s responsible AI roadmap. Here is how to achieve this alignment: Shift CSR efforts towards the discourse of responsible AI and fund opportunities to develop novel ways of mitigating AI risks. Run CSR-driven programs to mitigate biases introduced by internal teams in building AI models. Market your efforts to the larger community. Lastly, create a symbiosis between CSR efforts and internal teams to align the organization's DEI vision to its efforts and generate employee engagement to foster holistic DEI thinking. Final Words The idea of bias and fairness is incomplete and therefore, evolving. Before embarking on the journey towards fair AI development, enterprises must acknowledge that their conceptions of fairness (and consequently bias) will be incomplete, too. But that is not a reason to sideline the question altogether. The promises of AI are abundant for businesses - that is, only if the technology is used in a responsible and socially and ethically conscious manner. By following the framework outlined above and leveraging responsible AI tools from hyper-scale technology providers, enterprises will be well-positioned to achieve holistically desirable outputs from their AI strategy.

A 3-Pronged Framework for Achieving Fair, Ethical, and Bias-Free Results From Your AI Strategy

The Ethics of AI-Generated Character Animation: Balancing Innovation with Responsibility

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details 6 Conclusion, Limitations, and Ethical Concerns In this work, we presented a general-purpose, intelligent BRI system that allows human users to control a robot to accomplish a diverse, challenging set of real-world activities using brain signals. NOIR enables human intention prediction through few-shot learning, thereby facilitating a more efficient collaborative interaction. NOIR holds a significant potential to augment human capabilities and enable critical assistive technology for individuals who require everyday support. NOIR represents a pioneering effort in the field, unveiling potential opportunities while simultaneously raising questions about its limitations and potential ethical risks which we address in Appendix 1. The decoding speed, as it currently stands, restricts tasks to those devoid of time-sensitive interactions. However, advancements in the field of neural signal decoding hold promise for alleviating this concern. Furthermore, the compilation of a comprehensive library of primitive skills presents a long-term challenge in robotics, necessitating additional exploration and development. Nonetheless, we maintain that once a robust set of skills is successfully established, human users will indeed be capable of applying these existing skills to complete new tasks. This paper is under CC 4.0 license. available on arxiv

Neural Signal Operated Intelligent Robots: Conclusion, Limitations, & Ethical Concerns

Welcome to a new episode of the " " podcast with your host, Louis-François Bouchard. In this episode, I'm excited to share my talk with of . This discussion is particularly meaningful to me as we dove into the ethical complexities surrounding autonomous vehicles, an area that blends technological innovation with moral considerations. What's AI Jeremy Cohen Think Autonomous Our conversation was more than just technical jargon; we explored the real-world implications and ethical dilemmas presented by AI in the realm of transportation. I always aim to keep the episodes accessible to everyone. For example, we questioned how AI's decision-making processes in critical scenarios could redefine human roles in the future of transport. Moreover, we broadened our discussion to encompass AI's transformative influence in various sectors, including healthcare and finance and reflected on the sustainability of AI startups amidst rapid technological advancements. We talked about lots of topics including autonomous vehicles but not only! There’s something for everyone in this episode. I also tried a fresh format, engaging in “debates” around AI I came up with, providing listeners with, hopefully, a few new perspectives around AI. It's an essential listen for anyone interested in the intersection of AI, ethics, and technology, from industry professionals to curious individuals. So, join us as we dive into this fascinating discussion about AI, autonomous vehicles, and their impact on society. Catch this episode on , and don't forget to follow the "What's AI" podcast by Louis-François Bouchard on and for more insightful content! YouTube Spotify Apple Podcasts https://youtu.be/5YIeBhHbwNc?embedable=true&transcript=true The timestamps to help you decide if the topics seem interesting to you: 00:00:00 Coming up in the conversation 00:01:31 Jeremy shares about his background 00:03:55 The future of Self-driving cars is not that straightforward! 00:07:49 If there are numerous challenges, why are companies still developing autonomous cars? 00:08:46 The future might involve more self-driving buses and trucks instead of cars 00:09:24 Are AI Start-ups dead? 00:14:25 Can ‘wannabe’ AI start-ups harm the actual AI economy and market? 00:16:50 Should AI research prioritize progress over control and a deep understanding of algorithms? 00:21:14 Can AI replace experts? 00:24:04 If we make AI hyper-personalized or make it impersonate someone, can it replace experts? 00:25:18 If AI cannot replace experts, should we be worried about our jobs? 00:26:18 How to find out if your job can be taken by AI or not? 00:33:06 Potential of AI in creative expression, entertainment, and journalism 00:38:11 Will AI make us dumb? 00:40:16 Can hallucination be fixed or not? 00:46:29 Is it possible to build a biasless AI model? 00:52:24 Transparency is going to be a big thing AI economy 00:55:59 AI can make your content boring! 01:02:39 Your mom might not use AI unless this happens! 01:09:08 Is AI democratizing opportunities or is it still only benefitting the rich?

Steering the Future: The Ethical Roadmap of Autonomous Vehicles

In the modern world, where artificial intelligence (AI) plays an increasingly important role, the issue of how ethical principles guide AI behavior becomes particularly relevant. This issue takes on even greater significance in the context of developing powerful artificial intelligence capable of independent thought and self-awareness. To understand the potential effects of ethics on AI, it is necessary to review two main approaches to ethics: deontological and utilitarian. In the age of rapid technological development, artificial intelligence (AI) has become a major transformational power. Although AI holds tremendous promise in a variety of fields, from healthcare to finance, it is important to acknowledge and consider the potential dangers it poses ( ). source Deontological ethics or duty-based ethics is founded on principles considered to be absolute. Deontological ethics in philosophy, ethical theories that focus on the relationship between duty and the morality of human actions. The term “deontology” is derived from the Greek deon, “duty”, and logos, “science”. In deontological ethics, an action is considered morally good by some property of the action itself, not because the product of the action is good. Deontological ethics states that at least some actions are morally obligatory regardless of their consequences for human welfare. This ethics is described by expressions such as “Duty for duty's sake”, “Honesty is its own reward” and “Let justice be done though the heavens fall” ( ). source This approach assumes that certain actions are right or wrong regardless of their consequences. Philosopher Immanuel Kant was one of the main proponents of deontology, arguing that behavior should be based on duties and universal moral laws. In this case, moral principles represent unchangeable rules that must be observed. Referring to utilitarianism, it is primarily based on the works of philosophers such as Jeremy Bentham and John Stuart Mill and evaluates actions by their consequences. The fundamental idea of altruism is that actions that are beneficial to the majority of people are considered right. This approach is focused on achieving the common good, even if it requires sacrifices from some individuals. Utilitarianism is an ethical theory that distinguishes good from evil by focusing on results. It is a form of consequentialism. Utilitarianism states that the most moral choice is the one that will bring about the greatest good for the largest number of people. This is the only moral ground that can be used to justify military force or warfare. It is also the most common approach to moral reasoning used in business through the way it considers costs and benefits. However, since we are unable to predict the future, it is difficult to know exactly whether the consequences of our actions will be good or bad. This is one of the limitations of utilitarianism ( ). source The difference between deontology and utilitarianism is that the former can ascribe adherence to certain principles even if it may have a negative impact on society, whereas the latter can justify morally questionable actions if they serve the common good. The first thing to realize is that there are 4 different types of artificial intelligence. The degree to which an AI system can reproduce human capabilities is used as a criterion for defining AI types. There are 4 main types of artificial intelligence: • Reactive machines • Limited memory • Theory of mind • Self-awareness ( ) source Regardless of what moral values guide artificial intelligence, there is a certain assumption that artificial intelligence tends to be kind (although it is not endowed with any feelings at this point). This is because AI can contribute to optimizing certain goals that meet the basic principles of algorithms. This approach involves making decisions based on the calculation of the greatest common good, even if this has negative consequences for individuals or groups. It is important to note that the fear associated with strong AI is that it may perceive humanity as a threat or an inefficient agent to achieve the optimal results. This raises the concern that strong AI will lead to dangerous decisions for humanity. But it's important to keep in mind that at this point, AI systems are only as impartial as the data they learn from. If the data used to train artificial intelligence models are biased, artificial intelligence itself can maintain and even enhance social biases. This can lead to discriminatory effects in areas such as employment, lending and criminal justice ( ). source However, there is an important question to be raised: why do we believe that strong AI is motivated by self-interest? Finally, we don't really know how moral principles will determine the behavior of an advanced AI. Perhaps the AI of the future will combine different ethical systems to create a more balanced and safer way of making decisions. Anyway, it is worth remembering that even now artificial intelligence has already achieved an extraordinary level of development and progress, for example, a common at first glance GPT chat during its first year of existence has turned from an ordinary text generator into a comprehensively developed system with the ability to generate images and form different tables and charts. The study of these questions is of critical importance to the development and progress of artificial intelligence, which is currently working for the benefit of all humanity (except when AI is used for dangerous purposes), and requires further analysis and debate among experts in artificial intelligence, philosophy and ethics.

Deontological Ethics, Utilitarianism and AI

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