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Why Smart Design Decisions in Payment Systems Can Save You Millions

Choice Is Not the Enemy: Why Users Need More Options for Better Decision-Making

The HackerNoon Newsletter: The Product Manager’s Decision Making Dilemma (11/7/2024)

The number one challenge a product manager faces is prioritization. You make choices every day. How many times have you heard PMs say “data-driven” when asked about making roadmap decisions? This data-driven approach is easier said than done. More often than usual, the problem is not a dearth of great ideas. Prioritization is all about how to choose ‘what’ to build ‘when’ and ‘why’. Shouldn’t it be as simple as sifting through customer feedback, picking the most innovative and ‘seemingly cool’ ideas and start implementing? What makes this process so difficult? Why is prioritization difficult? Every product has a feedback loop, some more mature than others. Your customers provide you their ‘wish-list’ of features, your sales team has a set of requirements, they wished the product fulfilled in order to make better sales, you engineering team comes up with a list of tasks that are needed to make the product more resilient and your designers have these great ideas for a better, more intuitive UX/UI. It is these branches of feedback loop and the emerging competing opportunities that make it extremely difficult to decide what to build next. You wish you could build anything and everything from the above list, however, capacity in terms of resources (people, time, budget) to deliver is always limited. In addition, once you release a product, you inherently promise incremental predictable value, release after release and being able live up to that promise is extremely important in order to sustain customer trust. What does a PM need to prioritize? Prioritization happens at different levels. On a goals level, one has to prioritize on the metrics, the OKRs (objectives-key-result) and the KPIs (key performance indicators to evaluate and track progress toward the objectives.
At the strategy level, one has to prioritize on the high- level initiatives for the product.
On an architectural level, one has to prioritize on the platform technologies, scalability considerations, interoperability challenges etc.
At the release level, one has to prioritize on the sprints, features, user stories, epics and bugs. At all these levels, prioritization is performed using a combination of data, instinct and a consensus amongst stakeholders, which makes it even harder to define that standard magic formula that can be applied to all prioritization exercises. So how do you approach this? Before we dive into the best model to approach prioritization, there are a few non-prescriptive the techniques that I personally incorporate into my product planning. Product Vision As clichéd as it may sound, everything begins with the product vision. Once you have your product strategy defined, you plan a series of events that need to happen in order to achieve it. This plan is basically a detailed description of what has to happen within the product and what capabilities you hope to build. All prioritization decisions should then reflect that plan and be in constant check with your product vision. Every roadmap review and planning session should track progress toward achieving this vision and what would be the next steps or milestones to get you closer to the vision. In Marie Kondo’s words, When the ultimate goal is “Joy”, constantly ask yourself “Does it spark joy?”. Similarly, everything on the backlog should drive your product strategy forward and hence we need to ask, “Does this support the product strategy?” Customer Mindset Everyone is aware of Amazon’s 14 leadership Principles, the first of which is ‘customer obsession’. “The number one thing that has made us successful by far is obsessive compulsive focus on the customer as opposed to obsession over the competitor” — Bezos Now, I am not saying, what worked for Amazon, will or should work for your product. But keeping the customer at the center of every planning and prioritization exercise is key to ensure you are building things in the right direction. Also, beware as you do this, because customers will always want more, always ask for a new shiny feature the competitive product may have launched, however, it is your job to go beyond this ask into uncovering the actual need to solve a problem. You really need to go deep into revealing the real vs perceived value. What is a required vs a good to have capability? Does developing this new feature or set of features benefit a significant addressable customer segment? Impact What are the drivers for your business objectives and what impact does this feature have on moving the needle on those. Your eventual goal is to be profitable, however, for any company the typical business objectives are one of the following, depending on the stage of the product: New customer acquisition
Increase product engagement
Increase revenue from existing customers (up-sell)
Expand product into other markets/regions/parts of the customer org.
Customer retention In each roadmap planning session (however often you may perform it — quarterly or bi-annually), evaluate how you are doing on each of the above business objectives. Risk As a PM, you are responsible for every aspect of the product, not just shipping features out the door. Awareness of competitors, legal adherences, compliance, technical feasibilities, security, privacy etc. are crucial considerations while prioritization. You need to ask the tough questions, once released, can it be supported and maintained? If it fails, can we handle the consequences?
It is also important to have a common understanding amongst stakeholders of what is non-negotiable. For example, a social media product has to prioritize features that de-risk user privacy over say, working toward a new video chatting feature. A healthcare product has to prioritize efforts that make the product adhere to HIPAA guidelines vs building a new chatbot for customer service. Thus, it is important to incorporate a risk measurement exercise into your roadmap planning process.
In addition, as you think more about the execution phase, identifying dependency risks and mechanism to unblock them should be considered in prioritization and estimation. What techniques can I use to prioritize? There are several frameworks that can be applied to prioritization: The MoSCoW (must, should, could, won’t) method allows you to categorize your list of requirements or ideas into the sets of critical, high-priority, desirable and future. The KANO model, helps to understand the customer’s perspective on product features by assessing their satisfaction by identifying the basic, expected and attractive attributes of the product. The RICE scoring model combines factors of Reach, Impact, Confidence and Efforts. I am a very visual person and what I personally use for prioritizing roadmap initiative is the ‘Story Mapping’ method. This allows me to map features and user stories against ‘time’ and ‘necessity’ and cutting off the list to plan the MVP and subsequent releases. First you lay out the activity buckets (which are major attributes) for your product along the horizontal axis. The vertical axis represents the criticality in terms of how important the user stories are. Groups of user stories qualify as one activity. In order to define releases, you then mark a cut-off horizontal line, selecting stories that have fall into the same criticality level. This visualization technique provides a clear picture of incremental product iterations and provides stakeholders a visual map of an end-to-end product version in each release. Below is an example from Jeff Patton’s User Story Mapping. Of course there are many more frameworks out there, depending on the maturity stage and complexity of the product. No framework is perfect and the best way to create your own secret sauce for prioritization is to adopt, modify, test and test again to ultimately get to what works for you. And lastly, always remember to keep the key principles of product vision, customer mindset, impact and risk measurement as you plan your product roadmap. The number one challenge a product manager faces is prioritization. You make choices every day. How many times have you heard PMs say “data-driven” when asked about making roadmap decisions? This data-driven approach is easier said than done. More often than usual, the problem is not a dearth of great ideas. Prioritization is all about how to choose ‘what’ to build ‘when’ and ‘why’. Prioritization is all about how to choose ‘what’ to build ‘when’ and ‘why’. Shouldn’t it be as simple as sifting through customer feedback, picking the most innovative and ‘seemingly cool’ ideas and start implementing? What makes this process so difficult? Why is prioritization difficult? Why is prioritization difficult? Every product has a feedback loop, some more mature than others. Your customers provide you their ‘wish-list’ of features, your sales team has a set of requirements, they wished the product fulfilled in order to make better sales, you engineering team comes up with a list of tasks that are needed to make the product more resilient and your designers have these great ideas for a better, more intuitive UX/UI. It is these branches of feedback loop and the emerging competing opportunities that make it extremely difficult to decide what to build next. You wish you could build anything and everything from the above list, however, capacity in terms of resources (people, time, budget) to deliver is always limited. In addition, once you release a product, you inherently promise incremental predictable value, release after release and being able live up to that promise is extremely important in order to sustain customer trust. What does a PM need to prioritize? What does a PM need to prioritize? Prioritization happens at different levels. On a goals level, one has to prioritize on the metrics, the OKRs (objectives-key-result) and the KPIs (key performance indicators to evaluate and track progress toward the objectives. At the strategy level, one has to prioritize on the high- level initiatives for the product. On an architectural level, one has to prioritize on the platform technologies, scalability considerations, interoperability challenges etc. At the release level, one has to prioritize on the sprints, features, user stories, epics and bugs. On a goals level, one has to prioritize on the metrics, the OKRs (objectives-key-result) and the KPIs (key performance indicators to evaluate and track progress toward the objectives. OKRs At the strategy level, one has to prioritize on the high- level initiatives for the product. On an architectural level, one has to prioritize on the platform technologies, scalability considerations, interoperability challenges etc. At the release level, one has to prioritize on the sprints, features, user stories, epics and bugs. At all these levels, prioritization is performed using a combination of data, instinct and a consensus amongst stakeholders, which makes it even harder to define that standard magic formula that can be applied to all prioritization exercises. So how do you approach this? So how do you approach this? Before we dive into the best model to approach prioritization, there are a few non-prescriptive the techniques that I personally incorporate into my product planning. Product Vision Product Vision As clichéd as it may sound, everything begins with the product vision. Once you have your product strategy defined, you plan a series of events that need to happen in order to achieve it. This plan is basically a detailed description of what has to happen within the product and what capabilities you hope to build. All prioritization decisions should then reflect that plan and be in constant check with your product vision. Every roadmap review and planning session should track progress toward achieving this vision and what would be the next steps or milestones to get you closer to the vision. In Marie Kondo’s words, When the ultimate goal is “Joy”, constantly ask yourself “Does it spark joy?”. Similarly, everything on the backlog should drive your product strategy forward and hence we need to ask, “Does this support the product strategy?” product strategy Marie Kondo’s Customer Mindset Customer Mindset Everyone is aware of Amazon’s 14 leadership Principles , the first of which is ‘customer obsession’. 14 leadership Principles “The number one thing that has made us successful by far is obsessive compulsive focus on the customer as opposed to obsession over the competitor” — Bezos “The number one thing that has made us successful by far is obsessive compulsive focus on the customer as opposed to obsession over the competitor” — Bezos Now, I am not saying, what worked for Amazon, will or should work for your product. But keeping the customer at the center of every planning and prioritization exercise is key to ensure you are building things in the right direction. Also, beware as you do this, because customers will always want more, always ask for a new shiny feature the competitive product may have launched, however, it is your job to go beyond this ask into uncovering the actual need to solve a problem. You really need to go deep into revealing the real vs perceived value. What is a required vs a good to have capability? Does developing this new feature or set of features benefit a significant addressable customer segment? Impact Impact What are the drivers for your business objectives and what impact does this feature have on moving the needle on those. Your eventual goal is to be profitable, however, for any company the typical business objectives are one of the following, depending on the stage of the product: New customer acquisition Increase product engagement Increase revenue from existing customers (up-sell) Expand product into other markets/regions/parts of the customer org. Customer retention New customer acquisition Increase product engagement Increase revenue from existing customers (up-sell) Expand product into other markets/regions/parts of the customer org. Customer retention In each roadmap planning session (however often you may perform it — quarterly or bi-annually), evaluate how you are doing on each of the above business objectives. Risk Risk As a PM, you are responsible for every aspect of the product, not just shipping features out the door. Awareness of competitors, legal adherences, compliance, technical feasibilities, security, privacy etc. are crucial considerations while prioritization. You need to ask the tough questions, once released, can it be supported and maintained? If it fails, can we handle the consequences? It is also important to have a common understanding amongst stakeholders of what is non-negotiable. For example, a social media product has to prioritize features that de-risk user privacy over say, working toward a new video chatting feature. A healthcare product has to prioritize efforts that make the product adhere to HIPAA guidelines vs building a new chatbot for customer service. Thus, it is important to incorporate a risk measurement exercise into your roadmap planning process. In addition, as you think more about the execution phase, identifying dependency risks and mechanism to unblock them should be considered in prioritization and estimation. Awareness of competitors, legal adherences, compliance, technical feasibilities, security, privacy etc. are crucial considerations while prioritization. You need to ask the tough questions, once released, can it be supported and maintained? If it fails, can we handle the consequences? It is also important to have a common understanding amongst stakeholders of what is non-negotiable. For example, a social media product has to prioritize features that de-risk user privacy over say, working toward a new video chatting feature. A healthcare product has to prioritize efforts that make the product adhere to HIPAA guidelines vs building a new chatbot for customer service. Thus, it is important to incorporate a risk measurement exercise into your roadmap planning process. In addition, as you think more about the execution phase, identifying dependency risks and mechanism to unblock them should be considered in prioritization and estimation. What techniques can I use to prioritize? There are several frameworks that can be applied to prioritization: The MoSCoW (must, should, could, won’t) method allows you to categorize your list of requirements or ideas into the sets of critical, high-priority, desirable and future. MoSCoW The KANO model , helps to understand the customer’s perspective on product features by assessing their satisfaction by identifying the basic, expected and attractive attributes of the product. KANO model The RICE scoring model combines factors of Reach, Impact, Confidence and Efforts. I am a very visual person and what I personally use for prioritizing roadmap initiative is the ‘Story Mapping’ method. This allows me to map features and user stories against ‘time’ and ‘necessity’ and cutting off the list to plan the MVP and subsequent releases. ‘Story Mapping’ First you lay out the activity buckets (which are major attributes) for your product along the horizontal axis. The vertical axis represents the criticality in terms of how important the user stories are. Groups of user stories qualify as one activity. In order to define releases, you then mark a cut-off horizontal line, selecting stories that have fall into the same criticality level. This visualization technique provides a clear picture of incremental product iterations and provides stakeholders a visual map of an end-to-end product version in each release. Below is an example from Jeff Patton’s User Story Mapping . User Story Mapping Of course there are many more frameworks out there, depending on the maturity stage and complexity of the product. No framework is perfect and the best way to create your own secret sauce for prioritization is to adopt, modify, test and test again to ultimately get to what works for you. And lastly, always remember to keep the key principles of product vision, customer mindset, impact and risk measurement as you plan your product roadmap.

The Product Manager’s Decision Making Dilemma

Дылема прыняцця рашэнняў менеджэрам па прадуктах

Dilema rozhodování produktového manažera

Das Entscheidungsdilemma des Produktmanagers

El dilema de la toma de decisiones del gerente de producto

معضل تصمیم گیری مدیر محصول

O dilema da toma de decisións do xestor de produto

Dilema pri donošenju odluka voditelja proizvoda

プロダクトマネージャーの意思決定のジレンマ

Icyemezo cyumuyobozi wibicuruzwa Gufata Dilemma

தயாரிப்பு மேலாளரின் முடிவெடுக்கும் குழப்பம்

Önüm dolandyryjysynyň karary Dilemma

پروڈکٹ مینیجر کا فیصلہ سازی مشکوک

At this moment in time, virtually no industries remain that are untouched by AI penetration. From finance to healthcare, manufacturing to education, sectors are witnessing nothing less than an AI-powered revolution, one that is transforming the landscape of organizational operations, particularly in terms of how decisions are made. As businesses increasingly harness the power of AI-enabled analytics to tap into vast volumes of data, complex questions emerge. How much AI is too much? How can we best combine human experience, ingenuity and instinct with Machine Learning and Large Language Models (LLMs)? These queries are likely to ignite fierce debate for many years to come. One thing that cannot be denied is the utility and power of AI: even the most ardent skeptic must grudgingly acknowledge the technology’s potentially enormous impact on productivity, even if they bemoan the downstream effect on, say, human jobs or wages. Embracing the AI Revolution By far the most talked-about topic relating to AI’s effect on businesses is its ability to reduce dependence on manual labor. This article will not dwell on the moral ramifications of this trend, but rather consider the technology’s impact on organizations themselves. The most obvious example of AI in action is digital customer support agents, which are increasingly used to replace human operators. These chatbot agents are trained on common customer queries and provide 24/7 assistance, handling mostly routine inquiries with unprecedented efficiency. A McKinsey report suggests that about half of all customer service tasks could be automated by the end of the decade. Real-time production process monitoring, automated accounting, and simplified regulatory reporting are just a few other examples of how AI is transforming traditional business functions. As for the impact on workplaces themselves, PwC’s 2024 AI Jobs Barometer provides plenty of food for thought. The PwC survey shows that 84% of CEOs whose firms have begun adopting AI believe it will increase employee efficiency. Moreover, 70% of leaders anticipate AI will significantly change the way their business creates, delivers, and captures value over the next three years. AI can certainly analyze huge volumes and automate processes that would be time-consuming for a human to carry out. But what about making meaningful decisions? Can an AI-powered system really make educated judgment calls on matters of recruitment, budgeting, and so on? GenLayer, an intelligent blockchain that operates at the intersection of AI and distributed ledger technology, believes the answer could be yes. That’s because its AI-powered smart contracts are able to leverage Large Language Models and, unlike regular smart contracts, access the internet to autonomously make complex decisions in a decentralized manner. Indeed, these so-called Intelligent Contracts can handle a variety of non-deterministic operations including processing natural language and making complex judgments without human intervention. In fact, they’re flexible enough to incorporate multiple legal frameworks – meaning they could even be used to make nuanced decisions in matters of jurisprudence. This August, investors backed GenLayer to the tune of $7.5 million, with one of them, North Island Ventures’ Travis Scher, stating the intelligent blockchain could “enhance current decentralized applications (dApps) and unlock new potentials.” The Arrival of AI-Powered DAOs The way decisions are actually made is through an organizational structure familiar to those working in blockchain: DAOs, or decentralized autonomous organizations. The difference is, with GenLayer the DAOs are AI-powered. The breakthrough represents a new paradigm in organizational management, offering more transparent, efficient, and data-driven means of decision-making. Any organization that relies on real-time data to make decisions (virtually every business, in truth) can leverage GenLayer to make judgments that would otherwise fall on the shoulders of executives. Armed with accurate internet-sourced information, the DAO can even take decisions that accord with a constitution encoded directly into its Intelligent Contract. In the 13th century, the Consulate of the Sea provided a unified legal framework for maritime trade across borders. What GenLayer hopes to achieve is consulate status in the age of AI, offering a global, adaptable jurisdiction that enables efficient and secure AI-driven commerce. Why AI-Powered Decisions? AI’s ability to process enormous data sets, and to do so quickly, makes it the perfect decision making system or tool. After all, not all judgments can be made based purely on computation. While some decisions can be delegated to AI, others require human input. Nonetheless, even in the second scenario AI plays a role in presenting to the decision-maker the mother lode of data and insight required to make the right call. The phrase AI-powered decision-making is apposite: the AI remains a tool, and its integration into organizational processes, coupled with the emergence of technologies like GenLayer, promises to bring about a dynamic new era of operational efficiency. As we navigate this evolving landscape, it’s vital for leaders of businesses and governments to stay informed about these technologies and consider how they can be leveraged to create value, improve decision-making, and drive innovation. While challenges and questions remain, the potential benefits of AI and intelligent blockchain technologies are immense. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR At this moment in time, virtually no industries remain that are untouched by AI penetration. From finance to healthcare, manufacturing to education, sectors are witnessing nothing less than an AI-powered revolution, one that is transforming the landscape of organizational operations, particularly in terms of how decisions are made. As businesses increasingly harness the power of AI-enabled analytics to tap into vast volumes of data, complex questions emerge. How much AI is too much? How can we best combine human experience, ingenuity and instinct with Machine Learning and Large Language Models (LLMs)? These queries are likely to ignite fierce debate for many years to come. One thing that cannot be denied is the utility and power of AI: even the most ardent skeptic must grudgingly acknowledge the technology’s potentially enormous impact on productivity, even if they bemoan the downstream effect on, say, human jobs or wages. Embracing the AI Revolution By far the most talked-about topic relating to AI’s effect on businesses is its ability to reduce dependence on manual labor. This article will not dwell on the moral ramifications of this trend, but rather consider the technology’s impact on organizations themselves. The most obvious example of AI in action is digital customer support agents, which are increasingly used to replace human operators. These chatbot agents are trained on common customer queries and provide 24/7 assistance, handling mostly routine inquiries with unprecedented efficiency. A McKinsey report suggests that about half of all customer service tasks could be automated by the end of the decade. report report Real-time production process monitoring, automated accounting, and simplified regulatory reporting are just a few other examples of how AI is transforming traditional business functions. As for the impact on workplaces themselves, PwC’s 2024 AI Jobs Barometer provides plenty of food for thought. 2024 AI Jobs Barometer 2024 AI Jobs Barometer The PwC survey shows that 84% of CEOs whose firms have begun adopting AI believe it will increase employee efficiency. Moreover, 70% of leaders anticipate AI will significantly change the way their business creates, delivers, and captures value over the next three years. AI can certainly analyze huge volumes and automate processes that would be time-consuming for a human to carry out. But what about making meaningful decisions? Can an AI-powered system really make educated judgment calls on matters of recruitment, budgeting, and so on? GenLayer , an intelligent blockchain that operates at the intersection of AI and distributed ledger technology, believes the answer could be yes. That’s because its AI-powered smart contracts are able to leverage Large Language Models and, unlike regular smart contracts, access the internet to autonomously make complex decisions in a decentralized manner. GenLayer GenLayer Indeed, these so-called Intelligent Contracts can handle a variety of non-deterministic operations including processing natural language and making complex judgments without human intervention. In fact, they’re flexible enough to incorporate multiple legal frameworks – meaning they could even be used to make nuanced decisions in matters of jurisprudence. This August, investors backed GenLayer to the tune of $7.5 million, with one of them, North Island Ventures’ Travis Scher, stating the intelligent blockchain could “enhance current decentralized applications (dApps) and unlock new potentials.” backed GenLayer backed GenLayer The Arrival of AI-Powered DAOs The way decisions are actually made is through an organizational structure familiar to those working in blockchain: DAOs, or decentralized autonomous organizations. The difference is, with GenLayer the DAOs are AI-powered. The breakthrough represents a new paradigm in organizational management, offering more transparent, efficient, and data-driven means of decision-making. Any organization that relies on real-time data to make decisions (virtually every business, in truth) can leverage GenLayer to make judgments that would otherwise fall on the shoulders of executives. Armed with accurate internet-sourced information, the DAO can even take decisions that accord with a constitution encoded directly into its Intelligent Contract. directly In the 13th century, the Consulate of the Sea provided a unified legal framework for maritime trade across borders. What GenLayer hopes to achieve is consulate status in the age of AI, offering a global, adaptable jurisdiction that enables efficient and secure AI-driven commerce. Why AI-Powered Decisions? AI’s ability to process enormous data sets, and to do so quickly, makes it the perfect decision making system or tool. After all, not all judgments can be made based purely on computation. While some decisions can be delegated to AI, others require human input. Nonetheless, even in the second scenario AI plays a role in presenting to the decision-maker the mother lode of data and insight required to make the right call. or The phrase AI-powered decision-making is apposite: the AI remains a tool, and its integration into organizational processes, coupled with the emergence of technologies like GenLayer, promises to bring about a dynamic new era of operational efficiency. As we navigate this evolving landscape, it’s vital for leaders of businesses and governments to stay informed about these technologies and consider how they can be leveraged to create value, improve decision-making, and drive innovation. While challenges and questions remain, the potential benefits of AI and intelligent blockchain technologies are immense. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

How Organizational Decision-Making is Getting an AI Shakeup – And Why It’s a Good Thing

Як прыняцце арганізацыйных рашэнняў перабудоўваецца з дапамогай штучнага інтэлекту - і чаму гэта добра

Как вземането на организационни решения претърпява промяна на ИИ – и защо това е нещо добро

Jak organizační rozhodování je získání AI Shakeup – a proč je to dobrá věc

Cómo la toma de decisiones organizacionales está siendo revolucionada por la IA (y por qué es algo positivo)

Kuinka organisaation päätöksenteko saa AI-muutoksen – ja miksi se on hyvä asia

Como a toma de decisións organizativas está a ter un cambio de intelixencia artificial e por que é unha boa cousa

איך קבלת החלטות ארגונית מקבלת שיקום בינה מלאכותית - ולמה זה דבר טוב

組織の意思決定が AI によって大きく変化している理由とそれが良いこと

Como a tomada de decisões organizacionais está passando por uma reformulação da IA – e por que isso é uma coisa boa

Kuita Sarudzo YeSangano-Kuita Kuwana AI Shakeup - Uye Nei Icho Chinhu Chakanaka

Si është Marrja e Vendimeve Organizative duke marrë një ndryshim të AI - dhe pse është një gjë e mirë

Jinsi Uamuzi wa Kishirika Unavyopata Shakeup ya AI - Na Kwa Nini Ni Jambo Jema

Cross-Platform Design Wrapped Part 1: How to Anchor Every UX Decision

Trends and Predictions: How Social Media Influencers Shape Consumer Buying Decisions

Poring through university admissions applications takes immense manual labor, and human bias is often the subject of questionable headlines because of poor decision-making. AI could solve this problem, but bias oversights are still plentiful enough to negatively impact a student’s future. How Is AI Used in College Admissions? AI does more than scan college applications to see if they meet requirements, though this is its primary application. Application Screening Algorithms are customizable depending on a university’s standards for seeking applications that meet the criteria. AI can scan numbers from standardized tests, identify the value of extracurricular commitments and judge this against coursework. Predictive Analytics Machine learning can compare previous applicants to incoming ones to judge their chances of success. This is helpful from an administrative perspective because it gives boards insights into potential retention rates in the coming years. The process becomes more accurate the longer a university uses AI. Personalized Communication During and after the application process, competent chatbots could be invaluable resources for students. If they have questions about submitting forms or what a prompt means, a chatbot has the knowledge to give helpful responses. Bias Mitigation AI algorithms are just as capable of defeating bias as formulating it. For years, humans have turned the other way to accept some applications even if they did not meet a metric. An AI will not. Aspects of the admissions process become more objective because it removes some chances for human-based bias to compromise the process. How Could AI Develop Bias? Universities are responsible for collaborating with AI engineers and data scientists to filter out the chance of bias. This is an around-the-clock job, as the algorithms constantly change as they learn from themselves. Data Bias These repetitions could cause data bias, which is one of the biggest oversights for admissions. This is when the dataset includes information that promotes harmful training, such as discriminating against certain demographics because of historical tendencies. Algorithmic Bias Algorithmic bias is another red flag. This occurs when criteria carry different weights throughout the training process. Is the model starting to prioritize test scores over GPA, or is it skewed to prefer families with alumni? Experts must adjust how the AI views each metric to ensure consistency after repeated learning cycles. The University of California made a landmark decision to remove standardized test scores from its considerations because of systemic racism and ableism causing disadvantages to students. Years of historical trends like this do not bode well for training AI because it will need to learn how to weigh this metric if necessary in some places. Additionally, the depth of the bias will take a lot of work to overcome. Feedback Loops This hints at how dangerous feedback loops can be. Depending on the learning environment AI overseers employ, the model may become more extreme in its outcomes. Positive reinforcement could favor students with specific backgrounds or demographics. Researchers see this bias problem impacting universities, but graduates will find this extends into job applications. The crossover is immense, proven when researchers enter fake but demographically distinct names in ChatGPT 3.5 against a real job posting. AI put names culturally associated with Black Americans lower than names tied to white men and Asian women. Emotional Gaps An estimated 90% of students with over a 4.0 GPA and flawless SAT scores still fail to get into America’s top 10 universities. Adding AI oversights could make matters worse or inconsistent. AI has limited contextual understanding of an applicant’s experiences with too much weight on quantitative interpretations. It may not know how to judge a compelling story in an application essay or students who have to apply using nontraditional methods. What Can Admissions Officials Do? Some universities have already been privy to biased AI, and avoiding these fiascos in the future is crucial for maintaining reputation and keeping applicants interested. What proactive and preventive measures can schools take to keep students safe and be judged accurately? Many AI enthusiasts wonder if curating historical data, potentially skewing it or making the past inaccurate, would improve modern bias. Diverse and representative training alongside continuous monitoring is a more proactive approach to designing bias-free AI for admissions. This includes input from inclusive, interdisciplinary populations and datasets to fill gaps in socioeconomic, ethnic, educational and demographic experiences. Incorporating bias detection and mitigation tools will complement these efforts. AI professionals should help university leaders become literate in AI bias. They can offer workshops for noticing anomalies and providing feedback for further training. This helps if the admissions AI has integrated transparency, forcing it to explain where it derived its decisions. These insights are roadmaps for all AI users because they trace the source of problematic and biased information. Universities can also take a mixed approach to admissions, including human interactivity, to judge qualitative aspects. A recent study revealed an AI model was a poor predictor of educational outcomes because of its racial biases. It was incorrect at predicting the academic success of students with Asian backgrounds 73% of the time. AI can look at numbers effectively with minimal error, but humans may be better judges when reading personal statements or conducting interviews. Students in the Hands of AI Removing AI bias from college admissions is a deceptively critical issue, as poor data management could disrupt many students’ bright futures. Data scientists and university leaders must prioritize this, as the next-generation workforce relies on its accuracy and integrity to give them a chance at education. Neglect will cause numerous problems for all industries that need experienced professionals walking in the door. Poring through university admissions applications takes immense manual labor, and human bias is often the subject of questionable headlines because of poor decision-making. AI could solve this problem, but bias oversights are still plentiful enough to negatively impact a student’s future. Poring through university admissions applications takes immense manual labor, and human bias is often the subject of questionable headlines because of poor decision-making. AI could solve this problem, but bias oversights are still plentiful enough to negatively impact a student’s future. How Is AI Used in College Admissions? How Is AI Used in College Admissions? AI does more than scan college applications to see if they meet requirements, though this is its primary application. AI Application Screening Application Screening Algorithms are customizable depending on a university’s standards for seeking applications that meet the criteria. AI can scan numbers from standardized tests, identify the value of extracurricular commitments and judge this against coursework. Predictive Analytics Predictive Analytics Machine learning can compare previous applicants to incoming ones to judge their chances of success. This is helpful from an administrative perspective because it gives boards insights into potential retention rates in the coming years. The process becomes more accurate the longer a university uses AI. insights into potential retention rates Personalized Communication Personalized Communication During and after the application process, competent chatbots could be invaluable resources for students. If they have questions about submitting forms or what a prompt means, a chatbot has the knowledge to give helpful responses. Bias Mitigation Bias Mitigation AI algorithms are just as capable of defeating bias as formulating it. For years, humans have turned the other way to accept some applications even if they did not meet a metric. An AI will not. Aspects of the admissions process become more objective because it removes some chances for human-based bias to compromise the process. How Could AI Develop Bias? How Could AI Develop Bias? Universities are responsible for collaborating with AI engineers and data scientists to filter out the chance of bias. This is an around-the-clock job, as the algorithms constantly change as they learn from themselves. Data Bias Data Bias These repetitions could cause data bias, which is one of the biggest oversights for admissions. This is when the dataset includes information that promotes harmful training, such as discriminating against certain demographics because of historical tendencies. Algorithmic Bias Algorithmic Bias Algorithmic bias is another red flag. This occurs when criteria carry different weights throughout the training process. Is the model starting to prioritize test scores over GPA, or is it skewed to prefer families with alumni? Experts must adjust how the AI views each metric to ensure consistency after repeated learning cycles. The University of California made a landmark decision to remove standardized test scores from its considerations because of systemic racism and ableism causing disadvantages to students. Years of historical trends like this do not bode well for training AI because it will need to learn how to weigh this metric if necessary in some places. Additionally, the depth of the bias will take a lot of work to overcome. remove standardized test scores remove standardized test scores Feedback Loops This hints at how dangerous feedback loops can be. Depending on the learning environment AI overseers employ, the model may become more extreme in its outcomes. Positive reinforcement could favor students with specific backgrounds or demographics. Researchers see this bias problem impacting universities, but graduates will find this extends into job applications. The crossover is immense, proven when researchers enter fake but demographically distinct names in ChatGPT 3.5 against a real job posting. AI put names culturally associated with Black Americans lower than names tied to white men and Asian women. culturally associated with Black Americans lower culturally associated with Black Americans lower Emotional Gaps An estimated 90% of students with over a 4.0 GPA and flawless SAT scores still fail to get into America’s top 10 universities. Adding AI oversights could make matters worse or inconsistent. AI has limited contextual understanding of an applicant’s experiences with too much weight on quantitative interpretations. It may not know how to judge a compelling story in an application essay or students who have to apply using nontraditional methods. estimated 90% of students estimated 90% of students What Can Admissions Officials Do? Some universities have already been privy to biased AI, and avoiding these fiascos in the future is crucial for maintaining reputation and keeping applicants interested. What proactive and preventive measures can schools take to keep students safe and be judged accurately? Many AI enthusiasts wonder if curating historical data , potentially skewing it or making the past inaccurate, would improve modern bias. Diverse and representative training alongside continuous monitoring is a more proactive approach to designing bias-free AI for admissions. This includes input from inclusive, interdisciplinary populations and datasets to fill gaps in socioeconomic, ethnic, educational and demographic experiences. Incorporating bias detection and mitigation tools will complement these efforts. curating historical data AI professionals should help university leaders become literate in AI bias. They can offer workshops for noticing anomalies and providing feedback for further training. This helps if the admissions AI has integrated transparency, forcing it to explain where it derived its decisions. These insights are roadmaps for all AI users because they trace the source of problematic and biased information. Universities can also take a mixed approach to admissions, including human interactivity, to judge qualitative aspects. A recent study revealed an AI model was a poor predictor of educational outcomes because of its racial biases. It was incorrect at predicting the academic success of students with Asian backgrounds 73% of the time. AI can look at numbers effectively with minimal error, but humans may be better judges when reading personal statements or conducting interviews. students with Asian backgrounds 73% students with Asian backgrounds 73% Students in the Hands of AI Removing AI bias from college admissions is a deceptively critical issue, as poor data management could disrupt many students’ bright futures. Data scientists and university leaders must prioritize this, as the next-generation workforce relies on its accuracy and integrity to give them a chance at education. Neglect will cause numerous problems for all industries that need experienced professionals walking in the door.

Addressing Bias in AI Models Used for University Admissions Decisions

Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 29 of 29. DEMAND FOR JURY TRIAL Pursuant to Rule 38(b) of the Federal Rules of Civil Procedure, Plaintiff hereby demands a trial by jury for all issues triable to a jury. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on August 05, 2024, deadline.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 29 of 29. Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 29 of 29. HackerNoon’s Legal PDF Series here DEMAND FOR JURY TRIAL Pursuant to Rule 38(b) of the Federal Rules of Civil Procedure, Plaintiff hereby demands a trial by jury for all issues triable to a jury. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 05, 2024, deadline.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 05, 2024, deadline.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. deadline.com

Musk Wants a Jury Decision in His Lawsuit Against OpenAI

Open banking has revolutionized the financial landscape, compelling traditional banks to open their data troves to third-party providers through APIs. This pivotal shift, mandated by Europe's PSD2 directive, is paving the way for a wave of innovation in areas like payments, lending, and budget management. What Is Open Banking Data? Open banking data comprises the customer information that banks share with authorized third parties. This sharing is contingent upon customer consent, which can be revoked at any time, ensuring user control over their financial data. Data Sharing in Open Banking The type of data shared includes account holder details, transaction history, and more, depending on regional regulations like Europe's PSD2 or Australia's CDR. Banks use secure APIs for data sharing, a method far safer than the older screen scraping techniques, enhancing data security and user privacy. Who Accesses Open Banking Data? Access is granted only to licensed third-party providers, such as fintech companies, which must meet rigorous standards set by local regulatory bodies like the FCA in the UK or BaFin in Germany. These providers include Payment Initiation Service Providers (PISP) and Account Information Service Providers (AISP), each authorized for specific functions within the open banking ecosystem. The Power of Open Banking for Businesses For businesses, open banking data opens up numerous possibilities: Personalization: Businesses can tailor services based on the detailed financial behaviors of customers, enhancing user experiences across industries. Verification: Speeds up income and wealth verification processes, useful in sectors like lending and insurance. Compliance: Streamlines KYC and fraud prevention measures by providing a comprehensive view of a client's financial history. Financial management: Facilitates better budget and financial management through consolidated views of finances from multiple accounts. "Open banking is more than just a regulatory requirement; it's a transformative force that empowers businesses to innovate and provide enhanced services to their customers," said the representative from Noda. "By leveraging open banking data, companies can not only streamline operations but also deliver a personalized and secure experience that today’s consumers expect. At Noda, we are committed to leading this charge, providing robust solutions that help businesses capitalize on the immense opportunities open banking presents." As businesses continue to navigate the evolving financial services landscape, open banking stands as a beacon of innovation, offering a more personalized, secure, and efficient financial ecosystem. Noda supports this innovation by providing tools like direct bank payment integrations, enhancing financial operations across the globe with reduced fees and improved security. Open banking has revolutionized the financial landscape, compelling traditional banks to open their data troves to third-party providers through APIs. This pivotal shift, mandated by Europe's PSD2 directive, is paving the way for a wave of innovation in areas like payments, lending, and budget management. What Is Open Banking Data? What Is Open Banking Data? Open banking data comprises the customer information that banks share with authorized third parties. This sharing is contingent upon customer consent, which can be revoked at any time, ensuring user control over their financial data. Data Sharing in Open Banking The type of data shared includes account holder details, transaction history, and more, depending on regional regulations like Europe's PSD2 or Australia's CDR. Banks use secure APIs for data sharing, a method far safer than the older screen scraping techniques, enhancing data security and user privacy. Who Accesses Open Banking Data? Access is granted only to licensed third-party providers, such as fintech companies, which must meet rigorous standards set by local regulatory bodies like the FCA in the UK or BaFin in Germany. These providers include Payment Initiation Service Providers (PISP) and Account Information Service Providers (AISP), each authorized for specific functions within the open banking ecosystem. The Power of Open Banking for Businesses For businesses, open banking data opens up numerous possibilities: Personalization: Businesses can tailor services based on the detailed financial behaviors of customers, enhancing user experiences across industries. Personalization : Businesses can tailor services based on the detailed financial behaviors of customers, enhancing user experiences across industries. Personalization Verification: Speeds up income and wealth verification processes, useful in sectors like lending and insurance. Verification : Speeds up income and wealth verification processes, useful in sectors like lending and insurance. Verification Compliance: Streamlines KYC and fraud prevention measures by providing a comprehensive view of a client's financial history. Compliance : Streamlines KYC and fraud prevention measures by providing a comprehensive view of a client's financial history. Compliance Financial management: Facilitates better budget and financial management through consolidated views of finances from multiple accounts. Financial management: Facilitates better budget and financial management through consolidated views of finances from multiple accounts. Financial management : Facilitates better budget and financial management through consolidated views of finances from multiple accounts. Financial management "Open banking is more than just a regulatory requirement; it's a transformative force that empowers businesses to innovate and provide enhanced services to their customers," said the representative from Noda. "By leveraging open banking data, companies can not only streamline operations but also deliver a personalized and secure experience that today’s consumers expect. At Noda, we are committed to leading this charge, providing robust solutions that help businesses capitalize on the immense opportunities open banking presents." "Open banking is more than just a regulatory requirement; it's a transformative force that empowers businesses to innovate and provide enhanced services to their customers," said the representative from Noda. "By leveraging open banking data, companies can not only streamline operations but also deliver a personalized and secure experience that today’s consumers expect. At Noda, we are committed to leading this charge, providing robust solutions that help businesses capitalize on the immense opportunities open banking presents." As businesses continue to navigate the evolving financial services landscape, open banking stands as a beacon of innovation, offering a more personalized, secure, and efficient financial ecosystem. Noda supports this innovation by providing tools like direct bank payment integrations, enhancing financial operations across the globe with reduced fees and improved security. Noda

From Data to Decisions: Harnessing Open Banking for Enhanced Business Performance

ডেটা থেকে সিদ্ধান্ত পর্যন্ত: উন্নত ব্যবসায়িক কর্মক্ষমতার জন্য ওপেন ব্যাঙ্কিং ব্যবহার করা

Von Daten zu Entscheidungen: Open Banking zur Verbesserung der Geschäftsleistung nutzen

De los datos a las decisiones: aprovechar la banca abierta para mejorar el rendimiento empresarial

Des données aux décisions : exploiter l’Open Banking pour améliorer les performances des entreprises

डेटा से लेकर निर्णय तक: बेहतर व्यावसायिक प्रदर्शन के लिए ओपन बैंकिंग का उपयोग

データから意思決定へ: オープン バンキングを活用してビジネス パフォーマンスを向上

데이터에서 의사결정까지: 비즈니스 성과 향상을 위해 오픈 뱅킹 활용

Dos dados às decisões: aproveitando o Open Banking para melhorar o desempenho dos negócios

От данных к решениям: использование открытого банкинга для повышения эффективности бизнеса

Verilerden Kararlara: Gelişmiş İş Performansı için Açık Bankacılıktan Yararlanma

Từ dữ liệu đến quyết định: Khai thác ngân hàng mở để nâng cao hiệu quả kinh doanh

从数据到决策：利用开放银行提高业务绩效

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