Today, we live in a world where technology is advancing . In recent years, one of the most, if not the most, has been closely connected with , which has already become an integral part of many areas of our lives. AI is a field of computer science that develops algorithms and systems that can perform tasks that require human intelligence. at an unprecedented rate in-demand fields in science and technology Artificial Intelligence (AI) In this article, I’ll find that out. But is AI always responsible for its answers, advice, and guidance? Is its undeniable brilliance balanced with a sense of accountability for its actions? The Most Common AI Problems It is no secret that AI is still in the very early stages of its formation, and in this respect, it is . Although AI is developing by leaps and bounds, there is . I strongly believe that there are putting a spoke in the wheel of AI. Once all of these problems are successfully solved, AI will undoubtedly reach a never-before-seen level. doomed to face various kinds of problems plenty of room for its improvement 5 major issues So, the 5 principal problems related to AI are: AI Bias; AI Hallucination; Privacy and Confidentiality; Data Usage and Transparency; Replacing People. 1. AI Bias It’s common knowledge that every AI model is trained on . On the surface, can be seen in the fact that many analytical systems based on deep learning have an unexpected tendency to draw “biased” conclusions, which can then lead to flawed decisions on their basis. a specific data set determined by the developers AI bias Obviously, an AI model is going to replicate the biases of the data it has been trained on. Let’s dive into several real-world examples of AI bias to better understand this issue: In , IBM, an internationally-recognized tech giant, stopped selling its facial recognition software due to that the company’s AI product was continuously showing. , another major player in the tech market, Microsoft, also discontinued the selling of its AI-based facial-analysis software tools, which . There is no doubt that the crux of the facial recognition problem is , seemingly lacking racial diversity, on which IBM’s and Microsoft’s AI models were trained. Facial Recognition. June 2020 the racial profiling In June 2022 “performed poorly on subjects with darker skin” the undisclosed set of faces , the Credit Card issued by Goldman Sachs, a leading global banking firm, was heavily criticized by the international community for having . Even though Goldman Sachs claimed that its AI-based credit score algorithm was vetted for potential bias by a third party, there were numerous examples of the algorithm being . For instance, tweeted that his Apple Card credit limit turned out to be 20 times higher than his wife’s, in spite of her having a higher credit score. Bank Credit Score Calculation. In November 2019 Apple gender bias misogynistic an entrepreneur David Heinemeier Hansson , it was revealed that Amazon’s AI-based recruiting tool was , and if one of the two job candidates, who were completely identical professionally, was male, Amazon’s algorithm would rate him higher than a competing female. Recruiting. In October 2018 gender-biased , the Autocomplete algorithm was accused of being . In this way, the algorithm proposed offensive autocomplete suggestions for queries related to Jews, Muslims, and people of color. Autocomplete. In February 2018 Google biased against race, religion, and gender Thus, AI bias is the problem that machine learning algorithms can be subject to biases and distortions that can lead to . As I have described, bias can manifest itself in a variety of ways, including the selection of data used to train the model, the use of certain algorithms and data processing techniques, as well as the training of the model based on certain biases or stereotypes that exist in society. unfair and inaccurate results 2. AI Hallucination Another that hinders the development of AI is so-called . It is the phenomenon when an AI model gives a confident response that . acute problem AI hallucination doesn’t seem to be justified by the training data Over the last two decades, there have been related to incidents involving users who were directly following the routes suggested by navigation software and ended up being : , Lauren Rosenberg was run over by a driver after Google Maps’ walking directions instructed her to walk along a busy highway with no sidewalk. Additionally, , an article was published, covering the cases of drivers blindly following their GPS and riding up to a cliff’s edge or getting stuck in a cherry tree. hundreds of legal cases seriously injured in 2010 in 2015 Given the fact that AI is currently trending all around the world, we can expect to be added to this selection in the near future. In the meantime, AI is rapidly conquering such crucial life areas as . So, it’s safe to say that soon, we won’t even pay enough attention to such incidents because they will become . a new wave of incidents Healthcare, Transportation, Jurisdiction, Warfare, and the list goes on a routine part of every news briefing However, , OpenAI, the creator of ChatGPT, was for the first time since the groundbreaking AI chatbot was released. for one of his articles and asked ChatGPT for a summary of a Washington federal court case. As a result, the AI-powered advisor responded with the information regarding Mark Walters, an American radio host, claiming that Walters was involved in “defrauding and embezzling funds” coming from the Second Amendment Foundation. Needless to say, Walters . less than a month ago sued for defamation A journalist named Fred Riehl was doing research neither worked for the stated nonprofit organization nor received any money from it Such AI hallucinations seem to be . Imagine that just one response generated by an AI model . On the other hand, a wrongly compiled route can serve as . Apparently, the algorithm itself can’t be held responsible for the answers it gives. But its creators can and should. a very dangerous and horrendous thing can ruin the entire professional credibility of an innocent specialist the cause of someone’s death 3. Privacy and Confidentiality A further matter of concern I want to talk about is . Unfortunately, when it comes to implementing AI and using third-party services, even the world’s largest corporations aren’t usually aware of how their valuable and sensitive data is being processed and stored. Ultimately, this , as a rule, results in the . That’s exactly what happened to Samsung. privacy along with confidentiality reckless use of AI inadvertent exposure of the company’s precious information and priceless insights , three Samsung employees . The first one inserted top-secret source code to check for errors; the second one leaked another piece of code while trying to tweak it; and the third one shared the recording of an important internal meeting aiming to convert it into notes. It goes without saying that Samsung immediately banned all of its staff from using any AI-powered tools, but this fact , which didn’t even consider the possibility of such leaks prior to this case, , who reached the pinnacle of incompetence by acting so irresponsibly. In the spring of 2023 unintentionally leaked confidential information while using ChatGPT doesn’t take all the responsibility away from both the company and from the employees Nevertheless, there is related to one’s privacy and confidentiality that has been pointed out by one of the most prominent investors in AI, : a more frightening issue Marc Andreessen “ Every kid is going to grow up now with a friend that's a bot. That bot’s going to be with them their whole lives. It's going to have a memory, it's going to know all their private conversations, it's going to know everything about them”. If the above-mentioned scenario comes true, it would definitely be the worst feeling in the world for someone to learn that all of their personal data/thoughts/secrets accumulated throughout their entire life have been leaked. In the worst case, negligence towards privacy and confidentiality might lead to an uptick in mental health problems, and sadly, some people may even make choices that harm themselves. 4. Data Usage and Transparency The fourth major issue I would like to address in this article is . It’s no coincidence that some of the world’s leading LLM (Large Language Models) providers, including OpenAI, tend , therefore trying to hide the ways they collected the necessary training data. data usage and transparency not to completely disclose their original datasets Nowadays, it’s widely believed that most of the datasets used to train the most popular AI models were collected . For example, , Getty Images, the owner of 477 million images, sued Stability AI, an AI-based art generator, claiming that the latter had illegally used 12 million copyrighted pictures belonging to Getty Images. One of the examples that Getty Images attached to the case is quite illustrative: not only do the images , but the generated picture also contains . In this case, . without receiving appropriate permissions from the data owners in February 2023 look like two peas the outline of the watermark used by Getty Images opyright infringement is more than obvious c If you think that such violations are totally unacceptable, I ought to warn you that . In our severe reality, companies use manual labor to label the required data. , an external data labeling provider of OpenAI hired local workers to label toxic content generated by ChatGPT for only $2 per hour. If these people worked 10 hours a day for a month with no days off, they would only receive $600 a month, literally sacrificing their mental and physical health. No wonder that some of the workers interviewed described this experience as . this is just the tip of the iceberg In Kenya “torture” 5. Replacing People The last but certainly not the least concern about AI is . Today, many people fear that AI , putting us, humans, out of work. Actually, it’s one of of implementing AI that we permanently talk about. This fear has been supported by the World Economic Forum which predicted that . But in fact, that AI in areas such as Data Processing, ML Engineering, Cybersecurity, etc. In this regard, some of us can breathe a sigh of relief. its great potential to replace humans will automate millions of jobs the biggest effect s AI will take away about 85 million jobs by 2025 the World Economic Forum also forecast will create 97 million job s was conducted by Princeton University and outlined to be affected by AI, and especially the LLM. And while having telemarketers at the top of the list doesn't sound dramatic to many, they are followed on the list by teachers. This raises of how knowledge workers around the world will be affected by AI. Another study the key professions the burning and open question To delve deeper into this issue, I recommend reading published by Ethan Ilzetzki and Suryaansh Jain, where they refer to carried out by Daron Acemoglu and Pascual Retsrepo. The main point of these studies is that we can basically stick to a certain framework when considering the impact AI has on humans. This framework consists of three components: a recent article a fundamental study a displacement effect; a productivity effect; a reinstatement effect. Solutions to the Problems So far in this article, I have only written about the problems, and now it’s time to propose that will assist AI in reaching . multiple solutions the next peak in its development First and foremost, should be adopted to set the basic principles and standards for the establishment and development of an AI model, as it was done concerning the regulation of personal data collected after s. I’m glad that , and have recently signed calling for AI regulations. an industry-wide regulation the Facebook and Cambridge Analytica case the first steps in this direction are already being taken the AI “founding fathers” (Geoffrey Hinton, Yoshua Bengio, Sam Altman, Ilya Sutskever, etc.) a joint statement I strongly believe that these regulations should However, the companies shouldn’t wait for the regulation to come into force. Instead, they need in terms of implementing internal AI testing and . prioritize transparency, accountability, and fairness and should describe how the data is collected, as well as how the models are trained, used, and managed. to be proactive monitoring processes to minimize AI bias Another important aspect of the possible solution is simply , and so is the role of manual labor. , Wassily Leontief, a Nobel Prize laureate, and NYU professor, already foresaw that . In this context, it’s crucial for us to understand how this displacement is executed and how it can affect our lives in general. an acceptance of the fact that the world around us is inevitably evolving Back in 1973 “more and more works will be replaced by machines” I am personally convinced that . Corporations, businesses, and governments, on their part, are , alongside , to aid the displaced workforce in finding brand-new meaningful, and well-paid jobs. the displacement should be sustainable enough to take into account the long-term impact on society to provide all necessary support measures employment and training services Conclusion Thus, a vital part of both using and building AI models is . You, as a normal user, should always remember ; and AI model providers should have the courage . We are still associated with such a rapid introduction of AI into our lives. I don't want to sound conservative by any means, but I do believe that to treat these processes with complete responsibility not to trust AI with your personal or vulnerable data to assume total responsibility for the responses and advice of their brainchildren not fully aware of all the risks before we entrust AI with our lives, we have to find that proverbial balance between innovation and responsibility.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Ethics in AI: Balancing Innovation and Responsibility

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