Artificial Intelligence is becoming a part of the way we make decisions all around the world. The problem is that we do not really understand how Artificial Intelligence makes these decisions. This has created an issue where nobody is really responsible for what Artificial Intelligence does. This paper is going to look closely at the ethical issues that come up when we use Artificial Intelligence and other systems that can work on their own. We will be focusing on the problem of making Artificial Intelligence work quickly and efficiently while also making sure that it does what is right and fair. Artificial Intelligence has to be able to make decisions but it also has to be accountable, for what it does. The Black Box problem is central to this study. This problem is about the Large Language Models and the way they make decisions. These Large Language Models are very complicated. It is hard to understand why they make certain decisions. This is an issue in important areas, like healthcare and criminal justice and financial services. The Large Language Models are used to make decisions in these areas but it is not clear why they make these decisions. [3] This study looks at what has been written about "Fairness, Accountability and Transparency" frameworks far. It also suggests a way to check if these frameworks are ethical. We think that algorithms can be biased because the data used to train them reflects the prejudices of the past. The study also looks at the problem of trying to make predictions that're both accurate and fair to all people. It shows that trying to make predictions accurate can mean that some groups of people are treated unfairly. The idea that math can be completely neutral is not true because the data it is based on is not neutral. This research evaluates how well Fairness, Accountability and Transparency" frameworks are working. [1] One big problem with Artificial Intelligence is that it can be unfair. This happens when the data we use to train Artificial Intelligence is not fair. For example, if the data is mostly one type of person Artificial Intelligence might not work well for other types of people. To fix this we use something called "Fairness by Design". We make sure that the data is fair before we use it. We also add rules to the Artificial Intelligence system to make sure it is fair. We check the results to make sure they are fair. Artificial Intelligence systems also need to be accountable. This means that people need to be involved in the decision-making process. We call this "Human-in-the-loop". Artificial Intelligence can process a lot of data. People need to make sure that the choices it makes are good. As Artificial Intelligence gets better we want to make sure that it works well with people. We need to balance how well Artificial Intelligence works with how fair it's. We want Artificial Intelligence to make choices that are good for everyone. We need to keep working on this to make sure that Artificial Intelligence is good for society. Artificial Intelligence has to make choices that're good for people and we need to be able to trust it. We are trying to make Artificial Intelligence that's fair and good and that works well with people. This is a challenge but it is very important. We need to make sure that Artificial Intelligence is used in a way that's good, for everyone and that it does not hurt anyone [13].
Artificial Intelligence, Ethics, Machine Learning, Algorithmic Bias, Transparency, Accountability, Deontological Frameworks, Algorithmic Bias (or Bias Mitigation), Explainable AI (XAI), Transparency and Traceability, Robustness and Reliability
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