AI systems can exhibit biases and unintended consequences from "summary" of Artificial Intelligence by Melanie Mitchell
AI systems, while capable of remarkable feats, are not immune to flaws. One critical issue that has come to light in recent years is the predisposition of AI systems to exhibit biases. These biases can stem from various sources, such as the data used to train the system or the algorithms themselves. When AI systems are trained on data that is biased in some way, they are likely to perpetuate and even amplify those biases in their decision-making processes.
For example, if an AI system is trained on historical data that reflects societal biases, such as racial or gender biases, it may learn and replicate those biases in its predictions or recommendations. This can have serious real-world consequences, such as perpetuating discrimination in hiring practices or lending decisions. Furthermore, biases in AI systems can be difficult to detect and mitigate, as they are often insidious and ingrained in the system's underlying mechan...
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