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**Question 1:** Discuss the potential biases in AI algorithms and provide examples of how these biases can lead to unfair or discriminatory outcomes. AI algorithms can be biased due to biased training data, biased algorithm design, or biased interpretation of results. These biases can lead to unfair or discriminatory outcomes in areas such as hiring, lending, and criminal justice. For example, a facial recognition system trained on a dataset that is predominantly white may perform poorly on individuals with darker skin tones. To address these biases, it is important to use diverse and representative training data, carefully design algorithms to avoid bias, and regularly audit AI systems for fairness.
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