AI systems often operate as "black boxes," where the decision-making process is opaque. This lack of transparency makes it difficult to assess fairness and identify biases. Overcoming this challenge requires the development of explainable AI (XAI) techniques that make the workings of AI models understandable to humans, facilitating scrutiny and accountability.

AI systems often operate as "black boxes," where the decision-making process is opaque. This lack of transparency makes it difficult to assess fairness and identify biases. Overcoming this challenge requires the development of explainable AI (XAI) techniques that make the workings of AI models understandable to humans, facilitating scrutiny and accountability.

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