How can AI interpretability address AI bias?
Bias in AI systems often stems from imbalances or problematic patterns within the training data, leading to unfair or discriminatory outcomes. By making AI models more interpretable, we can illuminate the pathways through which such biases are learned and propagated. This transparency allows us to identify and rectify biased decision-making processes, moving towards more equitable AI applications. Understanding the model's reasoning is a prerequisite for addressing its flaws.
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