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Counterfactual Explanations
Counterfactual explanations answer 'What if X had not happened, what would the outcome have been?'. They identify the smallest change to an input that would alter a model's prediction, offering actionable insights for users. Unlike feature importance, which shows what contributed to the current decision, counterfactuals show how to achieve a desired alternative.
In plain terms
It's like figuring out what small detour you needed to take to avoid traffic, rather than just knowing where the current traffic jam is.
Why it matters
They provide human-understandable, actionable recourse for individuals affected by AI decisions, fostering trust and accountability.
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