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Counterfactual Explanations
Unlike feature importance which tells you what influenced a decision, counterfactuals explain specific model predictions by showing what minimal changes to the input features would alter the outcome. For example, if a loan was denied, a counterfactual explanation might indicate, 'if your credit score was 50 points higher, the loan would have been approved.'
In plain terms
It's like getting directions to a destination by being told, 'If you had turned left at the last intersection instead of right, you'd be there now.'
Why it matters
Provides actionable insights for users to understand and potentially influence AI decisions, building trust and fairness.
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