Counterfactual Explanations: How to Change a Model's Mind
A counterfactual explanation finds the smallest input change that flips a model's prediction. It's used to give actionable feedback, like telling a user what to change to get a loan approved.
A counterfactual explanation is a 'what-if' scenario for a model's prediction, finding the smallest change to inputs that alters the outcome. It answers, 'What's the minimum I need to change to get a different result?' This is crucial for user-facing systems where you need to provide actionable recourse, like explaining a loan rejection. The footgun: Don't mistake the model's logic for reality; it reveals what the model thinks is important, which may be a spurious correlation.
Read the original → christophm.github.io
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- #xai
- #explainability
- #model-agnostic
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