Counterfactual Explanations

Scorecards look objective until a user reads the explanation Scorecards are comforting. They turn a messy judgment into a neat row of numbers: sparsity, proximity, plausibility, trust score, completeness. The model team can rank explanation methods. The governance team can file the validation report. The product team can say the system is explainable. Everyone gets to leave the meeting before dinner. ...

Rejected. That is where counterfactual explanations usually enter the story. A loan applicant is declined by an automated system. A hiring candidate is filtered out. An insurance customer is priced into an unfavorable category. The counterfactual explanation is supposed to answer a practical question: what would need to change for the model to give me the desired outcome? ...