How Data Teams Use Counterfactual Explanations
transcript
show notes
Most model explanations tell users what happened, but rarely what could have happened. In this episode, we look at how forward-thinking data teams are deploying counterfactual explanations to drive actual behavior change. We examine the specific mechanics of generating minimal feasible changes in credit and hiring models, the trade-offs between interpretability and privacy, and why telling a customer they would be approved if they earned just five thousand dollars more is often more effective than a standard feature importance chart. This is about moving from descriptive analytics to prescriptive action.
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