Current approaches are unable to inform consumers when:
1. features are not responsive
2. features are not monotonically responsive (e.g., can't increase income "too much")
3. features must change in counterintuitive ways (e.g., decrease income) to obtain the desired prediction

But, SHAP highlights features that are:
1. Immutable: HistoryOfLatePayment
2. Mutable but not actionable: Age, NumberOfDependents
3. Actionable but not responsive: CreditUtilization

Hence, we designed responsiveness scores to highlight features that are actionable and responsive (i.e., lead to desired prediction when changed)

Denied a loan, an interview, or an insurance claim by machine learning models? You may be entitled to a list of reasons.

In our latest w @anniewernerfelt.bsky.social @berkustun.bsky.social @friedler.net, we show how existing explanation frameworks fail and present an alternative for recourse