We'll be @ ICLR!

Poster: Sat 26 Apr 10AM — 12:30PM SGT

Paper: tinyurl.com/2deek4wx
Code: tinyurl.com/2rb6zc28

We develop methods to compute responsiveness scores for any dataset and models. Three main advantages:
1. Can be swapped in place of existing methods
2. Highlight responsive features
3. Flag instances where such features don't exist!

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)

Many countries seek to protect consumers in applications like lending and hiring by requiring explanations for adverse outcomes. But,
- Many provide companies with substantial flexibility
- Standard approach is to use methods like SHAP and LIME to highlight important features