RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model

Understand

Counterfactual (CF) explanations for machine learning (ML) models are preferred by end-users, as they explain the predictions of ML models by providing a recourse (or contrastive) case to individuals who are adversely impacted by predicted outcomes.

• Existing CF explanation methods generate recourses under the assumption that the underlying target ML model remains stationary over time.
• However, due to commonly occurring distributional shifts in training data, ML models constantly get updated in practice, which might render previously generated recourses invalid and diminish end-users trust in our algorithmic framework.
• To address this problem, we propose RoCourseNet, a training framework that jointly optimizes predictions and recourses that are robust to future data shifts.