ElliCE: Efficient and Provably Robust Algorithmic Recourse via the Rashomon Sets

Bohdan Turbal, Iryna Voitsitska, Lesia Semenova

Keywords: Algorithmic recourse, Rashomon set, Model multiplicity, Counterfactual explanations, Robustness

TL;DR: ElliCE generates counterfactual explanations with theoretical guarantees that remain valid across Rashomon set by approximating it with an ellipsoid. The method is faster than existing robust baselines and ensures meaningful explanations.

Abstract:

Machine learning models now influence decisions that directly affect people’s lives, making it important to understand not only their predictions, but also how individuals could act to obtain better results. Algorithmic recourse provides actionable input modifications to achieve more favorable outcomes, typically relying on counterfactual explanations to suggest such changes.
However, when the Rashomon set - the set of near-optimal models - is large, standard counterfactual explanations can become unreliable, as a recourse action valid for one model may fail under another.
We introduce ElliCE, a novel framework for robust algorithmic recourse that optimizes counterfactuals over an ellipsoidal approximation of the Rashomon set.
The resulting explanations are provably valid over this ellipsoid, with theoretical guarantees
on uniqueness, stability, and alignment with key feature directions.
Empirically, ElliCE generates counterfactuals that are not only more robust but also more flexible, adapting to user-specified features constraints while being substantially faster than existing baselines.
This provides a principled and practical solution for reliable recourse under model uncertainty, ensuring stable recommendations for users even as models evolve.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 23256