arXiv AI By Mattia Cerrato, Otto Sahlgren, Xenia Heilmann

Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse

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arXiv Machine Learning
Sep 14

Explanations-Driven Active Feature Acquisition for Algorithmic Recourse

The paper introduces Explanation-Driven Feature Acquisition (EDFA), a method that jointly optimizes algorithmic recourse and feature acquisition by selecting features based on explanatory value per unit cost. Using Markov Blanket theory, EDFA unifies various explanation types and provides distribution‑free validity guarantees for recourse derived from partial information. Experiments on seven datasets show that EDFA requires fewer features than existing active feature acquisition baselines while maintaining accuracy and producing more actionable recourse.

By Vinura Galwaduge, Jagath Samarabandu
arXiv AI
Sep 25

The Gold in Bias: Maturing the AI Design Process through Verification

The paper proposes rethinking bias in AI as a diagnostic tool rather than merely a flaw to be minimized. It introduces a multidimensional framework that examines bias across origin, lifecycle emergence, technical causes, and validation methods, covering 30 bias types, 16 verification methods, and 20 countermeasures for both traditional and generative AI. The authors present a hierarchical evidence framework distinguishing internal and external validity, and advocate for Ethics by Design principles to embed bias verification throughout the AI development lifecycle.

By Samira Maghool, Paolo Ceravolo