Best Paper Award at AIES 2026 for Work on Algorithmic Contestability

The paper “Explainable AI Isn’t Enough! Rethinking Algorithmic Contestability” received the Best Paper Award at the Ninth AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026). The paper was written by MCML Junior Member Timo Freiesleben, Kristof Meding, and Gunnar König.

Machine learning systems increasingly make life-changing decisions about individuals, for example in loan approvals or hiring. Explainable AI research has so far focused mainly on recourse, which helps individuals change their situation to obtain a better outcome. Much less attention has been paid to contestability, which helps individuals challenge and correct decisions that may be wrong. The authors propose an operational definition of contestability and show that standard explanation methods, such as counterfactuals, LIME, or Anchors, are not sufficient to overturn a decision. Instead, they identify three types of evidence that can justify reversing a decision: predictive multiplicity, incorrect feature values, and neglected evidence that should have overruled the decision. They also analyze how existing EU legislation already grants individuals some rights to such evidence.

Congratulations to the authors on this recognition!