Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
Machine learning systems increasingly make life-changing decisions about individuals, such as loan approvals, hiring, and cheating detection, raising a pressing question: how can individuals respond to negative decisions made by these opaque systems? While explainable artificial intelligence (XAI) has largely focused on algorithmic recourse -- helping individuals change their features to obtain a desired outcome -- the parallel problem of algorithmic contestability -- helping individuals review and correct erroneous algorithmic decisions -- has received far less attention, despite its central ethical and legal importance. We trace this neglect to the absence of clear formal definitions an...
| Authors | Timo Freiesleben, Kristof Meding, Gunnar König |
|---|---|
| Primary category | stat.ML |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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