Explanation Hacking: The perils of algorithmic recourse
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Abstract (arXiv)
We argue that the trend toward providing users with feasible and actionable explanations of AI decisions, known as recourse explanations, comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we advocate that explanations of AI decisions should aim at understanding.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Mar 2024.
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2025Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants10 Aug 2025
- 2024Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning5 Sep 2024
- 2024SIDEs: Separating Idealization from Deceptive Explanations in xAI25 Apr 2024