# Explanation Hacking: The perils of algorithmic recourse

arXiv: https://arxiv.org/abs/2406.11843 · HTML: https://mlsanity.com/paper/2406.11843/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning](https://mlsanity.com/paper/2409.03632/) (2024)
- [SIDEs: Separating Idealization from Deceptive Explanations in xAI](https://mlsanity.com/paper/2404.16534/) (2024)
- [Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants](https://mlsanity.com/paper/2508.08337/) (2025)

## 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.