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  1. Explanation Hacking: The perils of algorithmic recourse.E. Sullivan & Atoosa Kasirzadeh - forthcoming - In Juan Manuel Durán & Giorgia Pozzi, Philosophy of science for machine learning: Core issues and new perspectives. Springer.
    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.
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  • Understanding and how-possibly explanations: Why can’t they be friends?Philippe Verreault-Julien & Till Grüne-Yanoff - 2025 - Philosophical Studies 182 (1):355-368.
    In the current debate on the relation between how-possibly explanations (HPEs) and understanding, two seemingly irreconcilable positions have emerged, which either deny or assert HPEs’ contribution to understanding. We argue, in contrast, that there is substantial room for reconciliation between these positions. First, we show that a shared assumption is unfounded: HPEs can be interpreted as being correct explanations. Second, we argue that what we call the standard account is actually compatible with the claim that HPEs may improve understanding. Our (...)
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