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Paper questions counterfactual explanations in AI for justification and recourse

By PulseAugur Editorial · [1 sources] ·

A new paper explores the limitations of counterfactual explanations (CEs) in explainable AI, particularly when used for justification and recourse. The research highlights that CEs can obscure crucial design and governance choices made throughout the machine learning pipeline, such as feature selection, business requirements, and model validation metrics. Empirical experiments demonstrate that these upstream decisions significantly influence the generated counterfactuals, suggesting that CEs alone do not fully answer critical "why" questions regarding decisions. AI

IMPACT Highlights potential shortcomings in AI explainability methods, suggesting a need for more comprehensive approaches to justification and recourse.

RANK_REASON The cluster contains a single academic paper discussing limitations of a specific AI technique. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Paper questions counterfactual explanations in AI for justification and recourse

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The cluster contains a single academic paper discussing limitations of a specific AI technique. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mattia Cerrato, Otto Sahlgren, Xenia Heilmann ·

    Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse

    arXiv:2608.30956v1 Announce Type: cross Abstract: Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input features were manipulated. This technique is used for a range of tasks such as d…

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