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New method uses explanations to guide feature acquisition for algorithmic recourse

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Explanation-Driven Feature Acquisition (EDFA) that jointly optimizes algorithmic recourse and feature acquisition. Unlike previous methods that provide explanations after features are acquired, EDFA uses explanations to guide the acquisition process. This approach leverages Markov Blanket theory to unify different types of explanations and determine how recourse improves with feature acquisition. Experiments show EDFA acquires fewer features than existing methods while maintaining comparable accuracy and yielding more actionable recourse. AI

IMPACT This research could lead to more efficient and actionable recourse in AI systems by optimizing the acquisition of necessary data.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for algorithmic recourse. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New method uses explanations to guide feature acquisition for algorithmic recourse

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The cluster contains a research paper published on arXiv detailing a new method for algorithmic recourse. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Vinura Galwaduge, Jagath Samarabandu ·

    Explanations-Driven Active Feature Acquisition for Algorithmic Recourse

    arXiv:2609.12179v1 Announce Type: new Abstract: Algorithmic recourse methods typically assume that a predictive model has access to all features of an individual. In practice, decisions are often made with partial information, because features are costly to acquire. Active featur…

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