arXiv AI
Algorithmic Recourse Under Competition
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Incentives to Offer Algorithmic Recourse
The paper investigates why decision-makers such as banks and employers might provide algorithmic recourse to applicants rejected by automated systems. In a screening model where recourse improves an applicant’s value but costs vary across individuals, the optimal policy is a threshold rule: reject low‑scoring applicants, offer recourse to those with intermediate scores, and accept high‑scoring applicants outright. This strategy creates a new acceptance path for some marginal applicants while imposing a costly hurdle on others who would otherwise be accepted.
arXiv Machine Learning
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Diverse and Plausible Algorithmic Recourse via Tractable Recourse Distributions
arXiv:2608. 04677v1 Announce Type: new Abstract: Algorithmic recourse seeks to help individuals reverse unfavorable automated decisions by recommending actionable changes that achieve a desired outcome.
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arXiv:2607. 28497v1 Announce Type: new Abstract: Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, such as loan and mortgage applications.
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Target-confidence Recourse Using tSeTlin machines: TRUST
arXiv:2606. 18832v1 Announce Type: cross Abstract: Counterfactual explanations are widely used to provide algorithmic recourse in high-stakes decision-making systems.
Hugging Face Trending Papers
Jun 17
Target-confidence Recourse Using tSeTlin machines: TRUST
Counterfactual explanations are widely used to provide algorithmic recourse in high-stakes decision-making systems. Most existing methods seek the smallest change to an input that flips a model's decision.
arXiv Machine Learning
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Explanations-Driven Active Feature Acquisition for Algorithmic Recourse
The paper introduces Explanation-Driven Feature Acquisition (EDFA), a method that jointly optimizes algorithmic recourse and feature acquisition by selecting features based on explanatory value per unit cost. Using Markov Blanket theory, EDFA unifies various explanation types and provides distribution‑free validity guarantees for recourse derived from partial information. Experiments on seven datasets show that EDFA requires fewer features than existing active feature acquisition baselines while maintaining accuracy and producing more actionable recourse.