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.
By Matthew Olckers, Toby Walsh
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.
By Anagha Sabu, Hrithik Suresh, Narayanan C. Krishnan
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.
By Srikanth Avasarala, Varun Gupta, Shahin Jabbari, Saber Salehkaleybar, Juba Ziani
arXiv:2606. 18832v1 Announce Type: cross Abstract: Counterfactual explanations are widely used to provide algorithmic recourse in high-stakes decision-making systems.
By K. Darshana Abeyrathna, Sara El Mekkaoui, Nils Enric Canut Taugb{\o}l, Anuja Vats
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.
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.
By Vinura Galwaduge, Jagath Samarabandu
arXiv:2606. 30932v1 Announce Type: new Abstract: Two-sided marketplaces connect distinct user groups whose interests often conflict -- improving outcomes on one side could degrade the other side's experience.
By Yufei Wu, Zhen Yan
arXiv:2605. 05882v2 Announce Type: replace-cross Abstract: Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age.
By Filip Edstr\"om, Guilherme W. F. Barros, Tetiana Gorbach, Xavier de Luna
arXiv:2402. 01811v2 Announce Type: replace Abstract: Credit scoring has been catalogued by the European Commission and the Executive Office of the US President as a high-risk classification task, in light of the potential harms of making loan approval decisions based on models that would be biased against certain groups.
By Pablo Casas, Huan Yu, Christophe Mues
The paper presents a new scaling law for reward optimization in AI alignment, showing that performance scales as Θ(√min{log(M), K}), where M is the number of preference comparisons used to train a proxy reward model and K is the KL‑divergence budget relative to a reference policy. The authors derive this law using an information‑theoretic model, prove its tightness, and validate it with extensive experiments involving a 70B gold reward model and smaller proxy models (0.6B–4B). The empirical results demonstrate a strong fit (R² 97–99 %) across different model sizes, noise levels, and optimization methods, suggesting that reward optimization behaves like a simple selection task over IID Gaussian variables with noisy feedback.
By Ali Aouad, Aymane El Gadarri, Vivek F. Farias
arXiv:2606. 08696v1 Announce Type: cross Abstract: Counterfactual recourse aims to provide actionable feature changes that would alter an unfavorable decision made by a predictive model.
By Yasuo Tabei
arXiv:2606. 16113v1 Announce Type: new Abstract: Algorithmic recourse methods provide counterfactual explanations that inform individuals of the actions required to overturn an unfavorable model decision.
By Zahra Khotanlou, Hashir Ahmed, Chenghao Tan, Ahmed Abdelaal, Amir-Hossein Karimi