Selection Bias

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Actionable Recourse in Competitive Environments: A Dynamic Game of Endogenous Selection
Mar 18, 2026Ya-Ting Yang, Quanyan ZhuAlgorithmic Recourse Selection Bias
Department of Electrical and Computer Engineering, New York University, NY, 11201, USA
Can Blindfolded LLMs Still Trade? An Anonymization-First Framework for Portfolio Optimization
Mar 18, 2026Joohyoung Jeon, Hongchul LeeAlgorithmic Trading LLM Agent Reliability
Korea University · Mirae Asset Securities
No evaluation without fair representation : Impact of label and selection bias on the evaluation, performance and mitigation of classification models
Mar 10, 2026Magali Legast, Toon Calders, François FoussAlgorithmic Fairness Debiased ML
Université catholique de Louvain, Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Louvain-la-Neuve, Belgium · Universiteit Antwerpen, Adrem Data Lab, Antwerpen, Belgium · Université catholique de Louvain, Louvain Research Institute in Management and Organizations, Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Mons, Belgium
Feature-Weighted Maximum Representative Subsampling
Mar 1, 2026Tony Hauptmann, Stefan KramerDebiased ML Selection Bias
Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany
Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget
Feb 19, 2026Michael O. Harding, Vikas Singh, Kirthevasan KandasamyMinimax Estimation Selection Bias
Department of Statistics, University of Wisconsin-Madison · Department of Biostatistics, University of Wisconsin-Madison · Department of Computer Sciences, University of Wisconsin-Madison
Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases
Apr 6, 2025Alkis Kalavasis, Anay Mehrotra, Felix ZhouLinear Regression Stochastic Optimization Convergence
Yale University
Learning from a Biased Sample
Sep 5, 2022Roshni Sahoo, Lihua Lei, Stefan WagerDistribution Shift Robustness Distributionally Robust Optimization
Stanford University

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Actionable Recourse in Competitive Environments: A Dynamic Game of Endogenous Selection

Ya-Ting Yang, Quanyan Zhu

Department of Electrical and Computer Engineering, New York University, NY, 11201, USA

Algorithmic Recourse Selection Bias

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Actionable recourse studies whether individuals can modify feasible features to overturn unfavorable outcomes produced by AI-assisted decision-support systems. However, many such systems operate in competitive settings, such as admission or hiring, where only a fraction of candidates can succeed. A fundamental question arises: what happens when actionable recourse is available to everyone in a competitive environment? This study proposes a framework that models recourse as a strategic interaction among candidates under a risk-based selection rule. Rejected individuals exert effort to improve actionable features along directions implied by the decision rule, while the success benchmark evolves endogenously as many candidates adjust simultaneously. This creates endogenous selection, in which both the decision rule and the selection threshold are determined by the population's current feature state. This interaction generates a closed-loop dynamical system linking candidate selection and strategic recourse. We show that the initially selected candidates determine both the benchmark of success and the direction of improvement, thereby amplifying initial disparities and producing persistent performance gaps across the population.