Algorithmic Recourse

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 19

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Algorithmic Recourse Under Competition
Sep 30, 2026Shahin JabbariAlgorithmic Recourse
Drexel University
The Signed Geometry of One-Shot Recourse: On-Path Validity and the Signed-Curvature Criterion
Sep 28, 2026Hazar YuekselAlgorithmic Recourse
Google
Explanations-Driven Active Feature Acquisition for Algorithmic Recourse
Sep 14, 2026Vinura Galwaduge, Jagath SamarabanduCounterfactual Explanations Cost-Aware Inference
Dept. of Electrical and Computer Engineering Western University London, ON, Canada
Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse
Aug 31, 2026Mattia Cerrato, Otto Sahlgren, Xenia HeilmannCounterfactual Explanations AI Accountability
Johannes Gutenberg University Mainz, Germany · University of Tampere, Finland
Shaping Human-AI Interactions to Provide Improvement Pathways and Balance Competing Objectives
Aug 6, 2026Keziah NaggitaStrategic Classification Human-AI Interaction
TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO Chicago, Illinois
Diverse and Plausible Algorithmic Recourse via Tractable Recourse Distributions
Aug 5, 2026Anagha Sabu, Hrithik Suresh, Narayanan C. KrishnanProbabilistic Circuits Algorithmic Recourse
Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Palakkad Palakkad, Kerala, India
The Role of Causality in Algorithmic Recourse
Jul 30, 2026Srikanth Avasarala, Varun Gupta, Shahin Jabbari +2Performative Prediction Algorithmic Recourse
Georgia Institute of Technology · Vector Institute · Drexel University +1
Personalized Causal Recourse: A Human-In-The-Loop Approach
Jul 3, 2026Denise Tampieri, Giovanni De Toni, Paolo GiudiciStructural Causal Models Causal Structure Learning
The University of Edinburgh, Edinburgh, UK · ⋆Work conducted during the author’s MSc at the University of Trento, Trento, Italy. · Fondazione Bruno Kessler, Trento, Italy +1
Play Like Champions: Counterfactual Feedback Generation in Latent Space
Jun 30, 2026Andrzej Białecki, Adam Mastalerz, Han ZhouCounterfactual Learning Algorithmic Recourse
Warsaw University of Technology · Silesian University of Technology · University of British Columbia
Target-confidence Recourse Using tSeTlin machines: TRUST
Jun 17, 2026K. Darshana Abeyrathna, Sara El Mekkaoui, Nils Enric Canut Taugbøl +1Counterfactual Explanations Algorithmic Recourse
Group Research and Development Det Norske Veritas (DNV) Oslo, Norway
RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation
Jun 15, 2026Zahra Khotanlou, Hashir Ahmed, Chenghao Tan +2Benchmark Design Automated Evaluation
University of Waterloo
Agentic Search for Counterfactual Recourse under Fixed LLM Budgets
Jun 7, 2026Yasuo TabeiCounterfactual Explanations Large Language Model-Guided Optimization
RIKEN Center for Advanced Intelligence Project Tokyo, Japan
Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions
Jun 1, 2026Yifan WangConstraint Satisfaction Algorithmic Recourse
Department of Mechanical Engineering, McGill University, QC, H3A 2T7, Canada
Algorithmic Recourse of In-Context Learning for Tabular Data
May 29, 2026Wenshuo Dong, Jiaming Zhang, Shaopeng Fu +3Tabular ICL Algorithmic Recourse
King Abdullah University of Science and Technology, Saudi Arabia · Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates · University of Copenhagen, Denmark +2
When Bits Break Recourse: Counterfactual-Faithful Quantization
May 16, 2026Chaymae Yahyati, Ismail Lamaakal, Khalid El Makkaoui +1Mixed-Precision Quantization Counterfactual Evaluation
Mohammed First University
Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
May 15, 2026Timo Freiesleben, Kristof Meding, Gunnar KönigExplainable Artificial Intelligence Algorithmic Accountability
LMU Munich, Germany · Munich Center for Machine Learning (MCML) · Munich Center for Mathematical Philosophy (MCMP) +3
Causal Algorithmic Recourse: Foundations and Methods
May 12, 2026Drago Plecko, Collin Wang, Elias BareinboimCopula Models Causal Effect Estimation
Department of Statistics & Data Science UCLA Los Angeles, CA 90095, USA · Department of Computer Science Columbia University New York, NY 10027, USA
Optimal Recourse Summaries via Bi-Objective Decision Tree Learning
May 8, 2026Ioannis Chatzis, Jason Liartis, Athanasios Voulodimos +1Algorithmic Auditing Decision Tree Learning
Artificial Intelligence and Learning Systems Laboratory
Incentives to Offer Algorithmic Recourse
Jan 27, 2023Matthew Olckers, Toby WalshMechanism Design Algorithmic Recourse
Centre for Household Finance, Stellenbosch University · UNSW Sydney

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Algorithmic Recourse Under Competition

Shahin Jabbari

Drexel University

Algorithmic Recourse

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Algorithmic recourse provides individuals who have received undesirable outcomes from machine learning models with suggestions for minimum-cost improvements to achieve the desired outcome. A central assumption when computing recourse is that the decision rule remains fixed throughout the recourse implementation phase. We challenge this assumption in settings where individuals compete for limited resources. In such settings, widespread recourse implementation can change the acceptance threshold even when the scoring model that is used to evaluate individuals remains the same. This change in acceptance threshold can, in turn, invalidate the original recourse recommendations (i.e., following the recourse may not lead to the desired outcome). To address this problem, we introduce a framework called recourse under competition that jointly optimizes for recommendation recipients and the recommended score target they need to satisfy to balance the recourse cost and post-shift validity among initially rejected individuals. We develop an algorithm based on the Implicit Function Theorem and empirically analyze its performance. Experiments on synthetic and real datasets show that personalized score targets can achieve higher validity, albeit at a higher cost. In contrast, common score targets generally offer favorable cost-validity trade-offs for lower to medium validity values.

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