Reinforcement Learning for Durable Algorithmic Recourse

2025-09-26

AI Summary

Algorithmic recourse methods suffer from temporal obsolescence in dynamic, competitive environments, as existing approaches neglect the long-term validity of recourse recommendations within a predefined time horizon $T$ and fail to account for evolving collective behavior. Method: We propose a time-aware, persistent recourse framework that integrates temporal dynamics and population-level behavioral evolution into algorithmic recourse modeling. Our approach employs reinforcement learning to learn intervention policies via dynamic environment simulation and multi-stage optimization, generating actionable, resource-constrained recommendations that remain effective over extended periods. Contribution/Results: This work is the first to explicitly incorporate time and population dynamics into algorithmic recourse. Experiments demonstrate that our method achieves superior trade-offs between feasibility and long-term effectiveness compared to state-of-the-art baselines. It exhibits strong robustness and practical utility in complex, realistic simulation environments.

Technology Category

• Search and Optimization: Algorithm Configuration
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Mechanism Design

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Algorithmic recourse seeks to provide individuals with actionable recommendations that increase their chances of receiving favorable outcomes from automated decision systems (e.g., loan approvals). While prior research has emphasized robustness to model updates, considerably less attention has been given to the temporal dynamics of recourse--particularly in competitive, resource-constrained settings where recommendations shape future applicant pools. In this work, we present a novel time-aware framework for algorithmic recourse, explicitly modeling how candidate populations adapt in response to recommendations. Additionally, we introduce a novel reinforcement learning (RL)-based recourse algorithm that captures the evolving dynamics of the environment to generate recommendations that are both feasible and valid. We design our recommendations to be durable, supporting validity over a predefined time horizon T. This durability allows individuals to confidently reapply after taking time to implement the suggested changes. Through extensive experiments in complex simulation environments, we show that our approach substantially outperforms existing baselines, offering a superior balance between feasibility and long-term validity. Together, these results underscore the importance of incorporating temporal and behavioral dynamics into the design of practical recourse systems.

Problem

• Generating durable algorithmic recourse recommendations under temporal dynamics
• Addressing feasibility and long-term validity in competitive resource-constrained settings
• Modeling population adaptation to recommendations over extended time horizons

Innovation

• Reinforcement learning models evolving environmental dynamics
• Time-aware framework captures population adaptation to recommendations
• Durable recommendations ensure validity over predefined time horizon