RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation
cs.AI, cs.LG
Submitted: 2026-06-15
Updated: 2026-08-28
Terminology
Sources
- Getting a CLUE: A Method for Explaining Uncertainty Estimates
- Consistent Counterfactuals for Deep Models
- What is Reproducibility in Artificial Intelligence and Machine Learning Research?
- RobustX: Robust Counterfactual Explanations Made Easy
- Argumentative Ensembling for Robust Recourse under Model Multiplicity
- Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
- Learning-Augmented Robust Algorithmic Recourse
- Inverse Classification for Comparison-based Interpretability in Machine Learning
- Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
- Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
- Towards Robust and Reliable Algorithmic Recourse
- Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
- VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection