Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

Abstract

Machine learning based decision making systems are increasingly affecting humans. An individual can suffer an undesirable outcome under such decision making systems (e.g. denied credit) irrespective of whether the decision is fair or accurate. Individual recourse pertains to the problem of providing an actionable set of changes a person can undertake in order to improve their outcome. We propose a recourse algorithm that models the underlying data distribution or manifold. We then provide a mechanism to generate the smallest set of changes that will improve an individual's outcome. This mechanism can be easily used to provide recourse for any differentiable machine learning based decision making system. Further, the resulting algorithm is shown to be applicable to both supervised classification and causal decision making systems. Our work attempts to fill gaps in existing fairness literature that have primarily focused on discovering and/or algorithmically enforcing fairness constraints on decision making systems. This work also provides an alternative approach to generating counterfactual explanations.

Cited by in corpus (19)

• To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods
• The Conflict Between Explainable and Accountable Decision-Making Algorithms
• Generating collective counterfactual explanations in score-based classification via mathematical optimization
• Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
• Model extraction from counterfactual explanations
• On Counterfactual Explanations under Predictive Multiplicity
• GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
• From Explanation to Recommendation: Ethical Standards for Algorithmic Recourse
• Explanation by Progressive Exaggeration
• Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
• Consistent Counterfactuals for Deep Models
• Conditional Generative Models for Counterfactual Explanations
• Endogenous Macrodynamics in Algorithmic Recourse
• Framing Algorithmic Recourse for Anomaly Detection
• Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learning
• Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations
• Extracting Incentives from Black-Box Decisions
• Counterfactual Explanations via Latent Space Projection and Interpolation
• Explainability Requires Interactivity