Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learning

Abstract

Counterfactual instances are a powerful tool to obtain valuable insights into automated decision processes, describing the necessary minimal changes in the input space to alter the prediction towards a desired target. Most previous approaches require a separate, computationally expensive optimization procedure per instance, making them impractical for both large amounts of data and high-dimensional data. Moreover, these methods are often restricted to certain subclasses of machine learning models (e.g. differentiable or tree-based models). In this work, we propose a deep reinforcement learning approach that transforms the optimization procedure into an end-to-end learnable process, allowing us to generate batches of counterfactual instances in a single forward pass. Our experiments on real-world data show that our method i) is model-agnostic (does not assume differentiability), relying only on feedback from model predictions; ii) allows for generating target-conditional counterfactual instances; iii) allows for flexible feature range constraints for numerical and categorical attributes, including the immutability of protected features (e.g. gender, race); iv) is easily extended to other data modalities such as images.

18 pages

References in corpus (6)

• Conditional Generative Adversarial Nets
• Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
• Inverse Classification for Comparison-based Interpretability in Machine Learning
• PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards
• Conditional Generative Models for Counterfactual Explanations
• Born Identity Network: Multi-way Counterfactual Map Generation to Explain a Classifier's Decision