ML Infra + Apps

FastCFE: A counterfactual explainer using reinforcement learning

Ray Summit 2022

Counterfactuals, an active area of research in machine learning explainability, are explanations that produce actionable steps to move a data point from one side of a decision boundary to another. These explanations have a clear use case for several applications ranging from loan decisions to healthcare diagnosis, where they need to advise stakeholders about actions they can take to achieve a different outcome. Individuals not provided loans want steps they can take to achieve a loan, and similarly patients want to know how they can achieve a better diagnosis. This presentation showcases FastCFE, an algorithm and feature that uses reinforcement learning to provide real-time counterfactual explanations. Our presentation is broken down as follows:

  1. Overview of counterfactuals and reinforcement learning (RL)
  2. Deep distributed reinforcement learning using OpenAI Gym and Ray+RLlib and a demonstration using these frameworks.
  3. Benchmarks and results

About Karthik

Karthik Rao is a machine learning engineer at Arthur AI (Monitoring, Performance, Explainability). He was previously an undergraduate at Harvard focused on big data systems for machine learning. He is passionate about designing and building novel machine learning solutions using state-of-the art frameworks.

About Sahil

Sahil Verma is a PhD student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, Seattle. He is interested in exploring ways to make machine learning trustworthy, including explainability and fairness, and he hopes to continue exploring this area in the future.

Karthik Rao

Machine Learning Engineer, Arthur AI

Sahil Verma

PhD Student, University of Washington
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