20 citations · 21 across the 3 of their papers we have counts for
7 papers
(When) Are Contrastive Explanations of Reinforcement Learning Helpful?
Sanjana Narayanan, Isaac Lage, Finale Doshi-Velez
Global explanations of a reinforcement learning (RL) agent's expected behavior can make it safer to deploy. However, such explanations are often difficult to understand because of…
Promises and Pitfalls of Black-Box Concept Learning Models
Anita Mahinpei, Justin Clark, Isaac Lage +2
Machine learning models that incorporate concept learning as an intermediate step in their decision making process can match the performance of black-box predictive models while re…
Learning Interpretable Concept-Based Models with Human Feedback
Isaac Lage, Finale Doshi-Velez
Machine learning models that first learn a representation of a domain in terms of human-understandable concepts, then use it to make predictions, have been proposed to facilitate i…
Exploring Computational User Models for Agent Policy Summarization
Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez +1
AI agents are being developed to support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understan…
An Evaluation of the Human-Interpretability of Explanation
Isaac Lage, Emily Chen, Jeffrey He +4
Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what ki…
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Omer Gottesman, Fredrik Johansson, Joshua Meier +16
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL)…