121 citations · 276 across the 8 of their papers we have counts for
8 papers · 1 filter
Towards Integrating Personal Knowledge into Test-Time Predictions
Isaac Lage, Sonali Parbhoo, Finale Doshi-Velez
Machine learning (ML) models can make decisions based on large amounts of data, but they can be missing personal knowledge available to human users about whom predictions are made.…
(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…
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
Sean McGrath, Parth Mehta, Alexandra Zytek +2
As machine learning (ML) models are increasingly being employed to assist human decision makers, it becomes critical to provide these decision makers with relevant inputs which can…
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)…