1.6k citations · 2k across the 30 of their papers we have counts for
9 papers · 1 filter
Tandem Training for Language Models
Robert West, Ashton Anderson, Ece Kamar +1
As language models continue to rapidly improve, we can expect their actions and reasoning to become difficult or impossible for weaker agents and humans to follow, undermining inte…
A Bayesian Approach to Identifying Representational Errors
Ramya Ramakrishnan, Vaibhav Unhelkar, Ece Kamar +1
Trained AI systems and expert decision makers can make errors that are often difficult to identify and understand. Determining the root cause for these errors can improve future de…
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal, Tongshuang Wu, Joyce Zhou +5
Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, pr…
Learning to Complement Humans
Bryan Wilder, Eric Horvitz, Ece Kamar
A rising vision for AI in the open world centers on the development of systems that can complement humans for perceptual, diagnostic, and reasoning tasks. To date, systems aimed at…
Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork
Gagan Bansal, Besmira Nushi, Ece Kamar +2
AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously. However, in practice, AI system…
Interpretable & Explorable Approximations of Black Box Models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana +1
We propose Black Box Explanations through Transparent Approximations (BETA), a novel model agnostic framework for explaining the behavior of any black-box classifier by simultaneou…