1.6k citations · 2k across the 30 of their papers we have counts for
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Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts
Kaivalya Rawal, Ece Kamar, Himabindu Lakkaraju
As predictive models are increasingly being deployed to make a variety of consequential decisions, there is a growing emphasis on designing algorithms that can provide recourse to…
An Empirical Analysis of Backward Compatibility in Machine Learning Systems
Megha Srivastava, Besmira Nushi, Ece Kamar +2
In many applications of machine learning (ML), updates are performed with the goal of enhancing model performance. However, current practices for updating models rely solely on iso…
Personalization in Human-AI Teams: Improving the Compatibility-Accuracy Tradeoff
Jonathan Martinez, Kobi Gal, Ece Kamar +1
AI systems that model and interact with users can update their models over time to reflect new information and changes in the environment. Although these updates may improve the ov…
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
Besmira Nushi, Ece Kamar, Eric Horvitz
As machine learning systems move from computer-science laboratories into the open world, their accountability becomes a high priority problem. Accountability requires deep understa…
Investigating Human + Machine Complementarity for Recidivism Predictions
Sarah Tan, Julius Adebayo, Kori Inkpen +1
When might human input help (or not) when assessing risk in fairness domains? Dressel and Farid (2018) asked Mechanical Turk workers to evaluate a subset of defendants in the ProPu…
Discovering Blind Spots in Reinforcement Learning
Ramya Ramakrishnan, Ece Kamar, Debadeepta Dey +2
Agents trained in simulation may make errors in the real world due to mismatches between training and execution environments. These mistakes can be dangerous and difficult to disco…