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…
Understanding Failures of Deep Networks via Robust Feature Extraction
Sahil Singla, Besmira Nushi, Shital Shah +2
Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over fea…
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…
Avoiding Negative Side Effects due to Incomplete Knowledge of AI Systems
Sandhya Saisubramanian, Shlomo Zilberstein, Ece Kamar
Autonomous agents acting in the real-world often operate based on models that ignore certain aspects of the environment. The incompleteness of any given model -- handcrafted or mac…
Security and Machine Learning in the Real World
Ivan Evtimov, Weidong Cui, Ece Kamar +3
Machine learning (ML) models deployed in many safety- and business-critical systems are vulnerable to exploitation through adversarial examples. A large body of academic research h…
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…