168 citations · 871 across the 72 of their papers we have counts for
6 papers · 1 filter
MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models
Tessa Han, Aounon Kumar, Chirag Agarwal +1
As large language models (LLMs) develop increasingly sophisticated capabilities and find applications in medical settings, it becomes important to assess their medical safety due t…
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten
Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. Whil…
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations
Himabindu Lakkaraju, Osbert Bastani
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniq…
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…
Learning Cost-Effective Treatment Regimes using Markov Decision Processes
Himabindu Lakkaraju, Cynthia Rudin
Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bails to defendants on a daily basis. Such decisions t…
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration
Himabindu Lakkaraju, Ece Kamar, Rich Caruana +1
Predictive models deployed in the real world may assign incorrect labels to instances with high confidence. Such errors or unknown unknowns are rooted in model incompleteness, and…