15 citations · 35 across the 14 of their papers we have counts for
6 papers · 1 filter
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
Hussein Mozannar, Jimin J Lee, Dennis Wei +3
People are relying on AI agents to assist them with various tasks. The human must know when to rely on the agent, collaborate with the agent, or ignore its suggestions. In this wor…
In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer
Yuzhou Cao, Hussein Mozannar, Lei Feng +2
Enabling machine learning classifiers to defer their decision to a downstream expert when the expert is more accurate will ensure improved safety and performance. This objective ca…
Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration
Hussein Mozannar, Yuria Utsumi, Irene Y. Chen +4
A high-risk pregnancy is a pregnancy complicated by factors that can adversely affect the outcomes of the mother or the infant. Health insurers use algorithms to identify members w…
Consistent Estimators for Learning to Defer to an Expert
Hussein Mozannar, David Sontag
Learning algorithms are often used in conjunction with expert decision makers in practical scenarios, however this fact is largely ignored when designing these algorithms. In this…
Fair Learning with Private Demographic Data
Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations. We give a scheme that all…
From Fair Decision Making to Social Equality
Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) hav…