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Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain
We explore the potential of Large Language Models (LLMs) to assist and potentially correct physicians in medical decision-making tasks.
Learning To Guide Human Decision Makers With Vision-Language Models
As a remedy, we introduce learning to guide (LTG), an alternative framework in which - rather than taking control from the human expert - the machine provides guidance useful for decision making, and the human is entirely responsible for coming up with a decision.
Rethinking and Recomputing the Value of ML Models
In this paper, we argue that the way we have been training and evaluating ML models has largely forgotten the fact that they are applied in an organization or societal context as they provide value to people.
The Science of Rejection: A Research Area for Human Computation
We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
Active Learning from Crowd in Document Screening
In this paper, we explore how to efficiently combine crowdsourcing and machine intelligence for the problem of document screening, where we need to screen documents with a set of machine-learning filters.
