no code implementations • 11 Nov 2020 • Evgeny Krivosheev, Burcu Sayin, Alessandro Bozzon, Zoltán Szlávik
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.
no code implementations • 11 Nov 2021 • Burcu Sayin, Jie Yang, Andrea Passerini, Fabio Casati
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.
no code implementations • 30 Sep 2022 • Burcu Sayin, Fabio Casati, Andrea Passerini, Jie Yang, Xinyue Chen
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.