SWS   Info

Public Audience

English

60 Minutes

Saarbrücken

Machine learning (ML) based algorithms assist human decision-making in a variety of scenarios, ranging from medical diagnostics to bail decision-making. The potential societal impact of using machine decision aids in real-world settings sparked concerns about their accuracy and fairness, and inspired a flurry of research on algorithmic fairness, accountability and transparency. However, in many settings, algorithms do not make decisions, but only assist human decision-makers. In this thesis, we go beyond studying the fairness and accuracy of decision aids, and study machine-assisted human decision-making as a whole. Specifically, we study how people perceive and utilize machine decision aids.

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