Goran Radanovic is a postdoctoral researcher at Harvard University, where he works on problems related to human-AI collaboration,
value-aligned artificial intelligence, and social computing. His research particularly focuses on incentive mechanism design, reinforcement learning
with humans, and algorithmic fairness. He received his Ph.D. in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL)
in 2016. He is a recipient of the Early Postdoc Mobility Fellowship (2016-2018) from the Swiss National Science Foundation, and was awarded with an
EPFL Ph.D. Distinction for an outstanding dissertation in 2017.

SWS, RG1, MMCI   Info

AG Audience

English

90 Minutes

Saarbrücken

Recent AI advances have been driven by high-quality input data, often labeled by human annotators. A fundamental challenge in eliciting high-quality information from humans is that there is often no way to directly verify the quality of the information they provide. Consider, for example, product reviews and marketing surveys where data is inherently subjective, environmental community sensing where data is highly localized, and geopolitical forecasting where the ground truth is revealed in the distant future. In these settings, data elicitation has to rely on peer-consistency mechanisms, which incentivize high-quality reporting by examining the consistency between the reports of different data providers. In this talk, I will discuss some of the recent advances in peer-consistency designs. Furthermore, I will outline some thoughts on an agenda around the design of human-AI collaborative systems.

Claudia Richter

9303 9103

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Yes

Kaiserslautern

G26

111

SWS Space 2 (6312)

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