Talk Carlos Castillo

Abstract: In this talk, we present ongoing research on human factors of decision support systems that has consequences from the perspective of algorithmic fairness. We study two different settings: a game and a high-stakes scenario. The game is an exploratory “oil drilling” game, while the high-stakes scenario is the prediction of criminal recidivism. In both cases, a decision support system helps a human make a decision. We observe that in general users of such systems must thread a fine line between algorithmic aversion (completely disregarding the algorithmic support) and automation bias (completely disregarding their own judgment). The talk presents joint work led by David Solans and Manuel Portela.

Bio: Carlos Castillo (they/them) is an ICREA Research Professor at Universitat Pompeu Fabra in Barcelona, where they lead the Web Science and Social Computing research group. They are a web miner with a background in information retrieval and have been influential in the areas of crisis informatics, web content quality and credibility, and adversarial web search. They are a prolific, highly cited researcher who has co-authored over 110 publications in top-tier international conferences and journals, receiving two test-of-time awards, five best paper awards, and two best student paper awards. Their works include a book on Big Crisis Data, as well as monographs on Information and Influence Propagation, and Adversarial Web Search.