Carlos Castillo is a Distinguished Research Professor at Universitat Pompeu Fabra in Barcelona. He is a web miner with a background on information retrieval, and has been influential in the areas of web content quality and credibility, and adversarial web search. He is a prolific researcher with more than 80 publications in top-tier international conferences and journals, receiving 13,000+ citations. His works include a book on Big Crisis Data, as well as monographs on Information and Influence Propagation, and Adversarial Web Search.

AG 1, AG 2, AG 3, INET, AG 4, AG 5, SWS, RG1, MMCI   Info

Public Audience

English

60 Minutes

Saarbrücken

Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily (offline and online) lives. Social media, e-commerce, professional, political, educational, and dating sites, to mention just a few, shape our possibilities as individuals, consumers, employees, voters, students, and lovers. In this process, vast amounts of personal data are collected and used to train machine-learning based systems. These systems are used to classify and rank people, and can discriminate us on grounds such as gender, age, or ethnicity, even without intention, and even if legally protected attributes, such as race, are not explicit in the data. Algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data.

Daniela Alessi

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