Toon Calders
Machine-learning discrimination: bias in, bias out.
Biography
Toon Calders :received his PhD in Computer Science in 2003 from the University of Antwerp.
In 2006 he joined the Eindhoven University of Technology as an assistant professor, where he left in 2012 to become associate professor at the Université libre de Bruxelles.
In 2016 Toon Calders rejoined the University of Antwerp as a full professor.
The research interests of Toon Calders are situated in machine learning, data mining, and artificial intelligence.
More specifically, he carried out research projects on integrating pattern mining in database systems, on fairness in machine learning, on stream mining, and on dynamic network analysis.
He published over 80 papers in data mining and machine learning conferences such as ACM SIGKDD, ECML/PKDD, ACM PODS, IEEE ICDM, ACM WSDM, pVLDB, SIAM SDM and journals ACM Transactions
on Database Systems, Machine Learning, and Data Mining and Knowledge Discovery.
Abstract
Artificial intelligence is more and more responsible for decisions that have a huge impact on our lives. But predictions made using data mining and algorithms can affect population subgroups differently. Academic researchers and journalists have shown that decisions taken by predictive algorithms sometimes lead to biased outcomes, reproducing inequalities already present in society. Is it possible to make a fairness-aware data mining process? Are algorithms biased because people are too? Or is it how machine learning works at the most fundamental level?
