Social systems are now fueled by algorithms that facilitate and control connections and information. Simultaneously, computational systems are now fueled by people—their interactions, data, and behavior. Consequently, there is a pressing need to design new algorithms that are socially responsible in how they learn and socially optimal in the manner in which they use information.
We’ve made initial progress in addressing such problems at the interface of social and computational systems. Elisa Celis explores the emergence of bias in algorithmic decision making and presents first steps toward developing a systematic framework to control biases in classical problems, such as data summarization and personalization. This work leads to new algorithms that have the ability to alleviate bias and increase diversity while often simultaneously maintaining their theoretical or empirical performance with respect to the original metrics.
Elisa Celis is a senior research scientist at the School of Computer and Communication Sciences at EPFL. Previously, she was a research scientist at Xerox Research, where she was the worldwide head of the Crowdsourcing and Human Computation research thrust. Her research focuses on studying social and economic questions that arise in the context of the internet and spans multiple areas including fairness in AI/ML, social computing, online learning, network science, and mechanism design. Elisa is the recipient of the Yahoo! Key Challenges Award and the China Theory Week Prize. She holds a BSci in computer science and mathematics from Harvey Mudd College and a PhD in computer science from the University of Washington.
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