Machine learning (ML) and artificial intelligence (AI) systems are ubiquitous in everyday decision-making, and are extensively used in contexts that directly affect our everyday lives. These algorithms are run using already collected datasets, which unfortunately tend to include implicit social biases. As a result, there has been a plethora of examples where the final outcome of ML and AI systems perpetuates the existing underlying biases. Therefore, it is imperative to design systems while taking issues of fairness explicitly into account. This fascinating research direction has recently attracted the interest of the scientific community.
Sponsored by the University of Maryland Center for Machine Learning and Capital One, the purpose of the Fairness in AI Seminar Series is to explore the topic of fairness in AI, ML and theoretical computer science by having researchers present their cutting-edge work on this family of problems. The talks will take place virtually on Mondays from 11 a.m. to noon, and be recorded and posted here for later viewing.
Talk announcements and Zoom links will be distributed through the Center for Machine Learning mailing list, which you can sign up for at https://go.umd.edu/cml-signup. If you are interested in giving a talk, please contact Leonidas Tsepenekas, email@example.com.