AIhub monthly digest: May 2023 – mitigating biases, ICLR invited talks, and Eurovision fun

Welcome to our May 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we learn how to mitigate biases in machine learning, explore tradeoffs in school redistricting, and find out how machine learning algorithms fared in predicting the winner of this year's Eurovision Song Contest. In this blogpost, Max Springer examines the notion of fairness in hierarchical clustering. Max and colleagues demonstrate that it's possible to incorporate fairness constraints or demographic information into the optimization process to reduce biases in ML models without significantly sacrificing performance. Joar Skalse and Alessandro Abate won the AAAI 2023 outstanding paper award for their work, Misspecification in Inverse Reinforcement Learning, in which they study the question of how robust the inverse reinforcement learning problem is to misspecification of the underlying behavioural model.