Assessing and Mitigating Unfairness in AI Systems - Manojit Nandi | PyData Global 2021

Assessing and Mitigating Unfairness in AI Systems Speaker: Manojit Nandi Summary This workshop aims to help data science practitioners navigate the sociotechnical challenges of AI fairness. In the first half of the workshop, we walk participants through a Jupyter notebook showing how Fairlearn can be used to assess and mitigate unfairness in ML models. In the second half, a panel of speakers will discuss best practices for improving fairness of real-world AI systems. Description Fairness in AI systems is an interdisciplinary field of research and practice that aims to understand the negative impacts of AI, with an emphasis on improving and supporting historically marginalized and underserved communities. In this workshop, we first walk participants through an hour-long tutorial on assessing and mitigating fairness-related harms in the context of an U.S. healthcare scenario. Participants will learn how to use the Fairlearn library to assess machine learning models for performance dispari