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.
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 disparities across different racial groups.
In the second part of this workshop, we invite researchers and industry practitioners to speak on a panel about sociotechnical challenges data scientists face when applying fairness methodology to their work. The panel will be moderated by Michael Madaio, Postdoctoral Researcher at Microsoft Research. The panelists will first introduce themselves, and then answer some moderator-provided questions about documenting responsible AI practices, discussing fairness within your team and organization, and incorporating fairness in the design and evaluation of AI systems.