Schedule, Notes/Handouts & Videos

While algorithmic fairness continues to be a thriving area of research in ML, in practice, mitigating issues of bias and unfairness often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing fairness constraints during the optimization step, post-processing model outputs or by manipulating the training data. Recent work has called on the ML community to take a more holistic approach to tackle fairness issues by systematically investigating the multitude of design choices made through the ML pipeline and identifying effective interventions at the root cause, as opposed to the symptoms. However, there are currently very few methods of operationalizing this approach in practice. Drawing on our experience as educators and practitioners, we first demonstrate that without clear guidelines and toolkits, even individuals with specialized ML knowledge find it challenging to hypothesize how various design choices influence model behavior. We then consult the fair-ML literature to understand the progress to date toward operationalizing the pipeline-aware approach: we systematically collect and organize the prior work that attempts to detect, measure, and mitigate various sources of unfairness through the ML pipeline. We utilize this extensive categorization of previous contributions to sketch a research agenda for the community.

More than 65 years after school segregation was ruled unconstitutional, public schools across the United States are resegregating along racial and socioeconomic lines. Many cities have attempted to disentangle school and neighborhood segregation and improve equitable access using policies for city-wide choice. However, these policies have largely not improved patterns of segregation and inequity. From 2018 to 2020, we worked with the San Francisco Unified School District (SFUSD) to design a new policy for student assignment system that meets the district’s goals of diversity, predictability, proximity and equity of access. In close collaboration with SFUSD, we informed the design of a new policy that was approved in 2020 for use starting the 2026–27 school year. In this talk, I will discuss our policy design approach, as well as questions it raises about algorithms, market design, and equity.