Alice Xiang (Partnership on AI)
Note: The event time listed is set to Pacific Time.
In recent years, there has been a proliferation of papers in the algorithmic fairness literature proposing various technical definitions of algorithmic bias and methods to mitigate bias. Whether these algorithmic bias mitigation methods would be permissible from a legal perspective is a complex but increasingly pressing question at a time where there are growing concerns about the potential for algorithmic decision-making to exacerbate societal inequities. In particular, there is a tension around the use of protected class variables: most algorithmic bias mitigation techniques utilize these variables or proxies, but anti-discrimination doctrine has a strong preference for decisions that are blind to them. This talk will discuss the extent to which technical approaches to algorithmic bias are compatible with U.S. anti-discrimination law and recommend a path toward greater compatibility through providing causal interpretations.
To register for this event and receive the Zoom link, please email organizers bendavid.shai [at] gmail.com (subject: Inquiry%20to%20register%20for%20Interpretable%20Machine%20Learning%20event%20June%2029%2C%202020) (Shai Ben-David) or ruth.urner [at] gmail.com (subject: Inquiry%20to%20register%20for%20Interpretable%20Machine%20Learning%20event%20June%2029%2C%202020) (Ruth Urner).