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SSP Forum: Kara Schechtman (M.S. Candidate) + Community Social

Monday, March 11, 2024
Margaret Jacks Hall (Bldg. 460)
Room 126
(See description for Notes on Entry)
Kara Schechtman

Symbolic Systems Forum
(community sessions of SYMSYS 280 - Symbolic Systems Research Seminar)

An Epistemic Approach to Statistical Fairness

Kara Schechtman (M.S. Candidate)
Symbolic Systems Program

followed by

A Community Social with Dessert for All

Monday, March 11, 2024 12;30-1:20 pm PT
Margaret Jacks Hall (Bldg. 460), Room 126
In-person event, not recorded
(see below for entry instructions if you are not an active Stanford affiliate)

Note: Lunch is provided, if pre-ordered, only for members of SYMSYS 280, but others are welcome to bring a lunch and eat during the presentation.


Kara Schechtman, An Epistemic Approach to Statistical Fairness (Primary Advisor: Thomas Icard, Philosophy; Second Reader: Ray Briggs, Philosophy) 
Predictive algorithms are used across a wide variety of settings in order to inform consequential decisionmaking about people’s lives, ranging from pretrial detention decisions in the criminal justice system to surgical intervention decisions in medical contexts. With the increasing deployment of these algorithms comes the risk that they could make unfair predictions along race or gender lines. A popular method to audit these algorithms for such biases employs measures called “statistical fairness criteria.” Strikingly, many of these criteria are impossible to satisfy at once—a result sometimes called the “impossibility of fairness,” for it illuminates an apparently tragic choice between honoring different, intuitive conceptions of fairness. In this talk, I present an argument that this interpretation of the impossibility result is, in one sense, too pessimistic, and in another, too optimistic, with both arguments driven by connecting statistical fairness to accuracy-based epistemology. On the one hand, through analyzing its connection to accuracy, I argue one statistical fairness criterion is ill-formulated; reforming it resolves the tension between two of the most prominent statistical fairness criteria. On the other hand, though, the same connection demonstrates how statistical properties give a limited conception of fairness: they only capture salient fairness concerns insofar as accuracy matters to fairness, which in many cases, may not be very much at all. My analysis thus supports growing calls for a methodological shift in algorithmic fairness, away from devising and measuring isolated metrics about a predictor’s performance, and toward a situated, multifaceted conception of fairness.


Entry to the building is open to anyone with an active Stanford ID via the card readers next to each door. If you do not have a Stanford ID, you can gain entry between 12:15 and 12:30pm ONLY by knocking on the exterior windows of room 126. These windows are to the left of the west side exterior door on the first floor of Margaret Jacks Hall, which faces the back east side of Building 420. Please do not knock on these windows after 12:30pm when the talk has started. We will not be able to come out and open the door for you at that point.