SIAM Mathematics of Data Science (MDS20) Distinguished Lecture Series: Cynthia Dwork

/ May 19, 2020/

Facts, Flexibility, and Fairness

Abstract: The vast majority of work in algorithmic fairness addresses classification and scoring tasks. Missing from the literature is a treatment of fairness in ranking, a central element in many selection procedures as well as in approaches to affirmative action. Definitions of (algorithmic) fairness fall roughly into two categories: group fairness and individual fairness. The former typically require that some statistic be similar across supposedly disjoint groups; the latter require that people who are similar with respect to a given task should be treated similarly. A more recent line of work lies somewhere in between, and considers a large number of possibly intersecting subpopulations. The philosophy that undergirds this last notion is to first try to understand what the historical outcomes data say (“facts”); next explore the evidence-consistent options (“flexibility”); and, finally, adjust as appropriate (“fairness”). This self-contained talk will focus on evidence-based ranking and the flexibility it offers, noting that flexibility can be a double-edged sword.

Cynthia Dwork, Harvard University, U.S.

This is one of seven virtual plenary talks originally scheduled for the 2020 SIAM Conference on Mathematics of Data Science. For more information on this session, visit https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=69237. To view the virtual program and register for other invited plenary talks, minitutorial talks, and minisymposia, please visit the MDS20 website at https://www.siam.org/conferences/cm/conference/mds20.