Date: Wednesday, December 1st, 2021
9:00 am – 10:00 am Pacific Time
12:00 pm – 1:00 pm Eastern Time
Location: Weekly Seminar, Zoom
Title: Mitigating the Impact of Bias in Selection Algorithms
Abstract:
The introduction of automation into the hiring process has put a spotlight on a persistent problem: discrimination in hiring on the basis of protected-class status. Left unchecked, algorithmic applicant-screening can exacerbate pre-existing societal inequalities and even introduce new sources of bias; if designed with bias-mitigation in mind, however, automated methods have the potential to produce fairer decisions than non-automated methods. In this work, we focus on selection algorithms used in the hiring process (e.g., resume-filtering algorithms) given access to a “biased evaluation metric”. That is, we assume that the method for numerically scoring applications is inaccurate in a way that adversely impacts certain demographic groups. We propose a partially-ordered set “poset” model of bias, wherein certain pairs of candidates can be declared incomparable, which generalizes some of the existing models of bias in the literature. We bring in techniques from matroid secretary literature and order theory to develop bias-aware algorithms that are able to achieve certain “fair” properties, while obtaining near-optimal competitive ratios for maximizing true utility of hired candidates in a variety of adversarial and stochastic settings. Keeping in mind the requirements of U.S. anti-discrimination law, however, certain interventions based on demographic data can be construed as illegal (e.g., quotas), and we will conclude the talk by partially addressing tensions with the law and ways to argue legal feasibility of our proposed interventions. This talk is based on work with Jad Salem and Deven R. Desai.
Bio:
Dr. Swati Gupta is an Assistant Professor and Fouts Family Early Career Professor in the H. Milton Stewart School of Industrial & Systems Engineering at Georgia Institute of Technology. She is the lead of Ethical AI at the NSF AI Institute on AI4OPT (ai4opt.org). She received a Ph.D. in Operations Research from MIT in 2017 and a joint Bachelors and Masters in CS from IIT, Delhi in 2011. Dr. Gupta’s research interests are in optimization, machine learning and algorithmic fairness. Her work spans various application domains such as revenue management, energy and quantum computation. She received the JP Morgan Chase Early Career Faculty Award in 2021, Class of 1934 CIOS Honor Roll 2021 and NSF CISE Research Initiation Initiative (CRII) Award in 2019. She was also awarded the prestigious Simons-Berkeley Research Fellowship in 2017-2018, where she was selected as the Microsoft Research Fellow in 2018. Dr. Gupta received the Google Women in Engineering Award in India in 2011. Dr. Gupta’s research is partially funded by the NSF and DARPA.