IEOR Seminars occur on Mondays throughout the fall semester in room 3108 of Etcheverry Hall. Seminars feature leading-edge research from experts in industrial engineering and operations research who come from local, national, and international institutions. Seminars are open to students, faculty, and the public.

Title: Bridging Ethical Algorithms, Law, and Practice: Hiring and Beyond

Abstract: Optimization and statistical models based on historical and socio-economic data that do not incorporate fairness desiderata can lead to unfair, discriminatory, or biased outcomes. These are expected to be further exacerbated by the scale and adoption of cutting-edge technologies, especially in the aftermath of the current pandemic. New ideas are needed to mitigate the harm of socio-economic trends embedded in the data, ensure that the developed systems are accountable under uncertainty, and reduce a deeper propagation of biases in multi-level decisions.

In this talk, I will first discuss opportunities to develop ethical hiring pathways. Recent run-ins of Microsoft and Well Fargo with the Labor Department’s Office of Federal Contract Compliance Programs (OFCCP) highlight a paradox: failing to address workforce imbalance can result in legal sanctions and scrutiny, but proactive measures to address these issues might result in the same legal conflict. I will propose that partially-ordered sets, “posets”, can transparently account for known uncertainties and biases in evaluation data, giving rise to an interesting class of optimization problems. I will discuss incorporating posets in the online secretary framework, which has been classically used to model the online selection of k candidates from a streaming set of N. Using techniques from the matroid secretary literature and order theory, I will show near-optimal competitive ratios for maximizing the true total utility of hired candidates, even with access to only a partial order on the candidates. 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). I will discuss the tensions with the law and ways to argue the legal feasibility of our proposed approach. This is based on joint work with Deven Desai and Jad Salem. I will further discuss new challenges in optimization that can help with ethical decision-making in the context of other applications such as admissions, detection of critical diseases like sepsis, facility location, and gerrymandering.

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 recently awarded NSF AI Institute on Advances in Optimization (ai4opt.org). She received a Ph.D. in Operations Research from MIT in 2017. Dr. Gupta’s research interests are in optimization, machine learning, and algorithmic fairness. Her work spans various application domains such as revenue management, healthcare, energy, and quantum computation. She received the JP Morgan Chase Early Career Faculty Award in 2021, Student Recognition of Excellence in Teaching: Class of 1934 CIOS Honor Roll 2021 and 2022, 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. Her research is partially funded by the National Science Foundation and DARPA.

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