When someone is denied a job, offered a different price for the same goods or services, denied bail or declined a loan, intent to discriminate is often not the case. The decision system applies the same data and rules to all and yet has a disproportionate effect on a protected class, i.e., individuals protected by law from discrimination based on sex, race, age, etc. The causes of such disparate impact in machine learning and optimization are many, but two prominent ones that my work focuses on are: (i) Biases in Data and (ii) Competing objectives. The first entails reliance on observed data that inherently embeds socio-economic and historical practices, e.g., test scores are known to be underestimated for students in stereotyped groups and low-income families, leading to discriminatory outcomes. The latter set of problems entails trade-offs with utilitarian objectives, equity, efficiency, and learnability. I will discuss these issues in the context of various applications, ranging from selection problems such as hiring, admissions, and healthcare pipelines, to reliability in power systems operations, facility location, and demand learning. I will discuss three new challenges in optimization that must be tackled to make ethical decisions in these application-specific contexts.
Bio: Dr. Swati Gupta is an Assistant Professor at the MIT Sloan School of Management in the Operations Research and Statistics Group, since July 2023. Prior to this, she held a Fouts Family Early Career Professorship as an Assistant Professor at Georgia Institute of Technology, where she was the lead of Ethical AI in the multi-institution NSF AI Institute on Advances in Optimization (AI4OPT), awarded in 2021. She received a Ph.D. in Operations Research from MIT and a dual Bachelors and Masters from IIT Delhi. Her research interests include optimization and machine learning, with a focus on algorithmic fairness. Her work spans various domains such as hiring, admissions, e-commerce, quantum optimization, and energy. She received the NSF CAREER Award in 2023, the Class of 1934: Student Recognition of Excellence in Teaching in 2020 and 2021 at Georgia Tech, the JP Morgan Early Career Faculty Recognition in 2021, the NSF CISE Research Initiation Initiative Award in 2019, and the Google Women in Engineering Award (India) in 2011. Her research and students have received recognition at various venues, e.g., finalists in INFORMS Doing Good with OR 2022, INFORMS Undergraduate Operations Research 2018, INFORMS Computing Society 2016, and INFORMS Service Science Student Paper 2016. Dr. Gupta’s research is partially funded by the National Science Foundation and DARPA.