Sarah is a final-year PhD student at MIT in the Electrical Engineering and Computer Science Department advised by Prof. Aleksander Mądry and Prof. Devavrat Shah. Sarah utilizes methods from machine learning, statistical inference, causal inference, and game theory to study responsible computing and AI policy. Previously, she has written about social media, trustworthy algorithms, algorithmic fairness, and more. She is currently interested in AI auditing, AI supply chains, and IP Law x Gen AI.

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Abstact: Over the past several years, we have begun facing questions of algorithmic governance: the process of deciding when and how we should regulate algorithms. Algorithmic governance is a rich area of research that has both societal and operational significance, as it will determine not only how algorithms are permitted to intervene on our lives, but also how organizations are permitted to develop and deploy algorithms. In this talk, I will discuss three components of algorithmic governance, then illustrate them through a case study on social media regulation. Within the context of social media, I will focus on how social media platforms filter (or curate) the content that users see. I will demonstrate a way to operationalize regulations on algorithmic filtering that is mindful of the legal landscape on social media. I will further show that operationalizing such regulations does not necessarily impose a performance cost on social media platforms.