- About
- Courses
- Research
- Computational Social Science
- Critical Data Studies
- Data Science
- Economics and Information
- Education Technology
- Ethics, Law and Policy
- Human-Computer Interaction
- Human-Robot Interaction
- Incentives and Computation
- Infrastructure Studies
- Interface Design and Ubiquitous Computing
- Natural Language Processing
- Network Science
- Social Computing and Computer-supported Cooperative Work
- Technology and Equity
- People
- Career
- Undergraduate
- Info Sci Majors
- BA - Information Science (College of Arts & Sciences)
- BS - Information Science (CALS)
- BS - Information Science, Systems, and Technology
- MPS Early Credit Option
- Independent Research
- CPT Procedures
- Student Associations
- Undergraduate Minor in Info Sci
- Our Students and Alumni
- Graduation Info
- Contact Us
- Info Sci Majors
- Masters
- PHD
- Prospective PhD Students
- Admissions
- Degree Requirements and Curriculum
- Grad Student Orgs
- For Current PhDs
- Diversity and Inclusion
- Our Students and Alumni
- Graduation Info
- Program Contacts and Student Advising
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.
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.