CS Tea: Leah Ajmani presents “Ethics in High Stakes Predictive Settings”

13 May 2024

Leah Ajmani is a PhD candidate in Computer Science at the GroupLens Lab at The University of Minnesota. Her research interest lies in using philosophical approaches to preempt ethical problems with machine learning technologies in high-stakes settings (e.g., mental health prediction).

Thursday, May 16, 3:30-4:30 pm, Anderson 329

Abstract: In this talk, she will describe how data decisions rely on unchecked assumptions about “right” and “wrong.” If continuously left ungoverned, these assumptions produce material harm in high-stakes settings such as mental health support and criminal justice settings. Her research investigates two pillars of progressing the ethics of AI/ML applications: disclosure and interrogation. These two pillars work together to help us operationalize our own ethical intuitions on user-generated data. Using the case study of predicting mental health status from social media data, she will show that ethics disclosures create implicit “standard practices” for computer scientists. This implicitness allows problematic data practices to go unchecked. Then, she will demonstrate how to use a popular philosophy method—thought experiments—to interrogate these implicit stances and make them explicit. She will conclude by discussing how we can build on our own ethical intuitions to create theories that have evaluative power, as well as robust and rigorous theories of how we ought to treat data used in predictive systems.