Teaching Probabilistic Machine Learning in the Liberal Arts: Empowering Socially and Mathematically Informed AI Discourse
Yaniv Yacoby
TL;DR
Problem: Many undergraduate ML curricula neglect base-layer ethics and equitable participation, limiting socially informed discourse. Approach: Introduces a framework-focused, probabilistic modeling course using a PPL (NumPyro) and a whimsical Intergalactic Hypothetical Hospital to teach Directed Graphical Models, probabilistic inference, and critical ethics. Contributions: (1) three learning objectives (Theory, Application, Ethics), (2) math-accessible modeling via PPLs, (3) integrated counter-narratives with real case studies, and (4) publicly available materials. Significance: demonstrates a scalable liberal-arts pedagogy that empowers minoritized students to contribute as technologists and critical, engaged citizens in AI discourse.
Abstract
We present a new undergraduate ML course at our institution, a small liberal arts college serving students minoritized in STEM, designed to empower students to critically connect the mathematical foundations of ML with its sociotechnical implications. We propose a "framework-focused" approach, teaching students the language and formalism of probabilistic modeling while leveraging probabilistic programming to lower mathematical barriers. We introduce methodological concepts through a whimsical, yet realistic theme, the "Intergalactic Hypothetical Hospital," to make the content both relevant and accessible. Finally, we pair each technical innovation with counter-narratives that challenge its value using real, open-ended case-studies to cultivate dialectical thinking. By encouraging creativity in modeling and highlighting unresolved ethical challenges, we help students recognize the value and need of their unique perspectives, empowering them to participate confidently in AI discourse as technologists and critical citizens.