Teaching Probabilistic Machine Learning in the Liberal Arts: Empowering Socially and Mathematically Informed AI Discourse

TL;DR

This paper describes a new undergraduate machine learning course designed for liberal arts students, emphasizing probabilistic modeling, sociotechnical implications, and critical thinking through innovative teaching methods and real-world case studies.

Contribution

It introduces a framework-focused, accessible approach to teaching probabilistic ML that integrates ethical considerations and dialectical thinking for socially informed AI education.

Findings

Students develop a critical understanding of ML foundations.

The course enhances engagement through thematic storytelling.

Students are empowered to participate confidently 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.