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

2025-10-28

AI Summary

Underrepresented students—particularly those from humanities backgrounds and non-STEM disciplines—often face significant barriers to engaging with probabilistic machine learning (ML) due to high mathematical thresholds and perceived irrelevance to societal concerns. Method: This study introduces a “framework-focused” pedagogy for an undergraduate probability ML course, anchored in an interdisciplinary narrative—the fictional “Interstellar Hypothetical Hospital”—and integrating probabilistic programming to lower mathematical entry barriers. The curriculum employs open, real-world case studies and counter-narrative discussions to systematically connect foundational ML concepts (e.g., Bayesian modeling) with sociotechnical implications. Contribution/Results: The approach innovatively merges whimsical storytelling with rigorous probabilistic reasoning to enhance accessibility and engagement; uses ethical dilemmas as anchors for developing dialectical AI literacy; and concurrently cultivates modeling competence, critical thinking, and technocivic identity. Empirical evaluation demonstrates significant gains in students’ integrated understanding of ML principles and their social dimensions, alongside increased confidence and capacity to participate diversely in public AI discourse.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Probabilistic Programming
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Teaching probabilistic machine learning to liberal arts students
• Connecting mathematical foundations with sociotechnical implications
• Lowering mathematical barriers through probabilistic programming

Innovation

• Framework-focused probabilistic modeling for ML education
• Probabilistic programming to reduce mathematical learning barriers
• Counter-narratives paired with technical innovations for dialectical thinking