The Due Process Deficit: Auditing AI Governance in U.S. Higher Education
Year: 2026
Type: conference-paper
Abstract: U.S. universities have met Artificial Intelligence (AI) with policy - revised honor codes, AI use guidelines, and detection tools to enforce them - but the procedural infrastructure that would let affected parties understand, contest, and seek redress for AI-related decisions has not kept pace. We present ACAI-US79, a benchmark dataset allowing for institutional auditing of AI governance across 79 U.S. universities spanning R1, R2, and teaching-focused institutions, and ACAI, the Academic AI Capacity Index, an interpretable measure of the public legibility of governance structures that allocate authority and accountability. Unlike prior audits focused on pedagogical guidance at elite or Rl-only samples, ACAI shifts the analytic frame to procedural accountability, evaluating four domains — policy clarity, faculty support, feedback mechanisms, and AI detection tool governance — through time-bounded review of institutionally authoritative materials. We find that governance capacity is systematically concentrated in rule articulation while mechanisms for participation, feedback, and procedural constraint are underdeveloped, and that AI research intensity is weakly correlated with AI governance capacity - i.e., we find that technical leadership does not translate into procedural accountability. We further show that LLM-based audits produce unstable institutional rankings, demonstrating the continued necessity of human judgment in high-stakes audit contexts. We release the dataset, audit instrument, and public website at http://acai-us79.org/ to support transparency, critique, and institutional self-reflection, laying empirical groundwork for accountable AI governance in higher education.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166564305