Dynamic Transparency: Real-Time Audit Traceability for Adaptive AI Systems

Most current AI audit frameworks (see Schaeffer et al., 2024; Manheim et al., 2024) rely on periodic, snapshot-style audits that often lag behind the rapid evolution of AI systems. This is especially problematic for adaptive or continually learning models, which may introduce new risks or biases between audit intervals. Building on the observed audit deviations and the call for credible, evolving standards (Manheim et al.), this research proposes a protocol for real-time audit traceability: a secure, immutable log that records model changes, data drift, and decision rationales as they happen. Such a system could leverage blockchain or distributed ledger technologies to ensure integrity and transparency. By enabling auditors and stakeholders to monitor deviations in near real-time, this approach goes beyond static post-hoc audits and addresses the ever-evolving nature of deployed AI. The potential impact is significant—improving stakeholder trust, enabling quicker responses to emerging risks, and providing a model for adaptive, “living” audit standards.

References:

1. Risks of AI Applications Used in Higher Education. Donna Schaeffer, Lori Coombs, Jonathan Luckett, Marvin Marin, Patrick C. Olson (2024). Electronic Journal of e-Learning.
2. The Necessity of AI Audit Standards Boards. David Manheim, Sammy Martin, Mark Bailey, Mikhail Samin, Ross Greutzmacher (2024). AI & SOCIETY.