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AI governance frameworks built around compliance checklists are missing the larger risk: systems that satisfy every regulatory requirement while quietly eroding the human judgment they’re supposed to support. Writing for the IAPP, researcher and disability-accessibility advocate Vaibhav Namburi argues that human agency deserves treatment as a measurable governance objective, not an abstract ethical aspiration. The concern spans hiring, performance evaluation, public-sector welfare systems, and accessibility infrastructure, anywhere automated recommendations gradually displace active human reasoning without anyone formally deciding that’s acceptable.
What this means for your business
The risk profile here is unfamiliar, which is exactly what makes it dangerous. Your organization almost certainly has controls for the things auditors check: documented policies, escalation paths, explainability logs. What most governance programs don’t measure is workflow dependency drift, the slow migration of practical decision-making confidence from human reviewers to automated outputs. Nobody announces the handoff. It happens through repetition, efficiency incentives, and the social cost of consistently overriding a system that’s usually right. CISOs who think their job ends at regulatory alignment are governing the last war.
The argument Namburi makes is structurally sound, even though the IAPP’s institutional interest in expanding what “governance” covers means the framing tilts toward breadth rather than prioritization. The core observation holds regardless: explainability, the property that lets humans trace how a model reached a decision, doesn’t automatically produce human engagement with that explanation. A workforce trained to rubber-stamp explainable outputs is no more resilient than one working with a black box. The governance question worth operationalizing is whether your review processes create genuine friction points, moments where human judgment is actually required to proceed, or whether they’ve become ceremonial approval steps.
The falsification condition for this argument is straightforward: if your organization can demonstrate that human reviewers regularly and consequentially override AI recommendations in high-stakes workflows, and face no institutional penalty for doing so, then the agency-erosion problem isn’t acute for you yet. Most large enterprises can’t demonstrate that. The more useful budget question isn’t whether to fund another compliance audit; it’s whether your next AI deployment contract includes any commitments about preserving override rates, contestability mechanisms, or structured human review that can’t be quietly optimized away in the next efficiency cycle.
Based on reporting from AI governance beyond compliance: Designing systems that protect human agency, originally published 2026-08-27 08:15:00.
