Post-Deployment Monitoring as a Safety Proxy in General-Purpose AI

Edge of Practice · Automation · AI Systems · Governance

One-Sentence Assumption Under Test

Post-deployment monitoring and user feedback are sufficient to identify and mitigate harmful emergent behavior in large-scale, general-purpose AI systems.

Why This Assumption Is Tolerated

Continuous monitoring infrastructure and user feedback channels are visible, active, and frequently invoked after deployment. Many observed failures are detected and addressed iteratively, reinforcing confidence in reactive correction. Internal evaluation and red-teaming demonstrate competence in known failure modes, and the absence of immediate catastrophe is interpreted as evidence of sufficiency.

The assumption persists because corrective mechanisms are observable, while undetected harms remain structurally invisible.

Precise Restatement of the Assumption

The organization operates under the belief that post-deployment monitoring systems, supplemented by user feedback, function with sufficient speed, scope, and representativeness to detect and contain all materially harmful emergent behaviors before they propagate or cause irreversible impact. Implied is that feedback channels surface harms reliably and early enough to enable effective intervention. Unstated is the extent to which detection burden is externalized and delayed, and the degree to which undetected or diffuse harms bypass these mechanisms entirely.

Apparent Conditions for Validity — and Their Fragility

This assumption may appear valid when deployment environments are limited in scale, user populations are relatively homogeneous, harms are immediately visible, and organizational response cycles are faster than system propagation.

At global scale, these conditions fail. Use contexts fragment, harms diffuse across populations, feedback becomes selective and delayed, and model behavior propagates faster than governance updates or organizational reflexes.

Structural Failure Modes

Diffuse, Accumulating Harm

Some harms do not manifest as discrete, reportable incidents. They accumulate gradually through bias reinforcement, misinformation normalization, or subtle behavioral influence, remaining below detection thresholds until systemic effects are evident and difficult to reverse.

Observational Blind Spots

Not all affected parties recognize harm, have access to feedback channels, or possess incentives to report. Marginalized, non-dominant, or indirect stakeholders are systematically underrepresented, leaving entire harm classes invisible despite competent monitoring.

Epistemic Boundary

What Can Be Known Pre-Deployment: Documented performance on benchmarks, observed behavior in controlled tests, and known categories of failure.

What Cannot Be Known Until Harm Occurs: Context- specific, adversarial, or emergent behaviors arising in untested real-world environments, particularly those that propagate faster than detection and response mechanisms.

Where certainty ends, assurance cannot legitimately extend.

Disentitlement

On the basis of this assumption, no claim of comprehensive safety, assured benign generalization, or timely harm containment across all real-world contexts is justified. Post-deployment monitoring does not eliminate unknown unknowns, nor does it guarantee early detection of diffuse or systemic harm.

Steward’s Note

Reliance on post-deployment monitoring and external feedback transfers initial detection risk beyond the organization to users, regulators, and society. This displacement delays accountability and allows systemic harm to emerge before recognition. Stewardship requires explicit acknowledgment that this assumption externalizes, rather than contains, risk.

Part of the Edge of Practice short-cycle experiment index.