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7.1.1. Why Alerts Often Fail to Trigger Action

Why Alerts Often Fail to Trigger Action

“The system raised the flag. The institution ignored it.”

In many high-risk AI deployments, failure is not the result of a blind model. It’s the result of a blind process, where risk is detected but no one is positioned, authorized, or expected to respond.

Most AI systems today include at least basic logging and alerting capabilities. They monitor inputs, track drift, and surface anomalies. But these detection mechanisms often feed into dead-end oversight, unclaimed alerts, unreviewed logs, or siloed dashboards that never trigger meaningful response.

In practice, this creates a governance illusion: the organization can say the system was monitored, even though nothing changed when risk emerged. Detection alone does not produce trust, it must be linked to authority.

An alert is not protection unless it obligates someone to act.

Governance Breakdown by Design

In Section 2.1.3, we examined the Dutch SyRI system, where thousands of citizens were flagged as welfare fraud risks through opaque algorithmic scoring. That failure stemmed from transparency and rights violations.

But there’s another layer: the system kept running even after civil society complaints, legal warnings, and evidence of disproportionate harm. Oversight mechanisms existed in form, but not in function. No official was designated to suspend the tool or force a policy pause.

This kind of failure is not technical. It’s structural. And it’s increasingly common.

Structural Oversight Without Response Authority

Several real-world AI systems have demonstrated this pattern:

  • Internal audit teams flag system bias, but have no power to escalate.
  • Users submit complaints, but no SLA exists to trigger review.
  • Dashboards light up, but operators assume someone else is responsible.

In these cases, the monitoring stack is present, but no one is accountable for action.

This problem is especially acute when we consider that not all AI harms emerge as disasters. Many begin as (1)AI hazards, subtle, detectable risks that could plausibly lead to incidents. According to the OECD AI Incidents and Hazards Monitor, a system failure becomes an incident when it results in actual harm (e.g., rights violations or safety breaches), but may linger as a hazard for weeks or months beforehand1.

  1. A potential source of harm from an AI system that has not yet materialized as an incident. (OECD AI Incident Monitor Glossary, 2024)

A hazard ignored becomes an incident with paperwork.

To prevent this failure mode, organizational AI governance must include:

  • Bound escalation roles: Specific people tied to specific types of system behavior
  • Action thresholds: Predefined conditions under which intervention is triggered
  • Intervention authority: Legal and operational mandates to pause or override AI decisions

Without these elements, monitoring becomes ceremonial. Risks are observed but uncontained.

A system can record its own harm and still be considered “compliant”, if no one is asked to stop it.

Shifting the Oversight Paradigm

Trustworthy AI requires a new form of operational oversight. Instead of treating governance as paperwork or documentation, it must be framed as a system with inputs, thresholds, and designated control points.

Just as industrial safety protocols define stop-work triggers in physical systems, AI deployments must define intervention logic at the organizational level. The failure to build this logic is no different from removing the brake pedal from a moving vehicle.

In the next section, we move from diagnosis to design: how to turn passive alerts into meaningful control, using thresholds, triggers, and fallback roles that make oversight actionable.

Bibliography

  1. OECD.AI. (2024). Defining AI incidents and related terms. https://oecd.ai/en/monitor/incidents ↩