# Human Oversight and AI Incident Response
In March 2023, a Samsung engineer pasted proprietary semiconductor source code into ChatGPT to debug it. Three separate leaks happened within twenty days. There was no malicious actor, no adversarial attack, no model failure in the technical sense. The model did exactly what it was designed to do. What failed was oversight—the absence of any operating control between a capable system and the humans using it.
This is the uncomfortable truth of responsible AI at scale: your biggest exposure is rarely a model that breaks. It's a model that works perfectly while nobody is watching how it's used, whether its inputs have shifted, or what happens when its outputs are wrong. Governance frameworks and model cards are table stakes. This lesson is about the operating layer—the part that runs on Monday morning, not the policy that sits in a Confluence page.
Regulators love the phrase "human in the loop." Most implementations are theater. A fraud analyst who approves 2,000 flagged transactions a day is not exercising oversight—they are rubber-stamping at a rate that makes genuine judgment impossible. The EU AI Act's Article 14 demands oversight that lets a human "correctly interpret" and "decide not to use" a system. Automation bias makes that harder than it sounds: people defer to confident machines even when they're wrong, and the more accurate a system usually is, the less scrutiny humans apply to the cases where it isn't.
The CDO's job is to design oversight that matches the *stakes and reversibility* of each decision. Three operating modes:
The failure mode is mismatching the mode to the risk. Placing a human "in the loop" on a system generating 50,000 decisions an hour guarantees rubber-stamping. Removing humans from a decision that can ruin someone's creditworthiness guarantees a regulatory finding.
Oversight you can't measure isn't oversight. Instrument it. Track the override rate (how often humans disagree with the model), the override reversal rate (how often those overrides were later wrong), and decision latency (are reviewers spending real time or milliseconds?). A near-zero override rate is a red flag, not a success metric—it usually means automation bias, not a perfect model. A healthy oversight system shows humans catching a meaningful, stable fraction of edge cases.
Design the interface so the reviewer sees *why* the model made its call: the top features driving the score, a confidence estimate, and the nearest comparable cases. A reviewer staring at a bare score cannot exercise judgment. Give them the context to disagree.
A deployed model is a decaying asset. The world it was trained on moves; the model does not. Your monitoring architecture is what tells you the asset is decaying *before* it costs you.
Distinguish the three things that go wrong, because they demand different responses:
Data drift is a shift in the input distribution. Your income-verification model was trained on pre-pandemic employment patterns; a wave of gig-economy applicants now looks anomalous to it. The model is unchanged; the inputs have moved.
Concept drift is a shift in the relationship between inputs and the correct output. A fraud pattern that was rare becomes common; the very definition of "risky" has changed underneath a static model. This is the dangerous one—accuracy degrades silently while inputs look normal.
Misuse is the Samsung case: the model performing as designed but deployed outside its intended envelope—users pasting sensitive data, prompt injection, or the system being applied to a population it was never validated on.
A practical monitoring stack watches four layers continuously:
monitoring:
input_layer:
- feature_distribution_shift: PSI > 0.2 # population stability index
- null_rate_spike: threshold 3x baseline
output_layer:
- prediction_distribution_shift: alert on >15% move
- confidence_collapse: mean_confidence drop > 10pts
performance_layer:
- accuracy_vs_ground_truth: rolling 7d, alert < SLA
- subgroup_performance_gap: any cohort < 0.9 of overall
usage_layer:
- anomalous_query_patterns: PII/secret detection on inputs
- rate_or_geography_anomaly: flag out-of-envelope useThe subtlety most teams miss: performance monitoring requires ground truth, and ground truth arrives late. You know a loan defaulted months after you approved it. So you monitor leading indicators—input and output distribution shifts—as early warnings, and treat delayed performance metrics as confirmation. Don't wait for the confirmation to act.
The subgroup performance gap line is doing quiet, essential work. Aggregate accuracy can hold steady while performance for a specific demographic quietly collapses. Zillow's iBuying failure in 2021—$500M+ in losses and a 25% workforce cut—was in part a drift story: their pricing models couldn't keep pace with a fast-moving housing market, and the monitoring didn't force a stop early enough. Watch the segmentssegmentsDividing a market into distinct groups of customers who share similar needs, characteristics or behaviours, so each group can be served with a tailored approach.View full definition →, not just the average.
The governance move here is deciding, in advance and in writing, what triggers what. For each production model, define the metric, the alert threshold, the response, and the owner. Do this at deployment review, when nobody is under pressure. In the middle of an incident, every threshold looks negotiable, and that's exactly when you can't afford to negotiate.
Every mature security organization has an incident-response plan. Almost no organization has one for AI. When a model discriminates, hallucinates a defamatory claim, or produces cascading bad decisions, most companies improvise—and improvisation under legal, PR, and regulatory pressure is how a contained problem becomes a crisis.
Borrow the structure from security incident response, but adapt it, because AI incidents have properties security incidents don't: the "vulnerability" may be statistical rather than a bug, the harm may already be distributed across thousands of decisions before you notice, and remediation may mean re-deciding past cases, not just patching forward.
1. Detect and triage. An incident can surface from monitoring, a customer complaint, a journalist, or a regulator. Triage by severity using two axes: *scope* (how many decisions/people affected) and *reversibility* (can we undo the harm?). A hallucinated internal summary is low severity. A biased hiring screen that rejected 4,000 candidates is a Sev-1. Define severity levels in advance with named response times.
2. Contain. The single most important capability you can build before an incident is a kill switch—the ability to disable a model and fail over to a safe fallback (a simpler model, a rules-based system, or full manual review) without a code deployment. If shutting off a misbehaving model requires an engineering sprint, you don't have containment; you have a liability accruing by the minute. Test this quarterly. The question "how fast can we turn this model off?" should have a rehearsed, measured answer.
3. Investigate. Reconstruct what happened. This is where your governance foundation pays off: without model versioning, input logging, and decision provenance, you're guessing. You need to answer *which model version, on which inputs, produced which outputs, affecting whom.* If you can't reproduce the decision, you can't defend it and you can't fix it.
4. Remediate. Fix forward (retrain, adjust thresholds, add guardrails) *and* address the harm already done. This second part is what people forget. If a model wrongly denied benefits to 800 people, remediation includes re-adjudicating those 800 cases and, likely, notifying them. The forward fix protects the future; the backward fix addresses the people already harmed—and is increasingly what regulators demand.
5. Learn and disclose. Run a blameless post-mortem. Then face the disclosure question honestly: regulatory reporting obligations (the EU AI Act requires serious-incident reporting for high-risk systems), contractual duties to customers, and reputational calculus. Decide the disclosure decision-tree *before* you need it, with Legal and Comms already at the table.
The worst time to figure out who's in charge is during the incident. Establish a standing AI incident response function with a clear incident commander (owns the response, not necessarily the most senior person in the room), plus pre-identified leads from data science, legal, communications, and the affected business line. Give them a shared runbook and a communication channel that spins up on Sev-1. Run at least one tabletop exercise a year: simulate a realistic incident—say, a customer-facing chatbot giving dangerous advice—and walk the full cycle. Teams that have rehearsed respond in hours; teams that haven't lose the first day arguing about who owns the problem.
Knowledge check
1. According to the lesson, what was the fundamental failure in the Samsung ChatGPT incident?
2. Why does the lesson argue that a fraud analyst approving 2,000 flagged transactions a day does not constitute meaningful human oversight?
3. What principle should guide a CDO in deciding which oversight mode (HITL vs HOTL) to apply to a given AI decision?
4. Select ALL correct answers about automation bias as described in the lesson.
Select all the correct answers.
5. Select ALL correct answers about the type of AI decision best suited for a human-in-the-loop (HITL) design.
Select all the correct answers.
These three capabilities are not independent programs—they form one loop, and the connective tissue is what separates a responsible-AI *function* from a responsible-AI *slide deck*.
Oversight generates the human signals (overrides, escalations) that feed monitoring. Monitoring generates the automated signals (drift, anomalous usage) that trigger incident response. Incident response generates the learnings that reset oversight modes and monitoring thresholds. When Air Canada's chatbot invented a bereavement-fare policy in 2024 and a tribunal held the airline liable for what its bot said, the failure was the whole loop: no oversight on high-stakes customer commitments, no monitoring of what the bot was asserting, and no response plan for a wrong answer that had already reached a customer.
Two organizing mechanisms make the loop real:
A model risk tier drives everything. Classify each production model by risk tier at deployment. The tier determines the oversight mode, the monitoring intensity, the review cadence, and the incident severity floor. A Tier-1 model (high-stakes, affects individuals' rights or safety) gets HITL or tight HOTL, continuous subgroup monitoring, monthly review, and any incident is Sev-2 minimum. A Tier-3 internal-productivity model gets lightweight monitoring and best-effort response. Don't apply the same rigor everywhere—you'll either burn out on low-risk systems or under-protect high-risk ones. Match control intensity to risk.
A single register of record. Every production model, its tier, its oversight mode, its monitoring thresholds, its kill-switch owner, and its last incident lives in one inventory. If you can't produce this list on demand, you don't know what you're operating, and neither will the regulator who asks. This register is the artifact that connects your governance policy to your production reality—and it's the first thing an auditor will request.
The CDO doesn't personally review overrides or watch drift dashboards. The CDO's job is to ensure the loop exists, is tiered to risk, is instrumented with real metrics, and has been rehearsed under pressure. The organizations that get burned aren't the ones without a policy. They're the ones whose policy was never wired into how the model actually runs on a Tuesday afternoon when something starts to slip.
1. Match the oversight mode (HITL / HOTL / HIC) to the stakes and reversibility of the decision. A human "in the loop" on a high-volume system is rubber-stamping; measure override rate and reversal rate to prove oversight is real, not decorative.
2. Monitor four layers—input, output, performance, and usage—and act on leading indicators. Ground truth arrives late; drift in input and output distributions is your early warning. Watch subgroup performance, not just aggregate accuracy.
3. Build and test a kill switch before you need it. If disabling a misbehaving model requires an engineering sprint, you don't have containment. Rehearse the shutdown quarterly and measure how long it takes.
4. Remediation means fixing forward AND addressing harm already done. Re-adjudicate the wrong decisions and notify affected people—regulators increasingly require it, and it's the part most plans omit.
5. Run one register of record and one annual tabletop exercise. The model inventory (tier, oversight mode, thresholds, kill-switch owner) is what connects policy to production; the tabletop is what turns a written plan into a team that responds in hours, not days.