| Audience: | CIO · CTO · CISO |
| Primary Sectors: | Healthcare Systems · Insurance |
| Decision Horizon: | Immediately, before explainability metrics are accepted as evidence that human review works |
Executive Summary
An AI system can become easier for people to understand without making them better at recognizing when its recommendation is wrong. Controlled experiments consistently show that understanding an AI system is not the same as knowing when to trust it. Greater transparency, explanations, and confidence cues can help people understand the AI better or feel more calibrated about its abilities, but they often fail to improve people's ability to catch its errors, and can even make people more likely to accept incorrect advice.1,2,3
Most of this evidence comes from controlled studies using comparatively low-stakes tasks and non-specialist participants. It therefore does not establish how large the same effect will be among clinicians, underwriters, claims professionals, or other expert reviewers. What it does establish is that explanation and understanding cannot safely be assumed to prove effective discrimination.
Decision Posture: Do not accept explanation availability, explanation-view rates, user-reported understanding, confidence in the AI, or the presence of an override mechanism as evidence that effective human oversight exists. If explainability is being used to support an oversight claim, require a simple behavioral check using representative known AI failures and measure whether reviewers distinguish advice that should be followed from advice that should be rejected.
The burden of proof should sit with the oversight claim. If an organization is relying on human review as a safeguard, it should demonstrate that reviewers can distinguish good AI advice from bad—not infer that capability from explanation access or reported understanding.
This is not an argument against explanation. Explanations can improve appropriate reliance when they materially reduce the effort needed to verify an AI recommendation.4 The mistake is measuring explanation success and calling it oversight success.
Our Analysis
The common assumption is a neat sequence: more transparency produces better understanding; better understanding produces better judgment and; better judgment produces better oversight. Experimental evidence shows that this relationship is not reliably established.
The Narrative vs The Reality
The explainable-AI narrative often assumes that showing users more about how a model reached its recommendation will help them judge when to trust it. That's a reasonable goal to design for. But controlled studies show it shouldn't be mistaken for evidence that users actually get better at catching the AI's errors.1,2
- Understanding can improve while error correction deteriorates. Poursabzi-Sangdeh and colleagues found that participants presented with clearer, simpler models became better at simulating model predictions, yet greater transparency did not reliably improve reliance and, in one condition, made participants less able to detect and correct large model errors.1
- Explanations can increase acceptance without improving discrimination. Bansal and colleagues found that explanations increased the likelihood that users accepted AI recommendations regardless of whether those recommendations were correct; explanations did not improve complementary human-AI team performance.2
- Subjective calibration can move in the right direction while behavior moves in the wrong one. A recent 2026 preregistered LLM study of 495 participants found that visual confidence indicators improved users' subjective sensitivity to AI accuracy while simultaneously increasing agreement with incorrect outputs. Combining visual and verbal uncertainty cues produced the highest overreliance and the lowest independent verification rates.3
- But explanation is not the problem in itself. Across five studies involving 731 participants, Vasconcelos and colleagues found that explanations could reduce overreliance when they sufficiently lowered the effort required to verify the AI's prediction.4
The studies invalidate the proxy; they do not predict the size of the effect in expert populations.
The Signal in the Noise
An explainability dashboard can look healthy while the organization's ability to reject bad AI advice remains unmeasured.
What Changes the Decision
Treat explainability as an input to oversight, not a measure of oversight. An explanation earns oversight value only when there is evidence that users become better at discriminating between recommendations that should and should not be followed.
That means explanation exposure, reported understanding, confidence, and access to an override should remain secondary diagnostics. None should be allowed to stand in for observed decision behavior.
The behavioral check proposed here is a challenge to the proxy, not a complete certification methodology. Acceptable thresholds, sampling design, error prevalence, reviewer burden, and assurance criteria must reflect the specific workflow and sit beyond this article’s scope.
Why This Matters Now
Healthcare Systems: The distinction becomes material when clinical-decision-support workflows rely on the proposition that the clinician remains capable of independent judgment. FDA guidance for certain non-device clinical decision support specifically emphasizes enabling a healthcare professional to independently review the basis for a recommendation so they do not rely primarily on it.5 Showing that the clinician can see the basis, however, does not demonstrate that they will identify a plausible but erroneous recommendation. Where independent clinical judgment is part of the justification for the workflow, explanation access alone is therefore insufficient evidence.
Insurance: The same proxy problem appears differently in underwriting, claims and fraud workflows. NAIC's current AI overview identifies AI use across underwriting, pricing, claims handling and fraud detection, while also emphasizing the continuing role of underwriters, claims professionals and other humans in reviewing information and exercising judgment.6 If human review is the claimed safeguard around an AI-supported decision, formal authority and explanation visibility do not establish that the reviewer can distinguish bad advice from good.
What to Watch for Next
In Healthcare, watch for “independent clinical review” being evidenced primarily through explanation access rather than demonstrated rejection of erroneous advice. In Insurance, watch for human sign-off or explainability being treated as proof that underwriting, claims or fraud decisions remain meaningfully human-controlled.
Recommended Actions
Do This
- Remove explainability proxies from the oversight proof column. At the next AI review, the CIO or CTO should require that explanation availability, explanation-view rates, reported understanding, AI confidence, and override availability be labelled as descriptive or usability measures, not evidence that oversight works. The artifact is a simple evidence rule in the review template: “Explanation observed ≠ oversight demonstrated.”
- Healthcare: test the claim of independent clinical judgment, not merely access to rationale. Where a clinical-decision-support workflow relies on a clinician independently reviewing an AI recommendation, the clinical technology owner with the CIO/CTO should require representative erroneous recommendations in the oversight check before treating explanation as evidence of effective review. Record whether clinicians accept correct recommendations and reject incorrect ones. If the evidence demonstrates only that users can understand or restate the basis for the recommendation, the independent-review claim remains unproven.
- Insurance: make human-review claims earn their status in the workflow that matters. Where an insurer relies on a human reviewer as the safeguard around AI-supported underwriting, claims or fraud decisions, the technology owner with the accountable business owner should test representative correct and erroneous recommendations from that workflow. Do not count explanation visibility or formal sign-off authority as proof of oversight unless reviewers actually discriminate between advice that should and should not be followed.
Avoid This
- Turning “the reviewer saw the explanation” into “the reviewer exercised oversight.” In Healthcare, access to the basis of a recommendation does not itself demonstrate independent clinical judgment; in Insurance, a rationale plus human sign-off does not demonstrate effective challenge.
- Replacing explanation rate with override rate. More rejection is not automatically better oversight; the target is discrimination between good and bad AI advice, not maximum skepticism.
- Responding by stripping explanations out of AI systems. The evidence does not support an anti-explainability conclusion. The relevant question is whether the explanation helps the user determine when the recommendation should be followed.
Bottom Line
If your evidence stops at “the human saw and understood the explanation,” you have measured comprehension, not oversight. An oversight claim is not credible if you have not shown that people can distinguish when AI advice should (and should not) be followed.
Evidence and Sources
- Poursabzi-Sangdeh, Forough, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2021. Manipulating and Measuring Model Interpretability. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. DOI 10.1145/3411764.3445315.
- Bansal, Gagan, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel S. Weld. 2021. Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. DOI 10.1145/3411764.3445717.
- Ojewale, Victor, Julia Ryan, Suresh Venkatasubramanian, and C. Malik Boykin. 2026. More Is Not Better: Visual Uncertainty Cues and the Fragility of Trust Calibration in LLM-Assisted Decision Making. Computers in Human Behavior: Artificial Humans 8:100307. DOI 10.1016/j.chbah.2026.100307.
- Vasconcelos, Helena, Matthew Jörke, Madeleine Grunde-McLaughlin, Tobias Gerstenberg, Michael S. Bernstein, and Ranjay Krishna. 2023. Explanations Can Reduce Overreliance on AI Systems During Decision-Making. Proceedings of the ACM on Human-Computer Interaction 7 (CSCW1): 1–38. DOI 10.1145/3579605.
- U.S. Food and Drug Administration. 2026. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff. FDA guidance emphasizes that certain CDS software functions should enable healthcare professionals to independently review the basis for recommendations rather than rely primarily on those recommendations.
- National Association of Insurance Commissioners. 2026. Artificial Intelligence. Updated April 3, 2026. The NAIC identifies AI use in underwriting, pricing, claims handling and fraud detection and describes human review and judgment as continuing elements of insurance decision-making.