Bias, Safety & Human Factors
Where clinical AI really fails — and how to fix it
Beyond headline accuracy: subgroup bias, automation bias and the human-in-the-loop conditions that decide whether AI is safe in practice.
What we assess
A structured safety lens
Subgroup bias
Performance across patient groups and conditions.
Red-team testing
Adversarial scenarios that surface failure modes.
Severity model
Each issue scored for clinical consequence.
Automation bias
Over-reliance and alert-fatigue risks.
Oversight matrix
The human review conditions for safe use.
Remediation
Prioritised fixes and mitigations.
Why it matters
Accuracy on a dataset is not safety in a clinic
Most AI evaluations stop at headline performance on a held-out dataset. But clinical AI fails in ways those numbers never reveal: it can perform worse for an under-represented patient group, behave unpredictably on cases unlike its training data, or be unsafe simply because busy clinicians trust its output too readily. Our review looks at the system the way it will actually be used — the model, the interface, and the human decisions around it — because that triad, not accuracy alone, determines whether a tool is safe.
How a review runs
- Frame the clinical question. Define intended use, the decisions the AI influences, and the patient groups and edge cases that matter most.
- Subgroup and stress testing. Examine performance across relevant subgroups, and run scenario and red-team tests to provoke realistic failure modes.
- Human-factors assessment. Evaluate automation bias, alert fatigue, interface clarity and the practical conditions a clinician needs to override the AI safely.
- Severity scoring and remediation. Score every issue for clinical consequence and hand back a prioritised, actionable plan.
What you get
A clear, written report you can act on and show: subgroup findings, a catalogued and severity-scored set of failure modes, an automation-bias and oversight analysis, and prioritised remediation. It is structured to feed directly into your clinical safety case and your AI governance and oversight, whether you are building the AI or buying it.
Answers
Frequently asked questions
What is a human-factors review for AI?
It assesses how clinicians actually interact with the AI — including automation bias (over-trusting outputs), alert fatigue, and the conditions needed for safe human oversight. It complements statistical performance with real-world use safety.
How do you test for bias?
We analyse performance across relevant patient subgroups and use scenario and red-team testing to surface failure modes, scoring issues against a severity model and recommending remediation.
How does this fit with validation?
It is part of our wider AI validation and feeds the clinical safety case and governance.
Our model scored well on accuracy — isn't that enough?
No. Headline accuracy is measured on a dataset; safety is decided in use. A model can post excellent aggregate metrics yet under-perform for a subgroup, fail on out-of-distribution cases, or be unsafe because clinicians over-trust it. Aggregate accuracy is necessary but never sufficient.
What does the review actually produce?
A written report: subgroup performance findings, a catalogue of failure modes from scenario and red-team testing each scored against a severity model, an automation-bias and oversight assessment, and a prioritised, practical remediation plan. It is designed to drop straight into your clinical safety case and governance.
Can you review a third-party or vendor AI we are buying?
Yes. Independent review is often most valuable on the buy side — we assess a product you are considering and give you an evidence-based view of its real-world safety before you commit.
Stress-test your AI's safety
Request a bias and human-factors review or a governance workshop.