Human-AI Interaction Assessment
GMLP Principle 7. Test the device where it will actually be used: by the actual users, in their actual workflow, with realistic interpretive context.
Overview
A model that performs well in isolation often produces worse decisions in deployed workflow: through overreliance, alert fatigue, or misinterpretation of model output. Human factors is not a checkbox; it is core to safety and effectiveness.
We run human-AI interaction assessment grounded in the deployment workflow, the realistic user population, and the foreseeable misuse patterns.
Our Process
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1
Workflow ethnography
Observe target users in their actual workflow.
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2
Use-error analysis
Foreseeable use errors and misinterpretations cataloged.
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3
Simulated deployment study
Users interact with device in realistic context.
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4
Outcome measurement
Decision quality, time, confidence, satisfaction.
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5
Mitigation design
Labeling, UI changes, training, workflow integration.
Frequently Asked Questions
Is this redundant with HFE / usability engineering?
Coordinated, not redundant, focused on AI-specific interaction risks.
How do you handle overreliance?
Designed measurements and mitigations.
Standards alignment?
IEC 62366, FDA HFE guidance, IMDRF GMLP.
Can this delay submission?
It can but usually saves time by surfacing issues early.
Test the device in the workflow.
Send us your intended use and target users. We will return an interaction assessment plan within three weeks.
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