Responsible AI Governance Programme for a Wellness Wearable's Health-Insight Models
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Problem
Unexamined scores steering behaviour
A subscription wellness-wearable platform with roughly 120,000 active members delivers daily recovery and strain scores that visibly steer how people train, sleep and eat. The models behind those scores had no documented intended-use limits, no bias or drift review across member demographics, and no formal release sign-off — leaving product, legal and support teams exposed as scrutiny of consumer health AI grew.
Solution
Governance across every model
We designed and implemented a responsible AI governance programme: a complete model inventory with model cards, documented intended-use and limitation statements, quarterly bias and drift reviews across demographic slices, plain-language in-app score explanations, and a cross-functional review board with a defined sign-off workflow for every new or retrained model.
Measurable Impact
What changed after launch
All 14 production health-insight models catalogued with model cards and documented intended-use limits within 4 months
Quarterly bias and drift reviews now cover 100% of member-facing scores, replacing no formal review
Score-related support tickets fell 31% after in-app 'why this score' explanations shipped to all members
New-model governance sign-off completes in a 10-day review cycle, replacing ad-hoc release decisions
Tech & Tools Used
What powered the build
Ready to Build your Consumer Health & Wellness Business with Responsible AI & Governance
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