Engagement with safety: Headspace's guardrails for AI tests

At Headspace, VP of Data and Analytics Mary Alfheim argues that experimentation must prioritize member health outcomes alongside engagement, especially because the company combines mindfulness content with clinical care, coaching, therapy, and psychiatry. Her team links shorter-term behavioral signals to longer-term measures such as changes in self-reported anxiety and depression, creating composite metrics that product teams can use in daily decisions. For Ebb, Headspace’s AI conversational companion, every experiment is structured around inputs, performance outputs, and clinical safety guardrails, with clinicians monitoring interactions and escalation needs. Alfheim emphasizes setting explicit stop rules before tests so teams can independently roll out successful changes or halt harmful ones, while developing metrics collaboratively with product partners. She also advocates testing early through small, discrete changes rather than large redesigns, and carefully examining whether experimental treatments are truly randomized, as shown by a payment-processing test affected by differences in historical customer data. Looking ahead, Headspace plans to increase testing while maintaining a portfolio that includes not only routine and high-impact experiments but also exploratory learning tests, recognizing that low win rates can still produce substantial long-term value.

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