Fitbit Research
Applying health equity for generative AI
Research Skills:
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Literature Review
- Competitive Analysis
- Market Research
- Design Sprint
- Stakeholder Interview
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Differentiation Strategy
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Research Proposal Development
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Communicating research findings across organizations
Student Researcher
PROJECT CONTEXT
How might we promote health equity throughout the design process of generative AI tools?
Identify what actionable guidance for designers, developers, policymakers, and others can lead to health equity-focused generative AI tools.
What product features can promote health equity in existing Fitbit tools?
Identify a set of potential features that can promote health equity through Fitbit's interfaces and devices.
RESEARCH ACTIVITIES
DESK RESEARCH
Led a literature review of internal and external work in health equity, ethical guidelines for generative AI, and guidelines for deploying healthcare in AI settings, to identify the key tenets of health equity and ethical, accountable, and responsible generative AI development.
STAKEHOLDER INTERVIEWS
Held discussions with clinical specialists, generative AI researchers and developers, and health policy specialists at Google to understand work processes, identify opportunities to influence their work, and expectations for health equity guidelines.
DESIGN GUIDELINE DEVELOPMENT
Documented a set of 9 design principles for promoting health equity in the design of generative AI. Distilled actionable guidance for professionals involved in the design processes.
MARKET RESEARCH
Conducted a competitive analysis of over 100+ unique health tracking mobile apps and devices to identify opportunities to offer new health equity features in Fitbit's tools. Summarized content in a presentation deck that included potential populations and health areas to target.
FEATURE GENERATION
Led an internal brainstorming session of researchers and clinical specialists to identify potential features based on proposed populations and health area.
FEATURE PROPOSAL
Developed a comprehensive research plan to create, refine, and evaluate features with current and new users. Included specifications for both qualitative and quantitative methods, as well as alternative methods to support different timelines and resources. Broadly, the research plan included:
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Identifying user needs through desk research of internal and external resources (literature review, user analytics, customer support data, artifact reports and studies).
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Feature refinement with UX Designers and target population (prototyping wireframes, semi-structured interview, and co-design sessions).
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Feature validation with target population using concept testing and pairwise human rater feedback.
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Solidifying feature recommendations and research finding share-out (presenting in several product and project areas at Google, documenting feature recommendations into feature overview, compelling user stories, design and functional specifications, performance metrics, and relevent dependencies and edge cases)
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Product incorporation and longitudinal assessment of feature performance
PRESENTATION
Will ensure future team's success by sharing the research approach, findings, insights, and recommendations in multiple public forums and through shareable and accessible reports available at the end of the internship.