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AI Studios with Natalia Burina
👩🏻🔬How to do User Research for AI Products with Lauren M. Kaplan, PhD
May 5, 2023
Lauren M. Kaplan, a mixed methods researcher passionate about inclusion and leveraging technology for social good, discusses the importance of user research for AI product development. They cover topics such as matching user research with product development, the benefits of user research for AI products, the difference between user research and user feedback, challenges of doing user research for AI, and approaches to user research for generative AI.
34:00
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Quick takeaways
- User research for AI helps validate assumptions, identify market opportunities, and align user needs with business goals.
- User research helps bridge the gap between human-centric product development and technical AI researchers by fostering collaboration and empathy.
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Overview of User Research for AI
User research for AI involves the systematic application of qualitative and quantitative research methods to understand user needs, behaviors, and expectations. It combines both qualitative approaches such as one-on-one interviews and diary studies to delve into foundational needs, and quantitative methods like surveys to identify prevalent patterns and examine beliefs associated with desired outcomes. User research is crucial for startups developing generative AI products as it helps validate assumptions, identify market opportunities, inform market strategy, and align user needs with business goals. It is important to differentiate user research from user feedback, as research involves systematic assessment and analysis, while feedback provides a limited pulse on user needs. Privacy considerations and challenges are dominant in AI, requiring research to navigate privacy-preserving machine learning technologies and effectively communicate privacy-related information to users.
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