User Research GenAI Better Together
summary
Companies rush to add AI features without users and end up with biased or broken outcomes so specifics matter: a skin‑cancer model trained mostly on lighter tones misses cases in darker skin, Microsoft’s Twitter bot Tay becomes racist and is taken down, a company replaces 90% of support with a two‑day chatbot and later gets a 1.4 Trustpilot rating. Practical tools mentioned include Qualrix theme and sentiment extraction for interviews, MidJourney image generation for personas, Pixel Buds real‑time translation (promising but unproven), and Neurons AI simulated eyetracking.
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chapters (25)
- 0:00Introduction
- 1:24Why user research matters for AI
- 3:10Risks of poorly implemented AI
- 4:52Researcher’s role in AI advocacy
- 6:37Challenging AI adoption assumptions
- 8:23Inclusive data and user testing
- 10:06User privacy in AI features
- 11:09Accessible AI interactions
- 12:31Mitigating negative AI outcomes
- 13:12AI enhancing user research
- 13:54AI tools for brainstorming
- 14:36GenAI for visualization and upskilling
- 15:19Reliable interview transcription
- 16:42Sentiment analysis and theme extraction
- 17:25Generating personas and imagery
- 18:07Content generation and refinement
- 18:29Real-time language translation
- 19:53Participatory design with GenAI
- 21:22Pattern identification and analytics
- 22:24Simulated eyetracking studies
- 23:05AI-driven interviews
- 24:49Synthetic user data
- 25:10Hyperpersonalization and personas
- 26:35Building trust in AI workflows
- 27:18Future outlook
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