Summary
As a designer research working on Conversational AI products, I’ve been collaborating with AI Researchers (Computer Scientists) and ML engineers. The cross-pollination of our two research disciplines – i.e., I bring my design thinking, storytelling, qualitative research and thick data analysis lens, while the Computer Scientists bring their logical reasoning, modeling and coding, and big data analysis lens – has resulted in a much smarter and more empathetic AI product, as well as innovations in the Cognitive AI domain. I’ll share three use cases of how we human researchers collaborate with the AI researchers and the lessons learned.
Key Insights
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Collaboration between UX researchers and data scientists is essential for developing effective AI systems.
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Understanding user motivations is critical in shaping AI interactions.
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Research methods should adapt to the evolving landscape of technology rather than sticking rigidly to traditional practices.
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Proactivity and ownership in product development can lead to significant improvements in user experience.
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Human-centered insights are invaluable in informing AI model training and evaluation.
Notable Quotes
"I was still not happy and felt a little powerless."
"The chilling realization that after more than 10 years of doing UX research, I was still only working above the iceberg."
"How can I make bigger impact?"
"As a human researcher, how can I contribute to these fundamental AI research problems?"
"We found that people don't always get straight to their questions."
"The heart and brain of AI are actually connected."
"We've talked about the context to a lot in this conference."
"If it's a machine communicating with the customers, you need to be transparent."
"We need to learn the domain, learn the technology, learn the algorithm."
"Let's all take the driver's seat."
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