Summary
Ovetta will talk with us about reinvigorating the practice by incorporating Design Anthropology into our research tool-kits and further broadening our set of methodologies to include new research methods for AI/ML design.
Key Insights
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Design and research are inseparable and symbiotic in AI system development.
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In an automated future, deciding what not to design is as important as what to design.
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Machine learning models rely on historical data and lack active environmental interaction.
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AI systems introduce multi-agency contexts where humans and machines share control.
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Traditional software is static and task-based; AI is dynamic and decision-based.
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Researchers must be highly literate in AI and data to influence fairness and reduce bias.
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Design anthropology helps surface human values, culture, and rituals for AI design.
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Explainability in AI models is critical but challenging, especially with deep learning.
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Model governance provides external auditing to ensure fairness and equity in AI.
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Future-oriented, speculative research is essential as users often can’t articulate AI needs upfront.
Notable Quotes
"Design is the conscious and intuitive effort to impose meaningful order to chaos."
"Research is design; they are two sides of the same coin."
"Our role as designers is to determine what technology should not do, not just what it can do."
"Machine learning focuses on problem solving based on past observation, not active interaction."
"The relationship between users and AI is multi-agency: the user, the machine, and the environment all have agency."
"Traditional software interaction is one-way; AI systems require two-way communication between user and machine."
"Nuance is the enemy of machine learning and AI."
"You cannot affect the outcome of a model at the end; you have to influence data collection first."
"Explainability means turning AI from a black box into a glass box through model governance."
"We must seek unexpressed rituals, cultures, values, and irrational practices that make us distinctly human."
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