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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Designers must focus on what technologies should not be created to preserve human values.
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Research and design are intertwined, and both should influence AI and machine learning tools.
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Machine learning relies on past data and exhibits limitations in ambiguous situations.
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Algorithms dictate machine learning outcomes but can be influenced by bias in data.
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AI systems must be seen as dynamic, involving multi-agency interactions between humans and machines.
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Research methodologies must adapt to consider future-oriented, speculative scenarios in AI.
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Understanding human behavior and cultural context is crucial for effective AI design.
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Ethnography combined with design can help surface complex human interactions with technology.
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Prototypes should provoke thought and explore unexpressed human rituals and values in AI environments.
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Transparency in AI must extend to understanding data inputs and mitigating biases.
Notable Quotes
"I believe our job in an automated future is to determine what not to design."
"Design is a conscious effort to impose meaningful order to chaos."
"Research is not separate from design, but rather embedded within it."
"Machines can learn from data but have limitations in ambiguous situations."
"Human irrationality presents a challenge for machine learning systems to replicate."
"We need to understand what data goes into models to impact their outcomes effectively."
"You have to build in governance to ensure fairness in model development."
"AI is not static; it learns and evolves based on interactions."
"Speculative research is necessary to understand future technology interactions."
"Design anthropology can bridge the gap between human values and technology."
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