Engineers say test it and ship it, or shut the lab down. Others imply everything will emerge from a society of agents. Both are partly right, but I think both make a deep error, and it's one that matters for designing Minds for Our Minds.
Our March update focused on our obsession with trust and AI. the premise of our interest is that in order for AI to be useful, we need to know if/when to trust it.
In our update, we covered four key topics:
Why trust in AI is an important topic. Automation bias means we can be primed to rely on and trust in AI beyond what we should.
Trust in AI as a technology. Trust underpins everything about real-world value of AI. We discussed trustworthiness across 8 dimensions and trust in AI across populations and perspectives.
Trust in AI companies. Each major AI company has different strategies, innovations, and weaknesses related to trust.
Trust in AI as a partner. Trust is multi-faceted from relying on someone or something to be capable, aligned, and predictable to trusting someone or something to be ethical, empathetic, and vulnerable.
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Dave Edwards is a Co-Founder of Artificiality. He previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Apple, CRV, Macromedia, Morgan Stanley, Quartz, and ThinkEquity. Contact at dave@artificialityinstitute.org.
Helen Edwards is a Co-Founder of Artificiality. She previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Meridian Energy, Pacific Gas & Electric, Quartz, and Transpower. Contact at helen@artificialityinstitute.org.
The skills that can be taught quickly can be automated quickly. What's left is the slow work universities were built for: helping people become who they are.
The AI Adaptation Cube maps, in three dimensions, the roles we put AI in based on the three traits: Cognitive Permeability, Symbolic Plasticity, and Identity Coupling.
