Thanks for the preview before I get to reading this book!
The note about overconfidence, and in particular, the failures around high-stakes AI output (ex: when an important decision hinges upon the AI response/output) makes me think of what kind of guardrails can be put in place to prevent misinformation/non-optimal decisions. Some ideas for past projects have included a confidence "score"/meter so users aren't blindly taking decisions at face value (the consensus app shows meter for strength of evidence, for instance), or giving a different type of output based on how strong (or weak) the AI's "match" between its knowledge base and the user's input (ex: giving multiple options for a user to choose from if it's medium confidence; asking users to re-describe or give more info if it has low confidence).
These are exactly the right instincts, and they point to something I think about a lot: the design of the handoff between AI output and human judgment.
Confidence meters and tiered responses are smart interventions. But they only work if the person on the receiving end knows how to interpret them, and if the organizational culture actually slows down when uncertainty is flagged. That’s often the harder problem.
The systems get designed well. The conditions around them don’t.
This is a big part of what I want to unpack in the next few essays. Thanks for reading and sharing your perspective.
Thanks for the preview before I get to reading this book!
The note about overconfidence, and in particular, the failures around high-stakes AI output (ex: when an important decision hinges upon the AI response/output) makes me think of what kind of guardrails can be put in place to prevent misinformation/non-optimal decisions. Some ideas for past projects have included a confidence "score"/meter so users aren't blindly taking decisions at face value (the consensus app shows meter for strength of evidence, for instance), or giving a different type of output based on how strong (or weak) the AI's "match" between its knowledge base and the user's input (ex: giving multiple options for a user to choose from if it's medium confidence; asking users to re-describe or give more info if it has low confidence).
These are exactly the right instincts, and they point to something I think about a lot: the design of the handoff between AI output and human judgment.
Confidence meters and tiered responses are smart interventions. But they only work if the person on the receiving end knows how to interpret them, and if the organizational culture actually slows down when uncertainty is flagged. That’s often the harder problem.
The systems get designed well. The conditions around them don’t.
This is a big part of what I want to unpack in the next few essays. Thanks for reading and sharing your perspective.