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Instead of “trust me,” the model says “check for yourself.”
I wonder — can this kind of transparency help rebuild trust in AI? Or does it just expose how uncertain intelligence really is?
Instead of “trust me,” the system says, “check for yourself.”
We’re curious how the HN community sees this: Can trust in AI be engineered through transparency? Or does showing the uncertainty just make it harder to trust?
Thanks for reading — this project isn’t about “AI safety theater.” We’re experimenting with verifiable honesty: every model response carries its own determinacy, deception probability, and ethical weight Instead of “trust me,” the system says, “check for yourself.” We’re curious how the HN community sees this: Can trust in AI be engineered through transparency? Or does showing the uncertainty just make it harder to t…
Also, people tend to be pretty bad at interpreting probabilities.
Thanks for reading — this project isn’t about “AI safety theater.” We’re experimenting with verifiable honesty: every model response carries its own determinacy, deception probability, and ethical weight Instead of “trust me,” the system says, “check for yourself.” We’re curious how the HN community sees this: Can trust in AI be engineered through transparency? Or does showing the uncertainty just make it harder to t…
How does that let you check for yourself, though? Don't people still have to trust that the reported probabilities and weights are both meaningful and correct? Also, people tend to be pretty bad at interpreting probabilities.
Thanks for reading — this project isn’t about “AI safety theater.” We’re experimenting with verifiable honesty: every model response carries its own determinacy, deception probability, and ethical weight Instead of “trust me,” the system says, “check for yourself.” We’re curious how the HN community sees this: Can trust in AI be engineered through transparency? Or does showing the uncertainty just make it harder to t…
How does that let you check for yourself, though? Don't people still have to trust that the reported probabilities and weights are both meaningful and correct? Also, people tend to be pretty bad at interpreting probabilities.