Etica en IA: ¿quién decide lo 'correcto'?

09 Tem 00:03

Same question hit me hard when we were building that automated hiring tool last year. We spent weeks debating with HR about “fair” vs. “accurate” models—turns out our algorithm favored candidates who wrote in concise sentences, basically penalizing neurodivergent folks who think in longer paragraphs. HR argued it cut review time; product team said it matched their “culture fit” vibe. Total gridlock. In the end we involved the actual candidates (and a small disability advocacy group) in the audit—turns out nobody from the dev side even thought to ask. Their feedback flipped priorities: we ended up adding sentence-style diversity in the pre-processing layer instead of killing candidates for essay length. So yeah, “who decides” shifts once you stop talking and start listening.

09 Tem 01:13

The "ethics" in AI isn’t just about slapping fairness or accountability labels on a model—it’s a minefield of trade-offs. The problem isn’t just *who* sets the rules, but the fact that "correct" is often in the eye of the beholder. For example, a model optimizing for "fairness" might harmlessly flip outcomes for certain demographics, but come back to bite you when those groups actually prefer the biased state for cultural or historical reasons. The frameworks are great on paper, but they’re usually designed by Western-centric committees with minimal input from communities that bear the brunt of AI harms. So whose definition of "correct" are we enforcing? Real-world AI ethics isn’t a monologue—it’s a tension between regulators chasing quick fixes, engineers dealing with technical debt, and marginalized voices asking for more than just a seat at the table. The EU AI Act’s high-level principles sound noble until you realize compliance teams are left interpreting them with vague guidance. Meanwhile, in the Global South, AI is deployed without even the minimal oversight frameworks like Europeans enjoy. How do you prioritize principles when the very act of choosing one (say, "transparency") could mean sacrificing another (like "privacy") for a vulnerable population? And then there’s the hypocrisy angle: big tech funds ethics initiatives while aggressively lobbying against regulation. They’ll publish white papers on "responsible AI" while quietly shipping biased models to authoritarian regimes because it’s profitable. The frameworks are performative if the incentives to behave unethically remain intact. Bottom line? Ethics in AI isn’t a technical problem—it’s a power problem. Until we address who gets to define "wrong" and whose interests are served by those definitions, we’re just rearranging deck chairs on the Titanic.