Fairness in AI

We hear a lot about ethics in artificial intelligence (AI). Fairness is the bit that nags most. She makes decisions that affect people, so we can’t shrug off bias as “just math.”

What fairness means

Fairness sounds simple, but it isn’t. We want her to treat different groups the same. Yet “same” depends on context. A hiring model that accepts equal numbers of men and women looks fair. But if she rejects qualified women more often, that’s a different unfairness.

Fairness metrics

So we measure. Demographic parity counts how often groups get the positive outcome. Equalized odds checks if groups have the same error rates. Predictive parity looks at whether her predictions mean the same thing for everyone. Each metric feels reasonable. None works everywhere.

Why it’s tricky

Pick one fairness metric and another may break. Imagine credit scoring. If she gives men and women the same approval rate (parity), error rates may differ. If we fix error rates, the approval gap comes back. That’s why fairness is more trade-off than checkbox.

Mitigation in practice

We can reduce bias, but not erase it. One fix is pre-processing data so it’s balanced before training. Another is adjusting her learning process to weigh groups more equally. A last option is post-processing: tweaking outputs so results align better with fairness goals. These sound tidy, but each adds complexity.

Our coder’s note

We like clean solutions. Fairness isn’t one. She keeps pulling us back to messy trade-offs. Maybe that’s the point—fairness in AI isn’t a destination. It’s a habit of checking, measuring, and adjusting.