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AI systems can appear fair and performant in isolation, yet still harbor hidden instabilities that undermine real-world deployment readiness, demanding a more holistic governance approach.
Fairness assessments in ML are far more subjective than we thought: different metrics can completely flip your conclusion about whether a model is biased.
