Same System, Opposite Verdicts: Metric Discretion in AI Ethics Audits and the Limits of Disclosure
This paper demonstrates that the lack of standardized fairness metrics in AI ethics allows for arbitrary and contradictory audit outcomes, arguing that implementing disclosure requirements similar to financial alternative performance measures—rather than mandating a single correct metric—can significantly reduce this discretion and improve transparency.