Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products

Synopsis

Within 7 months of the Gender Shades audit, IBM, Microsoft, and Face++ released API updates that cut accuracy disparities (darker-skinned female subgroup error fell 17.7–30.4%), evidencing public disclosure as a governance mechanism.

Keywords

·algorithmic audit ·Gender Shades ·facial analysis ·accountability ·public disclosure

Related entries

• Dynamic calibration of trust and trustworthiness in AI-enabled systems
  2026 · International Journal on Software Tools for Technology Transfer 2026
• Trust in human-AI collaboration in finance: a bibliometric-systematic literature review
  May 8, 2026 · AI & SOCIETY 2026
• A Co-Evolutionary Theory of Human-AI Coexistence: Mutualism, Governance, and Dynamics in Complex Societies
  April 24, 2026 · arXiv
• Personality and Personal AI Agents: A Co-Evolutionary Framework (PACE)
  March 7, 2026 · International Journal on Social and Education Sciences 2026
• Boosting Metacognition in Entangled Human-AI Interaction to Navigate Cognitive-Behavioral Drift
  February 2, 2026 · arXiv
• Human-AI perception: not much different, but some distinct novelties
  January 29, 2026 · The Bottom Line 2026