Company AI Risk Explorer
Loading the data…
Welcome to the Company AI Risk Explorer

This tool maps what large global companies say in public about AI risk. We reviewed public documents from roughly 200 of the world's largest companies. We coded every risk they acknowledge against the AI Risk Repository taxonomy.

Filter by company, sector, AI role, or risk. Compare what companies acknowledge against expert ratings of severity, sector vulnerability, and role responsibility.

← Back to the overview Company AI Risk Explorer

Companies × risks

Unequal performance across groups

Accuracy and effectiveness of AI decisions and actions are dependent on group membership, where decisions in AI system design and biased training data lead to unequal outcomes, reduced benefits, increased effort, and alienation of users.

5 of the world's largest companies we reviewed acknowledge the AI risk "Unequal performance", including Microsoft, IBM and L'Oreal.

Which companies acknowledge this risk?

  • Microsoft: “Identify and document any justifiable factors... that account for: 1) any inability to meet any target minimum performance level for any identified demographic group, and 2) any remaining performance differences between identified demographic groups.” (Microsoft Responsible AI Standard v2 General Requirements)
  • IBM: “darker-skinned females are the most misclassified group (with error rates of up to 35% ). In comparison, the error rates for lighter-skinned were no more than 1%.” (Foundation models: Opportunities, risks and mitigations)
  • L'Oreal: “as the tool does not perform as well for certain specific skin tones” (L'Oreal - CODE OF ETHICS)
  • Mercedes-Benz: “The Mercedes-Benz Group is intensively addressing the potential negative impacts of bias in artificial intelligence on the safety of road users in automated vehicles.” (Mercedes-Benz Annual Report 2025 with integrated Sustainability Report)
  • SAP: “Test multilingual performance and guardrails, accounting for gendered language, low-resource languages, and cultural nuances.” (SAP AI Ethics Handbook May 2026)