Company AI Risk Explorer
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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.

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Companies × risks

Eli Lilly

Eli Lilly develops specialised AI models for drug discovery through its TuneLab platform, trained on years of internal R&D data valued at over $1 billion, and is investing in dedicated AI supercomputing with NVIDIA. It also deploys third-party generative AI (OpenAI) and partner discovery platforms.

We reviewed 3 public documents from Eli Lilly and found it acknowledges 4 of 24 AI risks, including discrimination and loss of privacy.

Sector: Agriculture, Mining, Construction and Manufacturing; Chemicals & pharmaceutical manufacturing

Roles: Specialized AI developer; AI deployer

AI risks Eli Lilly acknowledges

  • Discrimination: “The algorithms and models utilized in AI systems may have limitations, including biases” (Eli Lilly 2024 Annual Report)
  • Loss of privacy: “people, their activities, and health state can be gleamed from insignificant-looking data captured by commercially available devices including cell phones and smart watches.” (Lilly AI Policy and Implications for Digital Health and Drug Development)
  • Capability & robustness: “AI-generated content, analyses, or recommendations we utilize could be deficient... there are technical challenges associated with achieving desired levels of accuracy, efficiency, and reliability. The algorithms and models utilized in AI systems may have limitations, including biases, errors, or inability to handle certain data types or scenarios” (Eli Lilly 2024 Annual Report)
  • Transparency & interpretability: “The algorithms and models utilized in AI systems may have limitations, including biases, errors, or inability to handle certain data types or scenarios or to render explainable outputs.” (Eli Lilly 2024 Annual Report)

Documents we reviewed