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

Goldman Sachs

Goldman Sachs deploys AI at scale through an internal generative AI assistant that draws on multiple external foundation models (OpenAI, Google Gemini, Meta Llama) across employee workflows. Whether Goldman trains proprietary domain models beyond fine-tuning and RAG is not clearly established from public sources.

We reviewed 4 public documents from Goldman Sachs and found it acknowledges 6 of 24 AI risks, including weapons & cyberattacks and discrimination.

Sector: Finance and Insurance

Roles: AI deployer

AI risks Goldman Sachs acknowledges

  • Weapons & cyberattacks: “the use of AI by cybercriminals may increase the frequency and severity of cybersecurity attacks against us or our third-party vendors and clients.” (Goldman Sachs 2024 Form 10-K)
  • Discrimination: “may produce output or take action ... that reflect biases included in the data on which they are trained” (Goldman Sachs 2024 Form 10-K)
  • Devaluation of human creativity: “including risks arising from the inclusion of any unauthorized material in the training data for their models” (Goldman Sachs 2024 Form 10-K)
  • Fraud & scams: “in response to the proliferation of AI-enabled fraud ... we have emphasized phishing and cybersecurity training for our employees” (Goldman Sachs 2024 Form 10-K)
  • Loss of privacy: “may produce output or take action ... that result in the release of private, confidential or proprietary information” (Goldman Sachs 2024 Form 10-K)
  • Transparency & interpretability: “the complexity of many AI models makes it challenging to understand why they are generating particular outputs. This limited transparency increases the challenges associated with assessing the proper operation of AI models” (Goldman Sachs 2024 Form 10-K)

Documents we reviewed