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Observe, inspect, modify: Three conditions for generative AI governance
16 references·2 citing·46 second hop
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References

  • 2016How the machine ‘thinks’: Understanding opacity in machine learning algorithms3,179 cited
  • 2004States of Knowledge2,918 cited
  • 2019Model Cards for Model Reporting2,653 cited
  • 2019Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions that Shape Social Media1,595 cited
  • 2017Algorithms as culture: Some tactics for the ethnography of algorithmic systems1,010 cited
  • 2002Aircraft Stories383 cited
  • 2020Seeing the forest for the trees: Visualizing platformization and its governance342 cited
  • 2023Auditing large language models: a three-layered approach287 cited
  • 2021The great Transformer: Examining the role of large language models in the political economy of AI191 cited
  • 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them155 cited
  • 2023ChatGPT and the AI Act146 cited
  • 2023AI and Global Governance: Modalities, Rationales, Tensions136 cited
  • 2020Towards platform observability124 cited
  • 2023Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators111 cited
  • 2023The Gradient of Generative AI Release: Methods and Considerations

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90 cited
  • 2023Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI86 cited
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    • 2024Comparative Analysis of Generative AI Risks in the Public Sector37 cited
    • 2025Exploring the Challenges of Generative AI on Public Sector Communication in Europe12 cited