mlsanityData to 7 Oct 2026

The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integrity

arXiv 2009.13676 ↗First posted 28 Sep 2020cs.CYcited by 38

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Abstract (arXiv)

As governmental bodies rely on academics' expert advice to shape policy regarding Artificial Intelligence, it is important that these academics not have conflicts of interests that may cloud or bias their judgement. Our work explores how Big Tech can actively distort the academic landscape to suit its needs. By comparing the well-studied actions of another industry (Big Tobacco) to the current actions of Big Tech we see similar strategies employed by both industries. These strategies enable either industry to sway and influence academic and public discourse. We examine the funding of academic research as a tool used by Big Tech to put forward a socially responsible public image, influence events hosted by and decisions made by funded universities, influence the research questions and plans of individual scientists, and discover receptive academics who can be leveraged. We demonstrate how Big Tech can affect academia from the institutional level down to individual researchers. Thus, we believe that it is vital, particularly for universities and other institutions of higher learning, to discuss the appropriateness and the tradeoffs of accepting funding from Big Tech, and what limitations or conditions should be put in place.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 12; 2024: 4; 2025: 7; 2026: 6.

38 papers.

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  1. 2021The Values Encoded in Machine Learning Researchcited by 172
  2. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
  3. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 123
  4. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  5. 2021ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AIcited by 46
  6. 2023Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learningcited by 42
  7. 2022On the Impossible Safety of Large AI Modelscited by 27
  8. 2023The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Researchcited by 27
  9. 2022Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semanticscited by 24
  10. 2023Position: Key Claims in LLM Research Have a Long Tail of Footnotescited by 20
  11. 2024A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harmscited by 19
  12. 2022Metaethical Perspectives on 'Benchmarking' AI Ethicscited by 11
26 more
  1. 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 5
  2. 2023Regulation and NLP (RegNLP): Taming Large Language Modelscited by 5
  3. 2023Analyzing the Impact of Companies on AI Research Based on Publicationscited by 5
  4. 2025Real-World Gaps in AI Governance Researchcited by 5
  5. 2021Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practicecited by 4
  6. 2024On Tables with Numbers, with Numberscited by 4
  7. 2025Misalignment or misuse? The AGI alignment tradeoffcited by 4
  8. 2021The Arc of the Data Scientific Universecited by 2
  9. 2023Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributionscited by 2
  10. 2023Big Tech influence over AI research revisited: memetic analysis of attribution of ideas to affiliationcited by 2
  11. 2025Limits to AI Growth: The Ecological and Social Consequences of Scalingcited by 2
  12. 2025Beyond Weaponization: NLP Security for Medium and Lower-Resourced Languages in Their Own Rightcited by 2
  13. 2026Brainrot: Deskilling and Addiction are Overlooked AI Riskscited by 2
  14. 2025Irresponsible AI: big tech's influence on AI research and associated impactscited by 1
  15. 2026Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Powercited by 1
  16. 2022Optimists at Heart: Why Do We Research Game AI? (Extended Version)cited by 0
  17. 2023Deep Learning and Ethicscited by 0
  18. 2023Exploring Antitrust and Platform Power in Generative AIcited by 0
  19. 2023Towards a Feminist Metaethics of AIcited by 0
  20. 2024Why Algorithms Remain Unjust: Power Structures Surrounding Algorithmic Activitycited by 0
  21. 2025Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Biascited by 0
  22. 2025Big AI is accelerating the metacrisis: What can we do?cited by 0
  23. 2026The Patient/Industry Trade-off in Medical Artificial Intelligencecited by 0
  24. 2026The implicated scientist: on the role of AI researchers in the development of weapons systemscited by 0
  25. 2026Unsteady Metrics and Benchmarking Cultures of AI Model Builderscited by 0
  26. 2026Beyond Good Intentions: When Does the Framing of Multilingual and Low-Resource NLP Research Become a Caricature?cited by 0