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

arXiv: https://arxiv.org/abs/2009.13676 · HTML: https://mlsanity.com/paper/2009.13676/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 30 of 38)

- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI](https://mlsanity.com/paper/2109.09483/) (2021)
- [Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning](https://mlsanity.com/paper/2302.08476/) (2023)
- [On the Impossible Safety of Large AI Models](https://mlsanity.com/paper/2209.15259/) (2022)
- [The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research](https://mlsanity.com/paper/2305.02797/) (2023)
- [Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics](https://mlsanity.com/paper/2206.03390/) (2022)
- [Position: Key Claims in LLM Research Have a Long Tail of Footnotes](https://mlsanity.com/paper/2308.07120/) (2023)
- [A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms](https://mlsanity.com/paper/2407.01294/) (2024)
- [Metaethical Perspectives on 'Benchmarking' AI Ethics](https://mlsanity.com/paper/2204.05151/) (2022)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Regulation and NLP (RegNLP): Taming Large Language Models](https://mlsanity.com/paper/2310.05553/) (2023)
- [Analyzing the Impact of Companies on AI Research Based on Publications](https://mlsanity.com/paper/2310.20444/) (2023)
- [Real-World Gaps in AI Governance Research](https://mlsanity.com/paper/2505.00174/) (2025)
- [Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice](https://mlsanity.com/paper/2108.06217/) (2021)
- [On Tables with Numbers, with Numbers](https://mlsanity.com/paper/2408.06062/) (2024)
- [Misalignment or misuse? The AGI alignment tradeoff](https://mlsanity.com/paper/2506.03755/) (2025)
- [The Arc of the Data Scientific Universe](https://mlsanity.com/paper/2102.10050/) (2021)
- [Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions](https://mlsanity.com/paper/2312.03912/) (2023)
- [Big Tech influence over AI research revisited: memetic analysis of attribution of ideas to affiliation](https://mlsanity.com/paper/2312.12881/) (2023)
- [Limits to AI Growth: The Ecological and Social Consequences of Scaling](https://mlsanity.com/paper/2501.17980/) (2025)
- [Beyond Weaponization: NLP Security for Medium and Lower-Resourced Languages in Their Own Right](https://mlsanity.com/paper/2507.03473/) (2025)
- [Brainrot: Deskilling and Addiction are Overlooked AI Risks](https://mlsanity.com/paper/2605.03512/) (2026)
- [Irresponsible AI: big tech's influence on AI research and associated impacts](https://mlsanity.com/paper/2512.03077/) (2025)
- [Big AI is accelerating the metacrisis: What can we do?](https://mlsanity.com/paper/2512.24863/) (2025)
- [Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power](https://mlsanity.com/paper/2604.21043/) (2026)
- [Optimists at Heart: Why Do We Research Game AI? (Extended Version)](https://mlsanity.com/paper/2206.03261/) (2022)
- [Deep Learning and Ethics](https://mlsanity.com/paper/2305.15239/) (2023)

## 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.