mlsanityData to 7 Oct 2026

Democratising AI: Multiple Meanings, Goals, and Methods

arXiv 2303.12642 ↗First posted 22 Mar 2023cs.AI cs.CY cs.LGcited by 29

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Abstract (arXiv)

Numerous parties are calling for the democratisation of AI, but the phrase is used to refer to a variety of goals, the pursuit of which sometimes conflict. This paper identifies four kinds of AI democratisation that are commonly discussed: (1) the democratisation of AI use, (2) the democratisation of AI development, (3) the democratisation of AI profits, and (4) the democratisation of AI governance. Numerous goals and methods of achieving each form of democratisation are discussed. The main takeaway from this paper is that AI democratisation is a multifarious and sometimes conflicting concept that should not be conflated with improving AI accessibility. If we want to move beyond ambiguous commitments to democratising AI, to productive discussions of concrete policies and trade-offs, then we need to recognise the principal role of the democratisation of AI governance in navigating tradeoffs and risks across decisions around use, development, and profits.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 10 of its 83 references that are in the map. First posted on arXiv 22 Mar 2023.

Builds on

10 of 83 references.

References · 10

  1. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  2. 2019Release Strategies and the Social Impacts of Language Modelscited by 505
  3. 2020Artificial Intelligence, Values and Alignmentcited by 443
  4. 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
  5. 2022Do Users Write More Insecure Code with AI Assistants?cited by 186
  6. 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
  7. 2022Structured access: an emerging paradigm for safe AI deploymentcited by 40
  8. 2019The Windfall Clause: Distributing the Benefits of AI for the Common Goodcited by 13
  9. 2023'Generative CI' through Collective Response Systemscited by 13
  10. 2019Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learningcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 13; 2025: 4; 2026: 5.

29 papers.

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  1. 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
  2. 2024Visibility into AI Agentscited by 77
  3. 2023Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domainscited by 31
  4. 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectivescited by 31
  5. 2024Speech Translation with Speech Foundation Models and Large Language Models: What is There and What is Missing?cited by 23
  6. 2024The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hubcited by 18
  7. 2024Public Domain 12M: A Highly Aesthetic Image-Text Dataset with Novel Governance Mechanismscited by 15
  8. 2024Acceptable Use Policies for Foundation Modelscited by 12
  9. 2023Going public: the role of public participation approaches in commercial AI labscited by 11
  10. 2023Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Frameworkcited by 11
  11. 2023Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Modelscited by 7
  12. 2024Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creatorscited by 5
17 more
  1. 2024Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donationscited by 4
  2. 2025Misalignment or misuse? The AGI alignment tradeoffcited by 4
  3. 2024Understanding "Democratization" in NLP and ML Researchcited by 3
  4. 2024The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challengescited by 3
  5. 2024Towards Safe Multilingual Frontier AIcited by 2
  6. 2025Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergiescited by 2
  7. 2026Scaling Participation in Modular AI Systemscited by 2
  8. 2023Applying Standards to Advance Upstream & Downstream Ethics in Large Language Modelscited by 0
  9. 2024AccessShare: Co-designing Data Access and Sharing with Blind Peoplecited by 0
  10. 2024Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countriescited by 0
  11. 2024A Research Agenda for Usability and Generalisation in Reinforcement Learningcited by 0
  12. 2025Opening the Scope of Openness in AIcited by 0
  13. 2025SAIL: SRAM-Accelerated LLM Inference System with Lookup-Table-based GEMVcited by 0
  14. 2026Critically Engaged Pragmatism: Scientific Norm and Social, Pragmatist Epistemology for AI Science Evaluation Toolscited by 0
  15. 2026Three Models of RLHF Annotation: Extension, Evidence, and Authoritycited by 0
  16. 2026AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligencecited by 0
  17. 2026Position: The Alignment Community is Unintentionally Building a Censor's Toolkitcited by 0