# Democratising AI: Multiple Meanings, Goals, and Methods

arXiv: https://arxiv.org/abs/2303.12642 · HTML: https://mlsanity.com/paper/2303.12642/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 83 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [The Alignment Problem from a Deep Learning Perspective](https://mlsanity.com/paper/2209.00626/) (2022)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [The Gradient of Generative AI Release: Methods and Considerations](https://mlsanity.com/paper/2302.04844/) (2023)
- [Structured access: an emerging paradigm for safe AI deployment](https://mlsanity.com/paper/2201.05159/) (2022)
- [The Windfall Clause: Distributing the Benefits of AI for the Common Good](https://mlsanity.com/paper/1912.11595/) (2019)
- ['Generative CI' through Collective Response Systems](https://mlsanity.com/paper/2302.00672/) (2023)
- [Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning](https://mlsanity.com/paper/1907.11274/) (2019)

## Built on by (most cited first, 29 of 29)

- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024)
- [Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains](https://mlsanity.com/paper/2309.11285/) (2023)
- [Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives](https://mlsanity.com/paper/2311.09227/) (2023)
- [Speech Translation with Speech Foundation Models and Large Language Models: What is There and What is Missing?](https://mlsanity.com/paper/2402.12025/) (2024)
- [The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub](https://mlsanity.com/paper/2405.13058/) (2024)
- [Public Domain 12M: A Highly Aesthetic Image-Text Dataset with Novel Governance Mechanisms](https://mlsanity.com/paper/2410.23144/) (2024)
- [Acceptable Use Policies for Foundation Models](https://mlsanity.com/paper/2409.09041/) (2024)
- [Going public: the role of public participation approaches in commercial AI labs](https://mlsanity.com/paper/2306.09871/) (2023)
- [Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework](https://mlsanity.com/paper/2311.14711/) (2023)
- [Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models](https://mlsanity.com/paper/2312.14751/) (2023)
- [Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creators](https://mlsanity.com/paper/2409.00252/) (2024)
- [Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donations](https://mlsanity.com/paper/2409.17876/) (2024)
- [Misalignment or misuse? The AGI alignment tradeoff](https://mlsanity.com/paper/2506.03755/) (2025)
- [Understanding "Democratization" in NLP and ML Research](https://mlsanity.com/paper/2406.11598/) (2024)
- [The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges](https://mlsanity.com/paper/2406.18900/) (2024)
- [Towards Safe Multilingual Frontier AI](https://mlsanity.com/paper/2409.13708/) (2024)
- [Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies](https://mlsanity.com/paper/2505.22829/) (2025)
- [Scaling Participation in Modular AI Systems](https://mlsanity.com/paper/2606.07812/) (2026)
- [SAIL: SRAM-Accelerated LLM Inference System with Lookup-Table-based GEMV](https://mlsanity.com/paper/2509.25853/) (2025)
- [Applying Standards to Advance Upstream & Downstream Ethics in Large Language Models](https://mlsanity.com/paper/2306.03503/) (2023)
- [AccessShare: Co-designing Data Access and Sharing with Blind People](https://mlsanity.com/paper/2407.19351/) (2024)
- [Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries](https://mlsanity.com/paper/2409.14181/) (2024)
- [A Research Agenda for Usability and Generalisation in Reinforcement Learning](https://mlsanity.com/paper/2412.16970/) (2024)
- [Opening the Scope of Openness in AI](https://mlsanity.com/paper/2505.06464/) (2025)
- [Critically Engaged Pragmatism: Scientific Norm and Social, Pragmatist Epistemology for AI Science Evaluation Tools](https://mlsanity.com/paper/2601.09753/) (2026)
- [Three Models of RLHF Annotation: Extension, Evidence, and Authority](https://mlsanity.com/paper/2604.25895/) (2026)
- [AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence](https://mlsanity.com/paper/2605.16291/) (2026)
- [Position: The Alignment Community is Unintentionally Building a Censor's Toolkit](https://mlsanity.com/paper/2608.12346/) (2026)

## 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.