# Opening the Scope of Openness in AI

arXiv: https://arxiv.org/abs/2505.06464 · HTML: https://mlsanity.com/paper/2505.06464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 201 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 27 of 27)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Open-Endedness is Essential for Artificial Superhuman Intelligence](https://mlsanity.com/paper/2406.04268/) (2024)
- [The Gradient of Generative AI Release: Methods and Considerations](https://mlsanity.com/paper/2302.04844/) (2023)
- [Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models](https://mlsanity.com/paper/2211.14946/) (2022)
- [Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators](https://mlsanity.com/paper/2307.05532/) (2023)
- [Behavioral Use Licensing for Responsible AI](https://mlsanity.com/paper/2011.03116/) (2020)
- [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)
- [Democratising AI: Multiple Meanings, Goals, and Methods](https://mlsanity.com/paper/2303.12642/) (2023)
- [The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence](https://mlsanity.com/paper/2403.13784/) (2024)
- [The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub](https://mlsanity.com/paper/2405.13058/) (2024)
- [Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity](https://mlsanity.com/paper/2006.07495/) (2020)
- [Learning Factorized Representations for Open-set Domain Adaptation](https://mlsanity.com/paper/1805.12277/) (2018)
- [Risks and Opportunities of Open-Source Generative AI](https://mlsanity.com/paper/2405.08597/) (2024)
- [The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse?](https://mlsanity.com/paper/2001.00463/) (2019)
- [Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings](https://mlsanity.com/paper/2405.16820/) (2024)
- [Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives](https://mlsanity.com/paper/2301.08488/) (2023)
- [Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models](https://mlsanity.com/paper/2312.14751/) (2023)
- [On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI](https://mlsanity.com/paper/2402.05979/) (2024)
- [Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence](https://mlsanity.com/paper/2405.15802/) (2024)
- [A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases](https://mlsanity.com/paper/1305.0377/) (2013)
- [The tension between openness and prudence in AI research](https://mlsanity.com/paper/1910.01170/) (2019)
- [Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donations](https://mlsanity.com/paper/2409.17876/) (2024)
- [A tale of two databases: The use of Web of Science and Scopus in academic papers](https://mlsanity.com/paper/2002.02608/) (2020)
- [Global technology access in biolabs -- from DIY trend to an open source transformation](https://mlsanity.com/paper/2210.08976/) (2022)

## Abstract (arXiv)

The concept of openness in AI has so far been heavily inspired by the definition and community practice of open source software. This positions openness in AI as having positive connotations; it introduces assumptions of certain advantages, such as collaborative innovation and transparency. However, the practices and benefits of open source software are not fully transferable to AI, which has its own challenges. Framing a notion of openness tailored to AI is crucial to addressing its growing societal implications, risks, and capabilities. We argue that considering the fundamental scope of openness in different disciplines will broaden discussions, introduce important perspectives, and reflect on what openness in AI should mean. Toward this goal, we qualitatively analyze 98 concepts of openness discovered from topic modeling, through which we develop a taxonomy of openness. Using this taxonomy as an instrument, we situate the current discussion on AI openness, identify gaps and highlight links with other disciplines. Our work contributes to the recent efforts in framing openness in AI by reflecting principles and practices of openness beyond open source software and calls for a more holistic view of openness in terms of actions, system properties, and ethical objectives.