AI safety: state of the field through quantitative lens
Not on the map. Why?
Abstract (arXiv)
Last decade has seen major improvements in the performance of artificial intelligence which has driven wide-spread applications. Unforeseen effects of such mass-adoption has put the notion of AI safety into the public eye. AI safety is a relatively new field of research focused on techniques for building AI beneficial for humans. While there exist survey papers for the field of AI safety, there is a lack of a quantitative look at the research being conducted. The quantitative aspect gives a data-driven insight about the emerging trends, knowledge gaps and potential areas for future research. In this paper, bibliometric analysis of the literature finds significant increase in research activity since 2015. Also, the field is so new that most of the technical issues are open, including: explainability with its long-term utility, and value alignment which we have identified as the most important long-term research topic. Equally, there is a severe lack of research into concrete policies regarding AI. As we expect AI to be the one of the main driving forces of changes in society, AI safety is the field under which we need to decide the direction of humanity's future.
Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Feb 2020.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 2; 2024: 1; 2025: 1.
9 papers.
- 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 124
- 2022Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governancecited by 28
- 2021Impossibility Results in AI: A Surveycited by 13
- 2022Explainability in reinforcement learning: perspective and positioncited by 11
- 2024Safeguarding AI Agents: Developing and Analyzing Safety Architecturescited by 3
- 2023Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systemscited by 2
- 2021AI Risk Skepticismcited by 1
- 2022Prismal view of ethicscited by 0
- 2025Detecting underdetermination in parameterized quantum circuitscited by 0
- 2025Detecting underdetermination in parameterized quantum circuits4 Apr 2025
- 2024Safeguarding AI Agents: Developing and Analyzing Safety Architectures3 Sep 2024
- 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions30 Oct 2023
- 2023Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems22 Jun 2023
- 2022Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance23 Jun 2022
- 2022Prismal view of ethics26 May 2022
- 2022Explainability in reinforcement learning: perspective and position22 Mar 2022
- 2021Impossibility Results in AI: A Survey1 Sep 2021
- 2021AI Risk Skepticism2 May 2021