# AI safety: state of the field through quantitative lens

arXiv: https://arxiv.org/abs/2002.05671 · HTML: https://mlsanity.com/paper/2002.05671/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance](https://mlsanity.com/paper/2206.11922/) (2022)
- [Impossibility Results in AI: A Survey](https://mlsanity.com/paper/2109.00484/) (2021)
- [Explainability in reinforcement learning: perspective and position](https://mlsanity.com/paper/2203.11547/) (2022)
- [Safeguarding AI Agents: Developing and Analyzing Safety Architectures](https://mlsanity.com/paper/2409.03793/) (2024)
- [Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems](https://mlsanity.com/paper/2306.13686/) (2023)
- [AI Risk Skepticism](https://mlsanity.com/paper/2105.02704/) (2021)
- [Prismal view of ethics](https://mlsanity.com/paper/2205.13370/) (2022)
- [Detecting underdetermination in parameterized quantum circuits](https://mlsanity.com/paper/2504.03315/) (2025)

## 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.