# Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance

arXiv: https://arxiv.org/abs/2206.11922 · HTML: https://mlsanity.com/paper/2206.11922/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 124 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 18 of 18)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Unsolved Problems in ML Safety](https://mlsanity.com/paper/2109.13916/) (2021)
- [Risks from Learned Optimization in Advanced Machine Learning Systems](https://mlsanity.com/paper/1906.01820/) (2019)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [The AI Index 2021 Annual Report](https://mlsanity.com/paper/2103.06312/) (2021)
- [AI Research Considerations for Human Existential Safety (ARCHES)](https://mlsanity.com/paper/2006.04948/) (2020)
- [The AI Index 2022 Annual Report](https://mlsanity.com/paper/2205.03468/) (2022)
- [AI safety: state of the field through quantitative lens](https://mlsanity.com/paper/2002.05671/) (2020)
- [Linking Artificial Intelligence Principles](https://mlsanity.com/paper/1812.04814/) (2018)
- [Modeling AGI Safety Frameworks with Causal Influence Diagrams](https://mlsanity.com/paper/1906.08663/) (2019)
- [Advance gender prediction tool of first names and its use in analysing gender disparity in Computer Science in the UK, Malaysia and China](https://mlsanity.com/paper/1906.05769/) (2019)
- [Good AI for the Present of Humanity Democratizing AI Governance](https://mlsanity.com/paper/2007.04477/) (2020)

## Built on by (most cited first, 28 of 28)

- [Beyond principlism: Practical strategies for ethical AI use in research practices](https://mlsanity.com/paper/2401.15284/) (2024)
- [Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI](https://mlsanity.com/paper/2411.08881/) (2024)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [AI and Human Oversight: A Risk-Based Framework for Alignment](https://mlsanity.com/paper/2510.09090/) (2025)
- [Combining Insights From Multiple Large Language Models Improves Diagnostic Accuracy](https://mlsanity.com/paper/2402.08806/) (2024)
- [Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping](https://mlsanity.com/paper/2505.13182/) (2025)
- [What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI](https://mlsanity.com/paper/2602.20547/) (2026)
- [On the Efficiency of Ethics as a Governing Tool for Artificial Intelligence](https://mlsanity.com/paper/2210.15289/) (2022)
- [Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions](https://mlsanity.com/paper/2305.11581/) (2023)
- [Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice](https://mlsanity.com/paper/2503.04785/) (2025)
- [Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems](https://mlsanity.com/paper/2506.22774/) (2025)
- [AI Workers, Geopolitics, and Algorithmic Collective Action](https://mlsanity.com/paper/2511.17331/) (2025)
- [To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems](https://mlsanity.com/paper/2604.28053/) (2026)
- [Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools](https://mlsanity.com/paper/2409.11489/) (2024)
- [Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground](https://mlsanity.com/paper/2412.05130/) (2024)
- [When Discourse Stalls: Moving Past Five Semantic Stopsigns about Generative AI in Design Research](https://mlsanity.com/paper/2503.08565/) (2025)
- [Trustworthiness of Legal Considerations for the Use of LLMs in Education](https://mlsanity.com/paper/2508.03771/) (2025)
- [Do Ethical AI Principles Matter to Users? A Large-Scale Analysis of User Sentiment and Satisfaction](https://mlsanity.com/paper/2508.05913/) (2025)
- [OpenConstruction: A Systematic Synthesis of Open Visual Datasets for Data-Centric Artificial Intelligence in Construction Monitoring](https://mlsanity.com/paper/2508.11482/) (2025)
- [Plural Voices, Single Agent: Towards Inclusive AI in Multi-User Domestic Spaces](https://mlsanity.com/paper/2510.19008/) (2025)
- [The SMART+ Framework for AI Systems](https://mlsanity.com/paper/2512.08592/) (2025)
- [Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study](https://mlsanity.com/paper/2512.15791/) (2025)
- [Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA](https://mlsanity.com/paper/2603.24580/) (2026)
- [Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures](https://mlsanity.com/paper/2604.15514/) (2026)
- [Ethics Testing: Proactive Identification of Generative AI System Harms](https://mlsanity.com/paper/2604.22089/) (2026)
- [A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasms](https://mlsanity.com/paper/2607.15480/) (2026)
- [What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research](https://mlsanity.com/paper/2608.10431/) (2026)
- [The Hermon Moment: AI Self-Transcendence and Its Human Narration](https://mlsanity.com/paper/2608.30971/) (2026)

## Abstract (arXiv)

The utilization of artificial intelligence (AI) applications has experienced tremendous growth in recent years, bringing forth numerous benefits and conveniences. However, this expansion has also provoked ethical concerns, such as privacy breaches, algorithmic discrimination, security and reliability issues, transparency, and other unintended consequences. To determine whether a global consensus exists regarding the ethical principles that should govern AI applications and to contribute to the formation of future regulations, this paper conducts a meta-analysis of 200 governance policies and ethical guidelines for AI usage published by public bodies, academic institutions, private companies, and civil society organizations worldwide. We identified at least 17 resonating principles prevalent in the policies and guidelines of our dataset, released as an open-source database and tool. We present the limitations of performing a global scale analysis study paired with a critical analysis of our findings, presenting areas of consensus that should be incorporated into future regulatory efforts. All components tied to this work can be found in https://nkluge-correa.github.io/worldwide_AI-ethics/