Artificial Intelligence: the global landscape of ethics guidelines
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Abstract (arXiv)
In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.
Key facts. Cited by 107 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jun 2019.
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Citing papers per year: 2021: 19; 2022: 19; 2023: 31; 2024: 18; 2025: 12; 2026: 8.
107 papers.
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- 2021Trustworthy AI: From Principles to Practicescited by 207
- 2022The Fallacy of AI Functionalitycited by 88
- 2021Incorporating Domain Knowledge into Deep Neural Networkscited by 56
- 2023ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishingcited by 52
- 2022How Different Groups Prioritize Ethical Values for Responsible AIcited by 49
- 2023GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Modelscited by 42
- 2024Generative Artificial Intelligence: A Systematic Review and Applicationscited by 37
- 2023AI Alignment and Social Choice: Fundamental Limitations and Policy Implicationscited by 36
- 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
- 2022On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbationscited by 32
- 2023Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Surveycited by 30
38 more
- 2022Trust in AI and Its Role in the Acceptance of AI Technologiescited by 29
- 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
- 2022Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governancecited by 28
- 2021Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literaturecited by 27
- 2022From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible MLcited by 27
- 2021Automated scholarly paper review: Concepts, technologies, and challengescited by 21
- 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineeringcited by 20
- 2021Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulationcited by 19
- 2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Reviewcited by 19
- 2021Ethics of AI: A Systematic Literature Review of Principles and Challengescited by 18
- 2021Meaningful human control: actionable properties for AI system developmentcited by 18
- 2023Improving Recommendation Fairness via Data Augmentationcited by 18
- 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
- 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
- 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
- 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
- 2021Filling gaps in trustworthy development of AIcited by 14
- 2022The Conflict Between Explainable and Accountable Decision-Making Algorithmscited by 13
- 2023Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Frameworkcited by 11
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 11
- 2024Generative AI for Requirements Engineering: A Systematic Literature Reviewcited by 11
- 2023fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Taskscited by 10
- 2024Auditing of AI: Legal, Ethical and Technical Approachescited by 10
- 2021FLRA: A Reference Architecture for Federated Learning Systemscited by 9
- 2021Software Engineering for Responsible AI: An Empirical Study and Operationalised Patternscited by 9
- 2022Algorithmic audits of algorithms, and the lawcited by 9
- 2022Towards a Roadmap on Software Engineering for Responsible AIcited by 9
- 2023Human-Centred Learning Analytics and AI in Education: a Systematic Literature Reviewcited by 9
- 2024FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?cited by 9
- 2023Human-Centered Responsible Artificial Intelligence: Current & Future Trendscited by 8
- 2022Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliancecited by 7
- 2024Beyond principlism: Practical strategies for ethical AI use in research practicescited by 7
- 2024The Ethics of AI in Educationcited by 7
- 2022Moral Dilemmas for Moral Machinescited by 6
- 2022A Principles-based Ethics Assurance Argument Pattern for AI and Autonomous Systemscited by 6
- 2022Responsible Artificial Intelligence -- from Principles to Practicecited by 6
- 2022Persona-Based Conversational AI: State of the Art and Challengescited by 6
- 2023Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspectscited by 6
50 most cited of 107 All 107 (JSON)
- 2026Artificial Intelligence Literacy and Sustainable Development: An Ethical Governance and Development Goals Framework9 Sep 2026
- 2026D2VBench: Benchmarking Large Language Models with Value Dilemmas in Daily Scenarios22 Jul 2026
- 2026Exploring the relationship between human-centric AI and firm idiosyncratic risks23 Jun 2026
- 2026Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients2 Jun 2026
- 2026StackingNet: Collective Inference Across Independent AI Foundation Models14 Feb 2026
- 2026Legal Infrastructure for Transformative AI Governance1 Feb 2026
- 2026Responsible AI: The Good, The Bad, The AI28 Jan 2026
- 2026The Power of 10: New Rules for the Digital World7 Jan 2026
- 2025Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study16 Dec 2025
- 2025Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research25 Nov 2025
- 2025In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions17 Oct 2025
- 2025Do Ethical AI Principles Matter to Users? A Large-Scale Analysis of User Sentiment and Satisfaction8 Aug 2025
38 more
- 2025Towards Transparent Ethical AI: A Roadmap for Trustworthy Robotic Systems7 Aug 2025
- 2025Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems28 Jun 2025
- 2025Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometrics12 Jun 2025
- 2025Does AI and Human Advice Mitigate Punishment for Selfish Behavior? An Experiment on AI ethics From a Psychological Perspective26 May 2025
- 2025A Survey of Social Cybersecurity: Techniques for Attack Detection, Evaluations, Challenges, and Future Prospects6 Apr 2025
- 2025Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidents28 Mar 2025
- 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives18 Mar 2025
- 2025A Capability Approach to AI Ethics10 Jan 2025
- 2024Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice19 Dec 2024
- 2024TACOMORE: Exploring a replicable prompting protocol for LLM-assisted corpus analysis13 Dec 2024
- 2024The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems24 Sep 2024
- 2024Generative AI for Requirements Engineering: A Systematic Literature Review10 Sep 2024
- 2024Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models18 Jul 2024
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations12 Jul 2024
- 2024Auditing of AI: Legal, Ethical and Technical Approaches7 Jul 2024
- 2024Machine learning in business process management: A systematic literature review26 May 2024
- 2024Unraveling overoptimism and publication bias in ML-driven science23 May 2024
- 2024Generative Artificial Intelligence: A Systematic Review and Applications17 May 2024
- 2024Embedding Privacy in Computational Social Science and Artificial Intelligence Research17 Apr 2024
- 2024The Ethics of AI in Education22 Mar 2024
- 2024Trust in AI: Progress, Challenges, and Future Directions12 Mar 2024
- 2024Speaker-Independent Dysarthria Severity Classification using Self-Supervised Transformers and Multi-Task Learning29 Feb 2024
- 2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Review13 Feb 2024
- 2024From Algorithm Worship to the Art of Human Learning: Insights from 50-year journey of AI in Education5 Feb 2024
- 2024Beyond principlism: Practical strategies for ethical AI use in research practices27 Jan 2024
- 2024FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?19 Jan 2024
- 2023Human-Centred Learning Analytics and AI in Education: a Systematic Literature Review20 Dec 2023
- 2023Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics13 Dec 2023
- 2023Survey on AI Ethics: A Socio-technical Perspective28 Nov 2023
- 2023Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework18 Nov 2023
- 2023Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms12 Nov 2023
- 2023Towards a Feminist Metaethics of AI10 Nov 2023
- 2023Analyzing the Impact of Companies on AI Research Based on Publications31 Oct 2023
- 2023AI Alignment and Social Choice: Fundamental Limitations and Policy Implications24 Oct 2023
- 2023GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models1 Oct 2023
- 2023Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey25 Sep 2023
- 2023Unravelling Responsibility for AI4 Aug 2023
- 2023The Ethics of AI Value Chains31 Jul 2023
50 newest of 107 All 107 (JSON)
- 2021Trustworthy AI: From Principles to Practicescited by 207
- 2022The Fallacy of AI Functionalitycited by 88
- 2021Incorporating Domain Knowledge into Deep Neural Networkscited by 56
- 2023ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishingcited by 52
- 2022How Different Groups Prioritize Ethical Values for Responsible AIcited by 49
- 2023GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Modelscited by 42
- 2024Generative Artificial Intelligence: A Systematic Review and Applicationscited by 37
- 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
- 2022On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbationscited by 32
- 2023Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Surveycited by 30
- 2022Trust in AI and Its Role in the Acceptance of AI Technologiescited by 29
12 most cited of 106 All 106 (JSON)
- 2026Artificial Intelligence Literacy and Sustainable Development: An Ethical Governance and Development Goals Framework9 Sep 2026
- 2026D2VBench: Benchmarking Large Language Models with Value Dilemmas in Daily Scenarios22 Jul 2026
- 2026Exploring the relationship between human-centric AI and firm idiosyncratic risks23 Jun 2026
- 2026Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients2 Jun 2026
- 2026StackingNet: Collective Inference Across Independent AI Foundation Models14 Feb 2026
- 2026Legal Infrastructure for Transformative AI Governance1 Feb 2026
- 2026Responsible AI: The Good, The Bad, The AI28 Jan 2026
- 2026The Power of 10: New Rules for the Digital World7 Jan 2026
- 2025Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study16 Dec 2025
- 2025Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research25 Nov 2025
- 2025In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions17 Oct 2025
- 2025Do Ethical AI Principles Matter to Users? A Large-Scale Analysis of User Sentiment and Satisfaction8 Aug 2025
12 newest of 106 All 106 (JSON)