mlsanityData to 7 Oct 2026

Understanding artificial intelligence ethics and safety

arXiv 1906.05684 ↗First posted 11 Jun 2019cs.CY cs.AI cs.LGcited by 68

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Abstract (arXiv)

A remarkable time of human promise has been ushered in by the convergence of the ever-expanding availability of big data, the soaring speed and stretch of cloud computing platforms, and the advancement of increasingly sophisticated machine learning algorithms. Innovations in AI are already leaving a mark on government by improving the provision of essential social goods and services from healthcare, education, and transportation to food supply, energy, and environmental management. These bounties are likely just the start. The prospect that progress in AI will help government to confront some of its most urgent challenges is exciting, but legitimate worries abound. As with any new and rapidly evolving technology, a steep learning curve means that mistakes and miscalculations will be made and that both unanticipated and harmful impacts will occur. This guide, written for department and delivery leads in the UK public sector and adopted by the British Government in its publication, 'Using AI in the Public Sector,' identifies the potential harms caused by AI systems and proposes concrete, operationalisable measures to counteract them. It stresses that public sector organisations can anticipate and prevent these potential harms by stewarding a culture of responsible innovation and by putting in place governance processes that support the design and implementation of ethical, fair, and safe AI systems. It also highlights the need for algorithmically supported outcomes to be interpretable by their users and made understandable to decision subjects in clear, non-technical, and accessible ways. Finally, it builds out a vision of human-centred and context-sensitive implementation that gives a central role to communication, evidence-based reasoning, situational awareness, and moral justifiability.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2019.

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Citing papers per year: 2021: 12; 2022: 15; 2023: 13; 2024: 8; 2025: 18; 2026: 2.

68 papers.

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  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
  3. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  4. 2023Large AI Models in Health Informatics: Applications, Challenges, and the Futurecited by 61
  5. 2021Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Reviewcited by 30
  6. 2022Counterfactual harmcited by 29
  7. 2024A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harmscited by 19
  8. 2022Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributescited by 17
  9. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  10. 2024A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecastingcited by 14
  11. 2021Towards Accountability in the Use of Artificial Intelligence for Public Administrationscited by 12
  12. 2024Explainable Artificial Intelligence for Medical Applications: A Reviewcited by 12
38 more
  1. 2022Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applicationscited by 11
  2. 2022Deep Learning Opacity in Scientific Discoverycited by 9
  3. 2022Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcarecited by 9
  4. 2022A Principles-based Ethics Assurance Argument Pattern for AI and Autonomous Systemscited by 6
  5. 2022Automatic detection of glaucoma via fundus imaging and artificial intelligence: A reviewcited by 5
  6. 2025Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practicecited by 5
  7. 2021Co-evolutionary hybrid intelligencecited by 4
  8. 2022GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)cited by 4
  9. 2021A Taxonomy of Explainable Bayesian Networkscited by 3
  10. 2022Towards an Accountable and Reproducible Federated Learning: A FactSheets Approachcited by 3
  11. 2022Artificial intelligence in government: Concepts, standards, and a unified frameworkcited by 3
  12. 2025Ethical AI: Towards Defining a Collective Evaluation Frameworkcited by 3
  13. 2021The Arc of the Data Scientific Universecited by 2
  14. 2022Data Justice in Practice: A Guide for Developerscited by 2
  15. 2023Appropriateness is all you need!cited by 2
  16. 2023Looking deeper into interpretable deep learning in neuroimaging: a comprehensive surveycited by 2
  17. 2023Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applicationscited by 2
  18. 2024Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Educationcited by 2
  19. 2024Ethical and Scalable Automation: A Governance and Compliance Framework for Business Applicationscited by 2
  20. 2025Co-Producing AI: Toward an Augmented, Participatory Lifecyclecited by 2
  21. 2021Contrastive Explanations for Explaining Model Adaptationscited by 1
  22. 2022Explainability for identification of vulnerable groups in machine learning modelscited by 1
  23. 2022The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysiscited by 1
  24. 2023A multidomain relational framework to guide institutional AI research and adoptioncited by 1
  25. 2023The Representational Status of Deep Learning Modelscited by 1
  26. 2023The Promise and Peril of Artificial Intelligence -- Violet Teaming Offers a Balanced Path Forwardcited by 1
  27. 2023A Review of the Ethics of Artificial Intelligence and its Applications in the United Statescited by 1
  28. 2024Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networkscited by 1
  29. 2024Towards an Operational Responsible AI Framework for Learning Analytics in Higher Educationcited by 1
  30. 2025Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Studycited by 1
  31. 2025Mitigating LLM Hallucinations with Knowledge Graphs: A Case Studycited by 1
  32. 2025Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)cited by 1
  33. 2025Measuring What Matters: The AI Pluralism Indexcited by 1
  34. 2025A Comprehensive Survey on Surgical Digital Twincited by 1
  35. 2021Technology Readiness Levels for Machine Learning Systemscited by 0
  36. 2021Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEscited by 0
  37. 2021The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AIcited by 0
  38. 2021On the Limits of Design: What Are the Conceptual Constraints on Designing Artificial Intelligence for Social Good?cited by 0

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