# Understanding artificial intelligence ethics and safety

arXiv: https://arxiv.org/abs/1906.05684 · HTML: https://mlsanity.com/paper/1906.05684/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 30 of 68)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review](https://mlsanity.com/paper/2112.12596/) (2021)
- [Counterfactual harm](https://mlsanity.com/paper/2204.12993/) (2022)
- [A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms](https://mlsanity.com/paper/2407.01294/) (2024)
- [Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes](https://mlsanity.com/paper/2210.03175/) (2022)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting](https://mlsanity.com/paper/2407.15909/) (2024)
- [Towards Accountability in the Use of Artificial Intelligence for Public Administrations](https://mlsanity.com/paper/2105.01434/) (2021)
- [Explainable Artificial Intelligence for Medical Applications: A Review](https://mlsanity.com/paper/2412.01829/) (2024)
- [Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications](https://mlsanity.com/paper/2203.10794/) (2022)
- [Deep Learning Opacity in Scientific Discovery](https://mlsanity.com/paper/2206.00520/) (2022)
- [Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcare](https://mlsanity.com/paper/2211.16444/) (2022)
- [A Principles-based Ethics Assurance Argument Pattern for AI and Autonomous Systems](https://mlsanity.com/paper/2203.15370/) (2022)
- [Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review](https://mlsanity.com/paper/2204.05591/) (2022)
- [Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice](https://mlsanity.com/paper/2506.09873/) (2025)
- [Co-evolutionary hybrid intelligence](https://mlsanity.com/paper/2112.04751/) (2021)
- [GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)](https://mlsanity.com/paper/2211.01656/) (2022)
- [A Taxonomy of Explainable Bayesian Networks](https://mlsanity.com/paper/2101.11844/) (2021)
- [Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach](https://mlsanity.com/paper/2202.12443/) (2022)
- [Artificial intelligence in government: Concepts, standards, and a unified framework](https://mlsanity.com/paper/2210.17218/) (2022)
- [Ethical AI: Towards Defining a Collective Evaluation Framework](https://mlsanity.com/paper/2506.00233/) (2025)
- [The Arc of the Data Scientific Universe](https://mlsanity.com/paper/2102.10050/) (2021)
- [Data Justice in Practice: A Guide for Developers](https://mlsanity.com/paper/2205.01037/) (2022)
- [Appropriateness is all you need!](https://mlsanity.com/paper/2304.14553/) (2023)
- [Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey](https://mlsanity.com/paper/2307.09615/) (2023)
- [Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications](https://mlsanity.com/paper/2311.15716/) (2023)
- [Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education](https://mlsanity.com/paper/2402.15027/) (2024)

## 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.