Understanding artificial intelligence ethics and safety

By David Leslie. 2019

494 citations recorded by the source corpus.

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.

Subfields: Safety, Robustness & Interpretability, AI Ethics, Fairness & Society

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “Among them, we pause at the thorough study in [383] , which suggests that the incorporation and consideration of explainability in practical AI design and deployment workﬂows should comprise four major methodological steps: 1.”
Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, methodology): “For instance, The Alan Turing Institute released a guide for responsible design and implementation of AI (Leslie, 2019) that covers the whole life-cycle of design, use, and monitoring.”
Human-in-the-loop machine learning: a state of the art (2022, background): “Among them, the study in Leslie (2019) determines that the incorporation and consideration of explainability in practical AI design and deployment workflows should comprise four major methodological steps: 1.”
Interpretable Machine Learning (2019, background): “Discussion about AIAs has mainly focused on their use in the public sector.(7,8) The AI Now”
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, background): “• Contextual understanding: Human experts can provide valuable insights into the cultural, social, and historical contexts that shape language and communication, helping to guide AI models in generating more appropriate and sensitive responses [118, 136, 137].”
Ethical principles for artificial intelligence in education (2022, background): “As a result, it is pivotal that AIED developers take great care to design, train, pilot test, and validate the safety of AI systems (Leslie, 2019).”
Challenges in Deploying Machine Learning: A Survey of Case Studies (2020, background): “As stated in the report on ethical AI produced by the Alan Turing Institute [87], “it is essential to establish a continuous chain of human responsibility across the whole AI project delivery workflow”.”