The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Wiener, 1960) (Samuel, 1960). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles-the 'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)-rather than on practices, the 'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers 'apply ethics' at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

Italy and the United Kingdom. They will also help watchdogs and others to scrutinize such technologies.What do COVID-19 contact-tracing apps do? Running on a mobile phone, they inform people that they have spent time near someone with the virus. The contacts should then respond according to local rules, for example by isolating themselves. Prompt alerts are key because the incubation time of the virus is up to two weeks [1][2][3][4] .These digital interventions come at a price. Collecting sensitive personal data potentially threatens privacy, equality and fairness. Even if COVID-19 apps are temporary, rapidly rolling out tracing technologies runs the risk of creating permanent, vulnerable records of people's health, movements and social interactions, over which they have little control.More ethical oversight is essential. So far, such concerns have focused on rights to privacy (see go.nature.com/3e7jntx). Some governments have pledged to protect data privacy (see go.nature.com/3grwfe8). Apple and Google are developing a common interface to support apps that do not require central data storage (see Nature http://doi.org/dwc6; Protect privacy, equality and fairness in digital contact tracing with these key questions.Passengers on an underground train in Seoul. South Korea used contact tracing to great effect early in the pandemic.

This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched to support the following research question: how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be 'ethically mindful? A series of screening stages were carried out-for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)-yielding a total of 156 papers that were included in the review.We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new 'AI winter' could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.

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Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. Big Data Soc 3(2), 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms.

In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence (AI), entitled ‘New Generation Artificial Intelligence Development Plan’ (新一代人工智能发展规划). This strategy outlined China’s aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan (ca. 150 billion dollars) industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents.

Background: Long COVID describes new or persistent symptoms at least four weeks after onset of acute COVID-19. Clinical codes to describe this were recently created. Aim: To describe the use of long COVID codes, and variation of use by general practice, demographics and over time. Design and Setting: Population-based cohort study in English primary care records. Method: Working on behalf of NHS England, we used OpenSAFELY data encompassing 96% of the English population between 2020-02-01 and 2021-04-25. We measured the proportion of people with a recorded code for long COVID, overall and by demographic factors, electronic health record software system (EMIS or TPP), and week. Results: Long COVID was recorded for 23,273 people. Coding was unevenly distributed amongst practices, with 26.7% of practices having never used the codes. Regional variation, ranged between 20.3 per 100,000 people for East of England (95% confidence interval 19.3-21.4) and 55.6 in London (95% CI 54.1-57.1). Coding was higher amongst women (52.1, 95% CI 51.3-52.9) than men (28.1, 95% CI 27.5-28.7), and higher amongst EMIS practices (53.7, 95% CI 52.9-54.4) than TPP practices (20.9, 95% CI 20.3-21.4). Conclusion: Long COVID coding in primary care is low compared with early reports of long COVID prevalence. This may reflect under-coding, sub-optimal communication of clinical terms, under-diagnosis, a true low prevalence of long COVID diagnosed by clinicians, or a combination of factors. We recommend increased awareness of diagnostic codes, to facilitate research and planning of services; and surveys of clinicians’ experiences, to complement ongoing patient surveys.

he prospect of improved clinical outcomes and more efficient health systems has fueled a rapid rise in the development and evaluation of AI systems over the last decade. Because most AI systems within healthcare are complex interventions designed as clinical decision support systems, rather than autonomous agents, the interactions among the AI systems, their users and the implementation environments are defining components of the AI interventions' overall potential effectiveness. Therefore, bringing AI systems from mathematical performance to clinical utility needs an adapted, stepwise implementation and evaluation pathway, addressing the complexity of this collaboration between two independent forms of intelligence, beyond measures of effectiveness alone 1 . Despite indications that some AI-based algorithms now match the accuracy of human experts within preclinical in silico studies 2 , there