Jessica Morley

The gig economy is a phenomenon that is rapidly expanding, redefining the nature of work and contributing to a significant change in how contemporary economies are organised. Its expansion is not unproblematic. This article provides a clear and systematic analysis of the main ethical challenges caused by the gig economy. Following a brief overview of the gig economy, its scope and scale, we map the key ethical problems that it gives rise to, as they are discussed in the relevant literature. We map them onto three categories: the new organisation of work (what is done), the new nature of work (how it is done), and the new status of workers (who does it). We then evaluate a recent initiative from the EU that seeks to address the challenges of the gig economy. The 2019 report of the European High-Level Expert Group on the Impact of the Digital Transformation on EU Labour Markets is a positive step in the right direction. However, we argue that ethical concerns relating to algorithmic systems as mechanisms of control, and the discrimination, exclusion and disconnectedness faced by gig workers require further deliberation and policy response. A brief conclusion completes the analysis. The appendix presents the methodology underpinning our literature review.

Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted.

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare.

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, 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.

Technologies to rapidly alert people when they have been in contact with someone carrying the coronavirus SARS-CoV-2 are part of a strategy to bring the pandemic under control. Currently, at least 47 contact-tracing apps are available globally. They are already in use in Australia, South Korea and Singapore, for instance. And many other governments are testing or considering them. Here we set out 16 questions to assess whether — and to what extent — a contact-tracing app is ethically justifiable.

In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence, entitled ‘New Generation Artificial Intelligence Development Plan’. This strategy outlined China’s aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan 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.

AI, in the form of artificial carers, provides a possible solution to the problem of a growing elderly population Yet, concerns remain that artificial carers ( such as care-or chat-bots) could not emphathize with patients to the extent that humans can. Utilising the concept of empathy perception,we propose a Turing-type test that could check whether artificial carers could do many of the menial tasks human carers currently undertake, and in the process, free up more time for doctors to offer empathy.

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning 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 —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. 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 Machine Learning development 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.

Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of automation. In this article, we consider the feasibility and efficacy of ethics-based auditing as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS.

It has been suggested that to overcome the challenges facing the UK’s National Health Service (NHS) of an ageing population and reduced available funding, the NHS should be transformed into a more informationally mature and heterogeneous organisation, reliant on data-based and algorithmically-driven interactions between human, artificial, and hybrid (semi-artificial) agents. This transformation process would offer significant benefit to patients, clinicians, and the overall system, but it would also rely on a fundamental transformation of the healthcare system in a way that poses significant governance challenges. In this article, we argue that a fruitful way to overcome these challenges is by adopting a pro-ethical approach to design that analyses the system as a whole, keeps society-in-the-loop throughout the process, and distributes responsibility evenly across all nodes in the system.

On 8th August 2019, Secretary of State for Health and Social Care, Matt Hancock, announced the creation of a £250 million NHS AI Lab. This significant investment is justified on the belief that transforming the UK’s National Health Service (NHS) into a more informationally mature and heterogeneous organisation, reliant on data-based and algorithmically-driven interactions, will offer significant benefit to patients, clinicians, and the overall system. These opportunities are realistic and should not be wasted. However, they may be missed (one may recall the troubled Care.data programme) if the ethical challenges posed by this transformation are not carefully considered from the start, and then addressed thoroughly, systematically, and in a socially participatory way. To deal with this serious risk, the NHS AI Lab should create an Ethics Advisory Board and monitor, analyse, and address the normative and overarching ethical issues that arise at the individual, interpersonal, group, institutional and societal levels in AI for healthcare.

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of AI ethics principles and the practical design of AI systems. In previous work , we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible (and thus vulnerable to ethics washing) or too strict (unresponsive to context). This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service’.

As the range of potential uses for Artificial Intelligence, in particular machine learning, has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible or too strict. This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service.’

Over the past year, technology companies have made headlines claiming that their artificially intelligent (AI) products can outperform clinicians at diagnosing breast cancer, brain tumours, and diabetic retinopathy. Claims such as these have influenced policy makers, and AI now forms a key component of the national health strategies in England, the United States, and China. While it is positive to see healthcare systems embracing data analytics and machine learning, concerns remain about the efficacy, ethics, and safety of some commercial, AI health solutions. This paper argues that improved regulation and guidance is urgently required to mitigate risks, ensure transparency and best practice, Without this, patients, clinicians, and other stakeholders cannot be assured of an app’s efficacy, and safety.

This article highlights the limitations of the tendency to frame health- and wellbeing-related digital tools (mHealth technologies) as empowering devices, especially as they play an increasingly important role in the National Health Service (NHS) in the UK. It argues that mHealth technologies should instead be framed as digital companions. This shift from empowerment to companionship is advocated by showing the conceptual, ethical, and methodological issues challenging the narrative of empowerment, and by arguing that such challenges, as well as the risk of medical paternalism, can be overcome by focusing on the potential for mHealth tools to mediate the relationship between recipients of clinical advice and givers of clinical advice, in ways that allow for contextual flexibility in the balance between patiency and agency. The article concludes by stressing that reframing the narrative cannot be the only means for avoiding harm caused to the NHS as a healthcare system by the introduction of mHealth tools. Future discussion will be needed on the overarching role of responsible design.

The fact that Internet companies may record our personal data and track our online behavior for commercial or political purpose has emphasized aspects related to online privacy. This has also led to the development of search engines that promise no tracking and privacy. Search engines also have a major role in spreading low-quality health information such as that of anti-vaccine websites. This study investigates the relationship between search engines’ approach to privacy and the scientific quality of the information they return. We analyzed the first 30 webpages returned searching “vaccines autism” in English, Spanish, Italian, and French. The results show that not only “alternative” search engines but also other commercial engines often return more anti-vaccine pages (10–53%) than Google (0%). Some localized versions of Google, however, returned more anti-vaccine webpages (up to 10%) than Google. Health information returned by search engines has an impact on public health and, specifically, in the acceptance of vaccines. The issue of information quality when seeking information for making health-related decisions also impact the ethical aspect represented by the right to an informed consent. Our study suggests that designing a search engine that is privacy savvy and avoids issues with filter bubbles that can result from user-tracking is necessary but insufficient; instead, mechanisms should be developed to test search engines from the perspective of information quality (particularly for health-related webpages) before they can be deemed trustworthy providers of public health information.

Digital Health Tools (DHTs), also known as patient self-surveilling strategies, have increasingly been promoted by health-care policy makers as technologies that have the capacity to transform patients’ lives. At the heart of the debate is the notion of empowerment. In this paper, we argue that what is required is not so much empowerment but rather a shift to enabling DHTs as digital companions. This will enable policy makers and health-care system designers to provide a more balanced view—one that capitalises on the benefits of DHTs, while minimising the risks of potential harms.

Regulation by design (RBD) is a growing research field that explores, develops, and criticises the regulative function of design. In this article, we provide a qualitative thematic synthesis of the existing literature. The aim is to explore and analyse RBD's core features, practices, limitations, and related governance implications. To fulfil this aim, we examine the extant literature on RBD in the context of digital technologies. We start by identifying and structuring the core features of RBD, namely the goals, regulators, regulatees, methods, and technologies. Building on that structure, we distinguish between three types of RBD practices: compliance by design, value creation by design, and optimisation by design. We then explore the challenges and limitations of RBD practices, which stem from risks associated with compliance by design, contextual limitations, or methodological uncertainty. Finally, we examine the governance implications of RBD and outline possible future directions of the research field and its practices.

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, 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.

By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of a searchable typology of tools and methods designed to translate between the five most common AI ethics principles and implementable design practices. Whilst a useful starting point, that research rested on the assumption that _all_ AI practitioners are aware of the ethical implications of AI, understand their importance, and are actively seeking to respond to them. In reality, it is unclear whether this is the case. It is this limitation that we seek to overcome here by conducting a mixed-methods qualitative analysis to answer the following four questions: what do AI practitioners understand about the need to translate ethical principles into practice? What motivates AI practitioners to embed ethical principles into design practices? What barriers do AI practitioners face when attempting to translate ethical principles into practice? And finally, what assistance do AI practitioners want and need when translating ethical principles into practice?

This annual edited volume presents an overview of cutting-edge research areas within digital ethics as defined by the Digital Ethics Lab of the University of Oxford. It identifies new challenges and opportunities of influence in setting the research agenda in the field. The 2020 edition of the yearbook presents research on the following topics: governing digital health, visualising governance, the digital afterlife, the possibility of an AI winter, the limits of design theory in philosophy, cyberwarfare, ethics of online behaviour change, governance of AI, trust in AI, and Emotional Self-Awareness as a Digital Literacy. This book appeals to students, researchers and professionals in the field.

It has been suggested that to overcome the challenges facing the UK’s National Health Service of an ageing population and reduced available funding, the NHS should be transformed into a more informationally mature and heterogeneous organisation, reliant on data-based and algorithmically-driven interactions between human, artificial, and hybrid agents. This transformation process would offer significant benefit to patients, clinicians, and the overall system, but it would also rely on a fundamental transformation of the healthcare system in a way that poses significant governance challenges. In this chapter, we argue that a fruitful way to overcome these challenges is by adopting a pro-ethical approach to design that analyses the system as a whole, keeps society-in-the-loop throughout the process, and distributes responsibility evenly across all nodes in the system.

The SARS-CoV-2 (COVID-19) pandemic has caused social and economic devastation. As the milestone of two years of ‘living with the virus’ approaches, governments and businesses are attempting to develop means of reopening society whilst still protecting public health. However, developing interventions – particularly technological interventions – that find a safe, socially acceptable, and ethically justifiable balance between these two seemingly opposing demands is extremely challenging. There is no one right solution, but the current most popular ‘solution’ is the so-called ‘COVID-19 Vaccine Passport’ (also known as COVID-19 passes or certificates), the use of which may be supported by both human rights and international public health law if they are designed and implemented appropriately. (We use the term ‘Vaccine Passport’ because it has been adopted by the popular press. Though it has been used in many ways, here we use it generically to refer to a document that certifies that an individual has been vaccinated against COVID-19 and on that basis grants the bearer more liberties than to those who have not been vaccinated. Later, we will discuss why it is necessary to move beyond considering only vaccinations, which informs our preferred term of ‘COVID-19 Status Pass’.) We set out to answer the following questions: how should governments and businesses assess the risks in light of human rights, public health ethics, and digital ethics concerns which emerge from developing and deploying COVID-19 Vaccine Passports? What design decisions should businesses make when developing COVID-19 Vaccine Passports to help ensure they respect human rights, and both public health and digital ethics? Do the implications for human rights, public health, and digital ethics vary depending on where and when COVID-19 Vaccine Passports are used? What are the rights and powers of the individual to object to or seek remedy for the use of COVID-19 Vaccine Passports? How can the risks of inequalities and social division derived from the deployment of COVID-19 Vaccine Passports be avoided or mitigated? We conducted a literature review and documentary analysis, supplementing our findings with news articles where appropriate. The following pages report our results, concluding with a series of actionable recommendations for businesses, national governments, and supranational organisations.