Fairness is one of the most prominent values in the Ethics and Artificial Intelligence debate and, specifically, in the discussion on algorithmic decision-making. However, while the need for fairness in ADM is widely acknowledged, the very concept of fairness has not been sufficiently explored so far. Our paper aims to fill this gap and claims that an ethically informed re-definition of fairness is needed to adequately investigate fairness in ADM. To achieve our goal, after an introductory section aimed at clarifying (...) the aim and structure of the paper, in section “Fairness in algorithmic decision-making” we provide an overview of the state of the art of the discussion on fairness in ADM and show its shortcomings; in section “Fairness as an ethical value”, we pursue an ethical inquiry into the concept of fairness, drawing insights from accounts of fairness developed in moral philosophy, and define fairness as an ethical value. In particular, we argue that fairness is articulated in a distributive and socio-relational dimension; it comprises three main components: fair equality of opportunity, equal right to justification, and fair equality of relationship; these components are grounded in the need to respect persons both as persons and as particular individuals. In section “Fairness in algorithmic decision-making revised”, we analyze the implications of our redefinition of fairness as an ethical value on the discussion of fairness in ADM and show that each component of fairness has profound effects on the criteria that ADM ought to meet. Finally, in section “Concluding remarks”, we sketch some broader implications and conclude. (shrink)

In this article, we focus on the attribution of moral responsibility for the actions of autonomous weapons systems (AWS). To do so, we suggest that the responsibility gap can be closed if human agents can take meaningful moral responsibility for the actions of AWS. This is a moral responsibility attributed to individuals in a justified and fair way and which is accepted by individuals as an assessment of their own moral character. We argue that, given the unpredictability of AWS, meaningful (...) moral responsibly can only be discharged by human agents who are willing to take a moral gambit: they decide to design/develop/deploy AWS despite the uncertainty about the effects an AWS may produce, hoping that unintended and unwanted or unforeseen outcomes may never occurs, but also accepting to be held responsible if such outcomes will occur. We argue that, while a moral gambit is permissible for the use of non-lethal AWS, this is not the case for the actions of lethal autonomous weapon systems. (shrink)

In the last few years, we have witnessed a surge in scholarly interest and scientific evidence of how algorithms can produce discriminatory outcomes, especially with regard to gender and race. However, the analysis of fairness and bias in AI, important for the debate of AI for social good, has paid insufficient attention to the category of age and older people. Ageing populations have been largely neglected during the turn to digitality and AI. In this article, the concept of AI ageism (...) is presented to make a theoretical contribution to how the understanding of inclusion and exclusion within the field of AI can be expanded to include the category of age. AI ageism can be defined as practices and ideologies operating within the field of AI, which exclude, discriminate, or neglect the interests, experiences, and needs of older population and can be manifested in five interconnected forms: age biases in algorithms and datasets, age stereotypes, prejudices and ideologies of actors in AI, invisibility of old age in discourses on AI, discriminatory effects of use of AI technology on different age groups, exclusion as users of AI technology, services and products. Additionally, the paper provides empirical illustrations of the way ageism operates in these five forms. (shrink)

This paper offers three contributions to the burgeoning movements of AI for Social Good and AI and the United Nations Sustainable Development Goals. First, we introduce the SDG-Intense Evaluation framework that aims to situate variegated automated/AI models in a larger ecosystem of computational approaches to advance the SDGs. To foster knowledge collaboration for solving complex social and environmental problems encompassed by the SDGs, the SDGIE framework details a benchmark structure of data-algorithm-output to effectively standardize AI approaches to the SDGs. Second, (...) as a specific instantiation of the SDGIE framework, the SDG Impact Intensity Model is theoretically and operationally established. SDGIIM embeds expert decision-making and SDG keyword banks in textual data processing to determine overall SDG “impact intensity.” Ideally, SDGIIM can be applied to textual data sets from any sector or discipline: academia, business, government, non-profit, civil society, etc. Third, the SDGIIM instantiation is applied to the specific domain of academic journal rating systems as a case study. Traditionally, academic journals have been evaluated on loosely conceived and empirically shaky notions of ‘quality.’ Aligned with the trend of AI4SG and broader calls to action, ‘impact’ is rapidly becoming the primary normative consideration for assessing academic journals. We hypothesize and demonstrate that SDGIIM is capable of producing evaluations aligned with experts’ expectations of SDG impact intensity; the consistent analysis and rating of textual data sets that embody the SDGs with varying degrees of meaning and, ultimately, promote positive impact on the actual material conditions of the world. (shrink)

The world’s current model for economic development is unsustainable. It encourages high levels of resource extraction, consumption, and waste that undermine positive environmental outcomes. Transitioning to a circular economy (CE) model of development has been proposed as a sustainable alternative. Artificial intelligence (AI) is a crucial enabler for CE. It can aid in designing robust and sustainable products, facilitate new circular business models, and support the broader infrastructures needed to scale circularity. However, to date, considerations of the ethical implications of (...) using AI to achieve a transition to CE have been limited. This article addresses this gap. It outlines how AI is and can be used to transition towards CE, analyzes the ethical risks associated with using AI for this purpose, and supports some recommendations to policymakers and industry on how to minimise these risks. (shrink)

Over the past few years, there has been a proliferation of artificial intelligence strategies, released by governments around the world, that seek to maximise the benefits of AI and minimise potential harms. This article provides a comparative analysis of the European Union and the United States’ AI strategies and considers the visions of a ‘Good AI Society’ that are forwarded in key policy documents and their opportunity costs, the extent to which the implementation of each vision is living up to (...) stated aims and the consequences that these differing visions of a ‘Good AI Society’ have for transatlantic cooperation. The article concludes by comparing the ethical desirability of each vision and identifies areas where the EU, and especially the US, need to improve in order to achieve ethical outcomes and deepen cooperation. (shrink)

The effects that artificial intelligence (AI) technologies will have on society in the short- and long-term are inherently uncertain. For this reason, many governments are avoiding strict command and control regulations for this technology and instead rely on softer ethics-based approaches. The Russian approach to regulating AI is characterized by the prevalence of unenforceable ethical principles implemented via industry self-regulation. We analyze the emergence of the regulatory regime for AI in Russia to illustrate the limitations of this approach. The article (...) is based on 50 interviews with policymakers, representatives of AI companies, and academics in the country. The findings show that this regulatory regime was formed under the strong influence of Russian big tech companies, which saw an opportunity to avoid regulatory oversight by washing out concrete regulatory measures from the policy. This approach is part of a broader protectionist sanction-proofing strategy for the local IT sector designed by the government, which can be characterized by lifting regulatory barriers for local companies. Unenforceable ethics-based self-regulation is a _regulatory gift_ from the Russian government to the industry. This _gift_ was intentionally designed because the government thought that prioritizing local innovation over consumer protection would benefit the public. However, the _gift_ can also unintentionally undermine the public interest by providing an opportunity for _ethics washing_. (shrink)

Digital Twins are conceptualised in the academic technical discourse as real-time realistic digital representations of physical entities. Originating from product engineering, the Digital Twin quickly advanced into other fields, including the life sciences and earth sciences. Digital Twins are seen by the tech sector as the new promising tool for efficiency and optimisation, while governmental agencies see it as a fruitful means for improving decision-making to meet sustainability goals. A striking example of the latter is the European Commission who wishes (...) to delegate a significant role to Digital Twins in addressing climate change and supporting Green Deal policy. As Digital Twins give rise to high expectations, ambitions, and are being entrusted important societal roles, it is crucial to critically reflect on the nature of Digital Twins. In this article, we therefore philosophically reflect on Digital Twins by critically analysing dominant conceptualisations, the assumptions underlying them, and their normative implications. We dissect the concept and argue that a Digital Twin does not merely fulfil the role of being a representation, but is in fact a steering technique used to direct a physical entity towards certain goals by means of multiple representations. Currently, this steering seems mainly fuelled by a reductionist approach focused on efficiency and optimisation. However, this is not the only direction from which a Digital Twin can be thought and, consequently, designed and deployed. We therefore set an agenda based on a critical understanding of Digital Twins that helps to draw out their beneficial potential, while addressing their potential issues. (shrink)

The paper explores some normative challenges concerning the integration of Machine Learning (ML) algorithms into anticorruption in public institutions. The challenges emerge from the tensions between an approach treating ML algorithms as allies to an exclusively legalistic conception of anticorruption and an approach seeing them within an institutional ethics of office accountability. We explore two main challenges. One concerns the variable opacity of some ML algorithms, which may affect public officeholders’ capacity to account for institutional processes relying upon ML techniques. (...) The other pinpoints the risk that automating certain institutional processes may weaken officeholders’ direct engagement to take forward-looking responsibility for the working of their institution. We discuss why both challenges matter to see how ML algorithms may enhance (and not hinder) institutional answerability practices. (shrink)

Algorithmic systems are increasingly used by state agencies to inform decisions about humans. They produce scores on risks of recidivism in criminal justice, indicate the probability for a job seeker to find a job in the labor market, or calculate whether an applicant should get access to a certain university program. In this contribution, we take an interdisciplinary perspective, provide a bird’s eye view of the different key decisions that are to be taken when state actors decide to use an (...) algorithmic system, and illustrate these decisions with empirical examples from case studies. Building on these insights, we discuss the main pitfalls and promises of the use of algorithmic system by the state and focus on four levels: The most basic question whether an algorithmic system should be used at all, the regulation and governance of the system, issues of algorithm design, and, finally, questions related to the implementation of the system on the ground and the human–machine-interaction that comes with it. Based on our assessment of the advantages and challenges that arise at each of these levels, we propose a set of crucial questions to be asked when such intricate matters are addressed. (shrink)

The initial successes in recent years in harnessing machine learning technologies to improve medical practice and benefit patients have attracted attention in a wide range of healthcare fields. Particularly, it should be achieved by providing automated decision recommendations to the treating clinician. Some hopes placed in such ML-based systems for healthcare, however, seem to be unwarranted, at least partially because of their inherent lack of transparency, although their results seem convincing in accuracy and reliability. Skepticism arises when the physician as (...) the agent responsible for the implementation of diagnosis, therapy, and care is unable to access the generation of findings and recommendations. There is widespread agreement that, generally, a complete traceability is preferable to opaque recommendations; however, there are differences about addressing ML-based systems whose functioning seems to remain opaque to some degree—even if so-called explicable or interpretable systems gain increasing amounts of interest. This essay approaches the epistemic foundations of ML-generated information specifically and medical knowledge generally to advocate differentiations of decision-making situations in clinical contexts regarding their necessary depth of insight into the process of information generation. Empirically accurate or reliable outcomes are sufficient for some decision situations in healthcare, whereas other clinical decisions require extensive insight into ML-generated outcomes because of their inherently normative implications. (shrink)

This review seeks to present a comprehensive picture of recent discussions in the social sciences of the anticipated impact of AI on the world of work. Issues covered include: technological unemployment, algorithmic management, platform work and the politics of AI work. The review identifies the major disciplinary and methodological perspectives on AI’s impact on work, and the obstacles they face in making predictions. Two parameters influencing the development and deployment of AI in the economy are highlighted: the capitalist imperative and (...) nationalistic pressures. (shrink)

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact (...) tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps. (shrink)

Among the myriad of technical approaches and abstract guidelines proposed to the topic of AI bias, there has been an urgent call to translate the principle of fairness into the operational AI reality with the involvement of social sciences specialists to analyse the context of specific types of bias, since there is not a generalizable solution. This article offers an interdisciplinary contribution to the topic of AI and societal bias, in particular against the poor, providing a conceptual framework of the (...) issue and a tailor-made model from which meaningful data are obtained using Natural Language Processing word vectors in pretrained Google Word2Vec, Twitter and Wikipedia GloVe word embeddings. The results of the study offer the first set of data that evidences the existence of bias against the poor and suggest that Google Word2vec shows a higher degree of bias when the terms are related to beliefs, whereas bias is higher in Twitter GloVe when the terms express behaviour. This article contributes to the body of work on bias, both from and AI and a social sciences perspective, by providing evidence of a transversal aggravating factor for historical types of discrimination. The evidence of bias against the poor also has important consequences in terms of human development, since it often leads to discrimination, which constitutes an obstacle for the effectiveness of poverty reduction policies. (shrink)

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and (...) the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment. (shrink)

There is a long history of the science of intelligent machines and its potential to provide scientific insights have been debated since the dawn of AI. In particular, there is renewed interest in the role of AI in research and research policy as an enabler of new methods, processes, management and evaluation which is still relatively under-explored. This empirical paper explores interviews with leading scholars on the potential impact of AI on research practice and culture through deductive, thematic analysis to (...) show the issues affecting academics and universities today. Our interviewees identify positive and negative consequences for research and researchers with respect to _collective_ and _individual use_. AI is perceived as helpful with respect to information gathering and other narrow tasks, and in support of impact and interdisciplinarity. However, using AI as a way of ‘speeding up—to keep up’ with bureaucratic and metricised processes, may proliferate negative aspects of academic culture in that the expansion of AI in research should assist and not replace human creativity. Research into the future role of AI in the research process needs to go further to address these challenges, and ask fundamental questions about how AI might assist in providing new tools able to question the values and principles driving institutions and research processes. We argue that to do this an explicit movement of meta-research on the role of AI in research should consider the effects for research and researcher creativity. Anticipatory approaches and engagement of diverse and critical voices at policy level and across disciplines should also be considered. (shrink)

The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the subjective nature of ethical decision-making. (...) The challenge related to the developers is that they themselves lack training in ethical skills. The creators who develop an artificial ethical agent should be able to foresee the ethical issues and have knowledge about ethical decision-making to improve the ethical use of AI-equipped machines. The suggestion is that the focus should be on training professionals involved in the process of developing these artificial systems in ethics rather than developing artificial ethical agents and thereby attributing ethical agency to it. (shrink)

The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the subjective nature of ethical decision-making. (...) The challenge related to the developers is that they themselves lack training in ethical skills. The creators who develop an artificial ethical agent should be able to foresee the ethical issues and have knowledge about ethical decision-making to improve the ethical use of AI-equipped machines. The suggestion is that the focus should be on training professionals involved in the process of developing these artificial systems in ethics rather than developing artificial ethical agents and thereby attributing ethical agency to it. (shrink)

Responsible innovation in artificial intelligence calls for public deliberation: well-informed “deep democratic” debate that involves actors from the public, private, and civil society sectors in joint efforts to critically address the goals and means of AI. Adopting such an approach constitutes a challenge, however, due to the opacity of AI and strong knowledge boundaries between experts and citizens. This undermines trust in AI and undercuts key conditions for deliberation. We approach this challenge as a problem of situating the knowledge of (...) actors from the AI industry within a deliberative system. We develop a new framework of responsibilities for AI innovation as well as a deliberative governance approach for enacting these responsibilities. In elucidating this approach, we show how actors from the AI industry can most effectively engage with experts and nonexperts in different social venues to facilitate well-informed judgments on opaque AI systems and thus effectuate their democratic governance. (shrink)

Artificial intelligence systems have been widely applied to various contexts, including high-stake decision processes in healthcare, banking, and judicial systems. Some developed AI models fail to offer a fair output for specific minority groups, sparking comprehensive discussions about AI fairness. We argue that the development of AI systems is marked by a central paradox: the less participation one stakeholder has within the AI system’s life cycle, the more influence they have over the way the system will function. This means that (...) the impact on the fairness of the system is in the hands of those who are less impacted by it. However, most of the existing works ignore how different aspects of AI fairness are dynamically and adaptively affected by different stages of AI system development. To this end, we present a use case to discuss fairness in the development of corporate wellness programs using smart wearables and AI algorithms to analyze data. The four key stakeholders throughout this type of AI system development process are presented. These stakeholders are called service designer, algorithm designer, system deployer, and end-user. We identify three core aspects of AI fairness, namely, contextual fairness, model fairness, and device fairness. We propose a relative contribution of the four stakeholders to the three aspects of fairness. Furthermore, we propose the boundaries and interactions between the four roles, from which we make our conclusion about the possible unfairness in such an AI developing process. (shrink)

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede (...) informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. (shrink)

The increasing use of artificial intelligence by public actors has led to a push for more transparency. Previous research has conceptualized AI transparency as knowledge that empowers citizens and experts to make informed choices about the use and governance of AI. Conversely, in this paper, we critically examine if transparency-as-knowledge is an appropriate concept for a public realm where private interests intersect with democratic concerns. We conduct a practice-based design research study in which we prototype and evaluate a transparent smart (...) electric vehicle charge point, and investigate experts’ and citizens’ understanding of AI transparency. We find that citizens experience transparency as burdensome; experts hope transparency ensures acceptance, while citizens are mostly indifferent to AI; and with absent means of control, citizens question transparency’s relevance. The tensions we identify suggest transparency cannot be reduced to a product feature, but should be seen as a mediator of debate between experts and citizens. (shrink)

Recent advances in the capability of digital information technologies—particularly due to advances in artificial intelligence —have invigorated the debate on the ethical issues surrounding their use. However, this debate has often been dominated by ‘Western’ ethical perspectives, values and interests, to the exclusion of broader ethical and socio-cultural perspectives. This imbalance carries the risk that digital technologies produce ethical harms and lack social acceptance, when the ethical norms and values designed into these technologies collide with those of the communities in (...) which they are delivered and deployed. This special issue takes a step towards broadening the approach of digital ethics, by bringing together a range of cultural, social and structural perspectives on the ethical issues relating to digital information technology. Importantly, it refreshes and reignites the field of Intercultural Digital Ethics for the age of AI and ubiquitous computing. (shrink)

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, (...) and implications). We analyse this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasise or prioritise some over others depending on the proactive or reactive use of accountability and the missions of AI governance. (shrink)

The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry (...) that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)