The focus of AI ethics has recently shifted towards the question of whether and how the use of AI technologies can promote sustainability. This new research question involves discerning the sustainability of AI itself and evaluating AI as a tool to achieve sustainable objectives. This article aims to examine the justifications that one might employ to advocate for promoting sustainable AI. Specifically, it concentrates on a dimension of often disregarded reasons — reasons of “meaning” or “meaningfulness” — as (...) discussed more recently in the “meaning in life” literature of analytic ethics. To proceed, the article first elucidates the working definitions of “sustainable AI” and “meaning in life”, while also setting the criteria for evaluating the plausibility of these reasons. Subsequently, it presents and scrutinises three arguments for the claim that one has reasons to care about sustainable AI from a perspective of meaning: the Meaning-conferring-action Argument, the Afterlife Argument, and the Harm Argument. In conclusion, this article asserts that only the Harm Argument presents a viable line of reasoning. However, it also outlines the presuppositions of this argument and the additional steps necessary to make it compelling. (shrink)

Moving away from the currently prevalent Big Data mindset towards a Small Data approach would help improve the sustainability of AI systems and would additionally have positive implications for fairness, (global) justice, privacy, transparency, and accountability.

Artificial Intelligence (AI) developments are proliferating at an astonishing rate. Unsurprisingly, the number of meaningful studies addressing the social impacts of AI applications in several fields has been remarkable. More recently, several contributions have started exploring the ecological impacts of AI. Machine learning systems do not have a neutral environmental cost, so it is important to unravel the ecological footprint of these techno-scientific developments. In this chapter, we discuss the sustainability of AI from environmental ethics approaches. We examine the moral (...) trade-offs that AI may cause in different moral dimensions and analyse prominent conflicts that may arise from human and more-than-human-centred concerns. (shrink)

Member States of the United Nations proposed Seventeen Sustainable Development Goals (SDGs) in 2015, emphasizing the well-being of people, planet, prosperity, peace, and partnership. Countries are expected to work diligently to achieve these goals by the year 2030. The paths chosen to achieve the SDGs depend on each country’s specific needs, challenges, and opportunities. This contribution conducts a bibliometric study of selected SDG research related to hunger and climate change among countries of the North Asia Pacific region. A review (...) of literature related to the seventeen SDGs is provided to update the current research in SDGs as a background for the regional bibliometric search. The academic published literature related to this work were collected and text mined to create a research database for trend analysis. In this research, the natural language model KeyBERT and a text mining algorithm for normalized term frequency were used to extract key terminologies for bibliometric analysis. The study provides an overview of the seventeen SDGs in the literature review and then focuses on SDG 2 (zero hunger) and SDG 13 (climate action) publication trends. These two SDGs are in close alignment with sustainable food systems and agricultural development. The conclusion indicates that greater cooperation (SDG 17) is critical to improving the quality of life within the region and in building an ethical framework that restricts external exploitation of resources that benefit newly developed economies over developing economies. (shrink)

AI is increasingly becoming based upon Internet-dependent systems to handle the massive amounts of data it requires to function effectively regardless of the availability of stable Internet connectivity in every affected community. As such, sustainable development (SD) for rural and mountain communities will require more than just equitable access to broadband Internet connection. It must also include a thorough means whereby to ensure that affected communities gain the education and tools necessary to engage inclusively with new technological advances, whether (...) they be focused on machine learning algorithms or community infrastructure, as they will be increasingly dependent upon the automational capabilities of AI. In this essay, an exploration will be conducted into the means whereby student-engaged learning (SEL) can effectively be utilized to provide targeted, inclusive education to rural and mountain communities regarding the implications of AI for SD. (shrink)

The aim of this paper is to investigate the relationship between AI urbanism and sustainability by drawing upon some key concepts of Bruno Latour’s philosophy. The idea of a sustainable AI urbanism - often understood as the juxtaposition of smart and eco urbanism - is here critiqued through a reconstruction of the conceptual sources of these two urban paradigms. Some key ideas of smart and eco urbanism are indicated as incompatible and therefore the fusion of these two paradigms is (...) assessed as an unstable basis for shaping sustainable AI urbanism. The concepts in question - modernity, science and nature – are subsequently redefined following Latour’s philosophical perspective, in an attempt to define a different theoretical basis for a sustainable AI urbanism in the Anthropocene. Finally, the principles of a design philosophy shaped by Latour are used to change the design culture that informs AI urbanism towards a more sustainable practice. This paper constructs and promotes a dialogue between the disciplines of philosophy and urban theory with urban design in the conviction that the principles produced by the former and the practices carried out by the latter must start a biunivocal relationship. The paper reveals that in order to change design culture in the field of AI urbanism, it is necessary to rethink some of the key ideas that inform the Western and modern worldview through novel philosophical reflections. (shrink)

This paper offers three contributions to the burgeoning movements of AI for Social Good (AI4SG) and AI and the United Nations Sustainable Development Goals (SDGs). First, we introduce the SDG-Intense Evaluation framework (SDGIE) 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 (SDGIIM) 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)

To realize the coordinated and sustainable development of hydropower projects and regional society, comprehensively evaluating hydropower projects’ influence is critical. Usually, hydropower project development has an impact on environmental geology and social and regional cultural development. Based on comprehensive consideration of complicated geological conditions, fragile ecological environment, resettlement of reservoir area, and other factors of future hydropower development in each country, we have constructed a comprehensive evaluation index system of hydropower projects, including 4 first-level indicators of social economy, environment, (...) safety, and fairness, which contain 26 second-level indicators. To solve the problem that existing models cannot evaluate dynamic nonlinear optimization, a projection pursuit model is constructed by using rough set reduction theory to simplify the index. Then, an accelerated genetic algorithm based on real number coding is used to solve the model and empirical study is carried out with the Y hydropower station as a sample. The evaluation results show that the evaluation index system and assessment model constructed in our paper effectively reduce the subjectivity of index weight. Applying our model to the social impact assessment of related international hydropower projects can not only comprehensively analyze the social impact of hydropower projects but also identify important social influencing factors and effectively analyze the social impact level of each dimension. Furthermore, SIA assessment can be conducive to project decision-making, avoiding social risks and social stability. (shrink)

This paper introduces the concept of AI-inclusive epistemology, suggesting that artificial intelligence (AI) may develop its own epistemological perspectives, function as an epistemic agent, and assume the role of a quasi-member of society. We explore the unique capabilities of advanced AI systems and their potential to provide distinct insights within knowledge systems traditionally dominated by human cognition. Additionally, the paper proposes a framework for a sustainable symbiotic society where AI and human intelligences collaborate to enhance the breadth and depth (...) of epistemological pursuits. Through this exploration, we aim to redefine conventional knowledge paradigms by integrating the AI perspective, thereby fostering a more comprehensive and dynamic epistemological landscape. (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 contribute to combating 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 combating climate change, while reducing its impact on the environment. (shrink)

The European Union Commission’s whitepaper on Artificial Intelligence proposes shaping the emerging AI market so that it better reflects common European values. It is a master plan that builds upon the EU AI High-Level Expert Group guidelines. This article reviews the masterplan, from a culture cycle perspective, to reflect on its potential clashes with current societal, technical, and methodological constraints. We identify two main obstacles in the implementation of this plan: the lack of a coherent EU vision to drive future (...) decision-making processes at state and local levels and the lack of methods to support a sustainable diffusion of AI in our society. The lack of a coherent vision stems from not considering societal differences across the EU member states. We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU. Moreover, the Commission aims at changing the AI development culture proposing a human-centred and safety-first perspective that is not supported by methodological advancements, thus taking the risks of unforeseen social and societal impacts of AI. We discuss potential societal, technical, and methodological gaps that should be filled to avoid the risks of developing AI systems at the expense of society. Our analysis results in the recommendation that the EU regulators and policymakers consider how to complement the EC programme with rules and compensatory mechanisms to avoid market fragmentation due to local and global ambitions. Moreover, regulators should go beyond the human-centred approach establishing a research agenda seeking answers to the technical and methodological open questions regarding the development and assessment of human-AI co-action aiming for a sustainable AI diffusion in the society. (shrink)

Automatic identification system is a vessel tracking system, which since 2004 has become a global tool for the detection and analysis of seagoing traffic. In this article, we look at how this technology, initially designed as a collision avoidance system, has recently become involved in debates concerning migration across the Mediterranean Sea. In particular, after having briefly discussed its emergence and characteristics, we examine how through different practices of appropriation AIS, and the data it generate, have been seized upon, both (...) to contest and to sustain the exclusionary nature of borders, and the mass dying of migrants at sea to which it leads. We do so by referring to forms of data activism we have contributed to in the frame of our Forensic Oceanography project as well as to situations in which AIS has been mobilized by xenophobic groups to demand even stronger exclusionary measures. At the same time, we point to the multiplicity of actors who participate in the politics of migration through AIS in unexpected ways. We conclude by highlighting the irreducible ambivalence of practices of appropriation and call for persistent attention to one’s own positioning within the global datascape constituted by AIS and other data. (shrink)

This chapter provides an overview of six topics related to governance, ethical, legal, and social implications of artificial intelligence (AI) for sustainable development goals (SDGs) initiatives. We identified six common challenges and related opportunities to mitigate such challenges, as referred to by the authors analysing the chapters provided in the book The Ethics of Artificial Intelligence for the Sustainable Development Goals. They are (1) governance and collaboration, (2) private investments and the role of big tech companies, (3) AI (...) and communities, (4) AI and individuals, (5) jobs and skills, and (6) impact assessment. (shrink)

The idea of artificial intelligence for social good is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are (...) essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good. (shrink)

The application of Artificial Intelligence (AI) in healthcare and epidemiology undoubtedly has many benefits for the population. However, due to its environmental impact, the use of AI can produce social inequalities and long-term environmental damages that may not be thoroughly contemplated. In this paper, we propose to consider the impacts of AI applications in medical care from the One Health paradigm and long-term global health. From health and environmental justice, rather than settling for a short and fleeting green honeymoon between (...) health and sustainability caused by AI, it should aim for a lasting marriage. To this end, we conclude by proposing that, in the upcoming years, it could be valuable and necessary to promote more interconnected health, call for environmental cost transparency, and increase green responsibility. (shrink)

The pervasiveness of AI-empowered technologies across multiple sectors has led to drastic changes concerning traditional social practices and how we relate to one another. Moreover, market-driven Big Tech corporations are now entering public domains, and concerns have been raised that they may even influence public agenda and research. Therefore, this chapter focuses on assessing and evaluating what kind of business model is desirable to incentivise the AI for Social Good (AI4SG) factors. In particular, the chapter explores the implications of this (...) discourse for SDG #17 (global partnership) and how this goal may encourage Big Tech corporations to strengthen multi-stakeholder partnerships that promote effective public-private and civil society partnerships and the meaningful co-presence of non-market and market values. In doing so, the chapter proposes an analysis of the sociological notion of "social license to operate" (SLO) elaborated in the mining and extractive industry literature and introduces it into the discourse on sustainable digital business models and responsible management of risks in the digital age. This serves to explore how such a social license can be adopted as a practice by digital business models to foster trust, collaboration and coordination among different actors - AI researchers and initiatives, institutions and civil society at large - for the support of SDGs interrelated targets and goals. (shrink)

Machine learning systems (MLS) that model low-level processes are the cornerstones of current AI systems. These ‘indirect’ learners are good at classifying kinds that are distinguished solely by their manifest physical properties. But the more a kind is a function of spatio-temporally extended properties — words, situation-types, social norms — the less likely an MLS will be able to track it. Systems that can interact with objects at the individual level, on the other hand, and that can sustain this interaction, (...) can learn responses to increasingly abstract properties, including representational ones. This representational capacity, arguably the mark of intelligence, then, is not available to current MLS’s. (shrink)

Despite its elusiveness as a concept, ‘artificial intelligence’ (AI) is becoming part of everyday life, and a range of empirical and methodological approaches to social studies of AI now span many disciplines. This article reviews the scope of ethnomethodological and conversation analytic (EM/CA) approaches that treat AI as a phenomenon emerging in and through the situated organization of social interaction. Although this approach has been very influential in the field of computational technology since the 1980s, AI has only recently emerged (...) as such a pervasive part of daily life to warrant a sustained empirical focus in EM/CA. Reviewing over 50 peer-reviewed publications, we find that the studies focus on various social and group activities such as task-oriented situations, semi-experimental setups, play, and everyday interactions. They also involve a range of participant categories including children, older participants, and people with disabilities. Most of the reviewed studies apply CA’s conceptual apparatus, its approach to data analysis, and core topics such as turn-taking and repair. We find that across this corpus, studies center on three key themes: openings and closing the interaction, miscommunication, and non-verbal aspects of interaction. In the discussion, we reflect on EM studies that differ from those in our corpus by focusing on praxeological respecifications of AI-related phenomena. Concurrently, we offer a critical reflection on the work of literature reviewing, and explore the tortuous relationship between EM and CA in the area of research on AI. (shrink)

Purpose The purpose of this paper is to identify the key roles of transparency in making artificial intelligence (AI) greener (i.e. causing lesser carbon dioxide emissions) during the design, development and manufacturing stages or processes of AI technologies (e.g. apps, systems, agents, tools, artifacts) and use the “explicability requirement” as an essential value within the framework of transparency in supporting arguments for realizing greener AI. Design/methodology/approach The approach of this paper is argumentative, which is supported by ideas from existing literature (...) and documents. Findings This paper puts forward a relevant recommendation for achieving better and sustainable outcomes after the reexamination of the identified roles played by transparency within the AI technology context. The proposed recommendation is based on scientific opinion, which is justified by the roles and importance of the two approaches (compliance and integrity) in ethics management and other areas of ethical studies. Originality/value The originality of this paper falls within the boundary of filling the gap that exists in sustainable AI technology and the roles of transparency. (shrink)

Research and activism have increasingly denounced the problematic environmental record of the infrastructure and value chain underpinning artificial intelligence (AI). Water-intensive data centres, polluting mineral extraction and e-waste dumping are incontrovertibly part of AI’s footprint. In this article, I turn to areas affected by AI-fuelled environmental harm and identify an ethics of resistance emerging from local activists, which I term ‘elemental ethics’. Elemental ethics interrogates the AI value chain’s problematic relationship with the elements that make up the world, critiques the (...) undermining of local and ancestral approaches to nature and reveals the vital and quotidian harms engendered by so-called intelligent systems. While this ethics is emerging from grassroots and Indigenous groups, it echoes recent calls from environmental philosophy to reconnect with the environment via the elements. In empirical terms, this article looks at groups in Chile resisting a Google data centre project in Santiago and lithium extraction (used for rechargeable batteries) in Lickan Antay Indigenous territory, Atacama Desert. As I show, elemental ethics can complement top-down, utilitarian and quantitative approaches to AI ethics and sustainable AI as well as interrogate whose lived experience and well-being counts in debates on AI extinction. (shrink)

Initiatives relying on artificial intelligence (AI) to deliver socially beneficial outcomes—AI for social good (AI4SG)—are on the rise. However, existing attempts to understand and foster AI4SG initiatives have so far been limited by the lack of normative analyses and a shortage of empirical evidence. In this Perspective, we address these limitations by providing a definition of AI4SG and by advocating the use of the United Nations’ Sustainable Development Goals (SDGs) as a benchmark for tracing the scope and spread of (...) AI4SG. We introduce a database of AI4SG projects gathered using this benchmark, and discuss several key insights, including the extent to which different SDGs are being addressed. This analysis makes possible the identification of pressing problems that, if left unaddressed, risk hampering the effectiveness of AI4SG initiatives. (shrink)

Customer behavior, market dynamics, and technological advances have made it challenging for marketing theorists to provide comprehensive explanations and actionable insights. Although there are numerous substantive marketing frameworks, no formal marketing theory exists. This study aims to develop the first formal grounded theory in marketing by incorporating artificial intelligence and Forde's conceptual framework as a guiding lens. Charmaz's constructivist grounded theory tradition and Forde's conceptual framework and data analysis strategy were employed for this purpose. The data analysis strategy used with (...) grounded theory’s constant compare-and-contrast method comprises holistic and systems thinking, dramaturgical and situational analysis, perspective-taking, and deconstruction. To evaluate Glaser’s assertion that all is data, artificial intelligence was incorporated into the research as the data source for retrieving the theoretical samples to address the primary research question: How can Forde's conceptual framework and artificial intelligence be leveraged to develop a formal marketing theory grounded in evidence? Of significance was the emergence of democratic marketing behaviors and the normalization of anti-democratic marketing approaches leveraged to manipulate buyers or gain access to segments of customers. Buyers and sellers can simultaneously benefit from and be prejudiced by marketing exchange dynamics. This finding lends credibility to the newly emerged framework of strategic actions where empowerment and disempowerment are leveraged to achieve specific goals. Considering this finding, it is suggested that businesses view marketing as a symbiotic relationship between two power centers, buyers and sellers, who respectively strive to maintain human dignity and sustainability. Researchers are encouraged to apply the framework across substantive areas, international markets, public relations, political campaigns, and others. (shrink)

Artificial Intelligence (AI) has become a driving force in modern research, industry and public administration and the European Union (EU) is embracing this technology with a view to creating societal, as well as economic, value. This effort has been shared by EU Member States which were all encouraged to develop their own national AI strategies outlining policies and investment levels. This study focuses on how EU Member States are approaching the promise to develop and use AI for the good of (...) society through the lens of their national AI strategies. In particular, we aim to investigate how European countries are investing in AI and to what extent the stated plans contribute to the good of people and society as a whole. Our contribution consists of three parts: (i) a conceptualization of AI for social good highlighting the role of AI policy, in particular, the one put forward by the European Commission (EC); (ii) a qualitative analysis of 15 European national strategies mapping investment plans and suggesting their relation to the social good (iii) a reflection on the current status of investments in socially good AI and possible steps to move forward. Our study suggests that while European national strategies incorporate money allocations in the sphere of AI for social good (e.g. education), there is a broader variety of underestimated actions (e.g. multidisciplinary approach in STEM curricula and dialogue among stakeholders) that can boost the European commitment to sustainable and responsible AI innovation. (shrink)

The management of ethics within organisations typically occurs within a problem-solving frame of reference. This often results in a reactive, problem-based and externally induced approach to managing ethics. Although basing ethics management interventions on dealing with and preventing current and possible future unethical behaviour are often effective in that it ensures compliance with rules and regulations, the approach is not necessarily conducive to the creation of sustained ethical cultures. Nor does the approach afford (mainly internal) stakeholders the opportunity to be (...) co-designers of the organisation's ethical future. The aim of this paper is to present Appreciative Inquiry (AI) as an alternative approach for developing a shared meaning of ethics within an organisation with a view to embrace and entrench ethics, thereby creating a foundation for the development of an ethical culture over time. A descriptive case study based on an application of AI is used to illustrate the utility of AI as a way of thinking and doing to precede and complement problem-based ethics management systems and interventions. (shrink)

Healthcare is becoming increasingly automated with the development and deployment of care robots. There are many benefits to care robots but they also pose many challenging ethical issues. This paper takes care robots for the elderly as the subject of analysis, building on previous literature in the domain of the ethics and design of care robots. Using the value sensitive design approach to technology design, this paper extends its application to care robots by integrating the values of care, values that (...) are specific to AI, and higher-scale values such as the United Nations Sustainable Development Goals. The ethical issues specific to care robots for the elderly are discussed at length alongside examples of specific design requirements that work to ameliorate these ethical concerns. (shrink)

Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and changing (...) behaviours. This article discusses how these AI-driven systems pose particular ethical challenges with regards to nudging. To confront these issues, the value sensitive design approach is adopted as a principled methodology that designers can adopt to design these systems to avoid harming and contribute to the social good. The AI for Social Good factors are adopted as the norms constraining maleficence. In contrast, higher-order values specific to AI, such as those from the EU High-Level Expert Group on AI and the United Nations Sustainable Development Goals, are adopted as the values to be promoted as much as possible in design. The use case of Amazon Alexa's Healthcare Skills is used to illustrate this design approach. It provides an exemplar of how designers and engineers can begin to orientate their design programs of these technologies towards the social good. (shrink)

. The management of ethics within organisations typically occurs within a problem-solving frame of reference. This often results in a reactive, problem-based and externally induced approach to managing ethics. Although basing ethics management interventions on dealing with and preventing current and possible future unethical behaviour are often effective in that it ensures compliance with rules and regulations, the approach is not necessarily conducive to the creation of sustained ethical cultures. Nor does the approach afford (mainly internal) stakeholders the opportunity to (...) be co-designers of the organisations ethical future. The aim of this paper is to present Appreciative Inquiry (AI) as an alternative approach for developing a shared meaning of ethics within an organisation with a view to embrace and entrench ethics, thereby creating a foundation for the development of an ethical cul- ture over time. A descriptive case study based on an application of AI is used to illustrate the utility of AI as a way of thinking and doing to precede and complement problem-based ethics management systems and interventions. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Touching TestAI and the Future of Human IntimacyMartha J. Reineke (bio)Each Friday, the New York Times publishes Love Letters, a compendium of articles on courtship. A recent story featured Melinda, a real estate agent, and Calvin, a human resources director.1 They had met at a market deli counter. On their first date, a lasagna dinner at Melinda's home, Calvin posed the question, "What are you looking for in (...) a man?" To his surprise, Melinda reached into her pocket and pulled out a two-page list, "Melinda's Husband Traits." Her 46-item list begins with "Christian, honor God, put Him first" and includes as entry number 37, "He likes to be touched and likes to touch me." Notwithstanding laudable features of Melinda's inventory, if David Levy, President of the International Computer Games Association and an expert on artificial intelligence (AI), is correct in his predictions, in the years ahead, women like Melinda who know exactly what they are looking for in a husband will desire something radically new: a robot. After all, artificial agents will soon possess or improve on every quality the romantically inclined previously have sought in a human partner. [End Page 123]Levi predicts robots will be preferred romantic partners by the middle of this century.2 Capable of providing erotic satisfaction and creating deep emotional bonds with human partners, these robots will be an achievable outcome of AI engineering that already has brought to market industrial and service robots, virtual pets, and caregiver robots for the elderly. Levi offers a research-based inventory of traits a robot will offer its human partner: kindness and understanding, a sense of humor, expressiveness, intelligence, and sociability.3 He pinpoints additional features that will facilitate romance: The ideal robot partner will elicit erotic attraction, give evidence of readiness for a serious relationship with its verbal assertions, and possess a capacity for mutual intimacy.4 Levi's argument is intentionally provocative, for he emphasizes the centrality of sex to future robot/human pairings, waxing rhapsodic about the performative superiority of robots to humans in the sexual act.5Elaborating on potential human love relationships with robots, Levi asserts that a robot will need to master imitation if a human is to want it as a romantic partner. Not only is imitation a central aspect of every human-to-human relationship, but also imitation has been identified by researchers as a crucial element in romantic desire.6 Levi states that human-to-human attraction is founded on similarities between prospective partners in appearance, personality, and social context. It is sustained only through what Levi calls "reciprocal liking."7 For romantic relationships between humans and robots to flourish, they too must engage in acts of reciprocal imitation.In highlighting the necessity of imitation in effective robot and human interactions, Levi draws close to René Girard's theory of "mimetic desire." According to Girard, imitation structures all "wanting,"8 delineating what we aspire to look like or hope to possess and determining what we value, take comfort in, or experience as a threat. Indeed, the individual is never the origin point of its desires. Each of us always desires mimetically—according to the desire of the other.9 As Scott Garrels confirms in Mimesis and Science, researchers studying imitation in developmental, comparative, and social psychology, as well as in neurophysiology and cognitive neuroscience, support Girard's claims: Imitation is fundamental in human life.10 Likewise, Girard concurs with Levi that romantic relationships are primary examples of imitative desire. As described by Girard in Deceit, Desire, and the Novel, imitative behaviors form the romantic triangles that feature prominently in fiction: for example, Odette and the many men enchanted by her in Proust's In Search of Lost Time; Anselmo, Lotario, and Camilla in Don Quixote; and Velchaninov and Troussotzki, along with Troussotzki's deceased wife and prospective fiancée, in Dostoyevsky's The Eternal Husband.11 [End Page 124]Notwithstanding similarities in Levi and Girard's views of imitation, they differ in a crucial respect. Only Girard acknowledges just how extraordinarily fraught mimesis can become. When imitators compete (e.g., for things, status, and experiences), they will contest... (shrink)

Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems and proposed technical solutions to address these, we argue that effective solutions require human engagement. Furthermore, there is a lack of research on how to motivate the adoption of these solutions and promote investment in designing AI systems that align with values (...) such as transparency and fairness from the outset. Drawing on insights from psychological theories, we assert the need to understand the values that underlie decisions made by individuals involved in creating and deploying AI systems. We describe how this understanding can be leveraged to increase engagement with de-biasing and fairness-enhancing practices within the AI healthcare industry, ultimately leading to sustained behavioral change via autonomy-supportive communication strategies rooted in motivational and social psychology theories. In developing these pathways to engagement, we consider the norms and needs that govern the AI healthcare domain, and we evaluate incentives for maintaining the status quo against economic, legal, and social incentives for behavior change in line with transparency and fairness values. (shrink)

This paper criticises the ideological dimension of the AI narrative. It does so by questioning the implicit assumptions behind its vision, which promises a world that automatically adapts to our desires before we even know them. These assumptions hinge on a misconception of the value of desire as residing exclusively with its fulfilment, warranting human manipulation for increased predictability. This social trajectory towards algorithmic governance, rather than delivering on the promised fulfilment, undermines our capacity to sustain the same desire that (...) it uses to justify its enterprise. (shrink)

In recent years, the European Union has advanced towards responsible and sustainable Artificial Intelligence (AI) research, development and innovation. While the Ethics Guidelines for Trustworthy AI released in 2019 and the AI Act in 2021 set the starting point for a European Ethical AI, there are still several challenges to translate such advances into the public debate, education and practical learning. This paper contributes towards closing this gap by reviewing the approaches that can be found in the existing literature (...) and by interviewing 11 experts across five countries to help define educational strategies, competencies and resources needed for the successful implementation of Trustworthy AI in Higher Education (HE) and to reach students from all disciplines. The findings are presented in the form of recommendations both for educators and policy incentives, translating the guidelines into HE teaching and practice, so that the next generation of young people can contribute to an ethical, safe and cutting-edge AI made in Europe. (shrink)

The present paper aims to review the value of Action Research in the evolution of sustainable mHealth. On the one hand, mHealth is a medically and economically massively expanding domain. On the other hand, the mHealth development suffers from a serious lack of sustainability, which has become particularly evident through the concept of “pilotitis.” The proposed methodological remedy shows a high congruence to the principle of AR. A quantitative and qualitative literature research is performed. Each result from the qualitative (...) literature research is analyzed with regard to aim, main focuses of AR methodology, study results and AR contribution to mHealth development. The principles of AR with regard to mHealth development are described, showing a strong connection between corresponding methodology and effects in health care. We analyze the quantitative contribution of AR to health care in general and show the typical phases of AR healthcare implementation. Furthermore, a quantitative literature research reveals that AR contributes to 6 % of mHealth publications. Our qualitative literature research discovers 27 studies dealing with AR contributions to mHealth. Main subjects of the direct contributions are Health Information Systems, Surgery, Conservative/internal medicine, Self-help and Medical Education. In summary, our review shows that AR contributes qualitatively and quantitatively to the mHealth development and that AR has a special potential to generate sustainable mHealth solutions.GraphicalOpen image in new window. (shrink)

Artificial intelligence is poised to eliminate millions of jobs, from finance to truck driving. But artisanal products are valued precisely because of their human origins, and thus have some inherent “immunity” from AI job loss. At the same time, artisanal labor, combined with technology, could potentially help to democratize the economy, allowing independent, small-scale businesses to flourish. Could AI, robotics and related automation technologies enhance the economic viability and environmental sustainability of these beloved crafting professions, perhaps even expanding their niche (...) to replace some job loss in other sectors? In this paper, we compare the problems created by the current mass production economy and potential solutions from an artisanal economy. In doing so, the paper details the possibilities of utilizing AI to support hybrid forms of human–machine production at the microscale; localized and sustainable value chains at the mesoscale; and networks of these localized and sustainable producers at the macroscale. In short, a wide range of automation technologies are potentially available for facilitating and empowering an artisanal economy. Ultimately, it is our hope that this paper will facilitate a discussion on a future vision for more “generative” economic forms in which labor value, ecological value and social value can circulate without extraction or alienation. (shrink)

This paper will examine the social and ethical impacts of using artificial intelligence (AI) in the agricultural sector. It will identify what are some of the most prevalent challenges and impacts identified in the literature, how this correlates with those discussed in the domain of AI ethics, and are being implemented into AI ethics guidelines. This will be achieved by examining published articles and conference proceedings that focus on societal or ethical impacts of AI in the agri-food sector, through a (...) thematic analysis of the literature. The thematic analysis will be divided based on the classifications outlined through 11 overarching principles, from an established lexicon (transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, dignity, sustainability, and solidarity). While research on AI agriculture is still relatively new, this paper aims to map the debate and illustrate what the literature says in the context of social and ethical impacts. It aim is to analyse these impacts, based on these 11 principles. This research will contrast which impacts are not being discussed in agricultural AI and which issues are not being discussed in AI ethics guidelines, but which are discussed in relation to agricultural AI. The aim of this is to identify gaps within the agricultural literature, and gaps in AI ethics guidelines, that may need to be addressed. (shrink)

Artificial intelligence (AI) assurance is an umbrella term describing many approaches—such as impact assessment, audit, and certification procedures—used to provide evidence that an AI system is legal, ethical, and technically robust. AI assurance approaches largely focus on two overlapping categories of harms: deployment harms that emerge at, or after, the point of use, and individual harms that directly impact a person as an individual. Current approaches generally overlook upstream collective and societal harms associated with the development of systems, such as (...) resource extraction and processing, exploitative labour practices and energy intensive model training. Thus, the scope of current AI assurance practice is insufficient for ensuring that AI is ethical in a holistic sense, i.e. in ways that are legally permissible, socially acceptable, economically viable and environmentally sustainable. This article addresses this shortcoming by arguing for a broader approach to AI assurance that is sensitive to the full scope of AI development and deployment harms. To do so, the article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation. It then reviews assurance mechanisms used in adjacent industries to mitigate similar harms, evaluating their strengths, weaknesses, and how effectively they are being applied to AI. Finally, it provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain. (shrink)

The purpose of this article was to validate the published artificial intelligence facilities management outsourcing relationships system by real business cases in the working environment. The research aims to inspire the modern FM professionals in different industries with some challenging and innovative concepts about FM outsourcing relationships between facilities owners and service providers. First, it will briefly introduce the theory of the FM outsourcing relationships system on how it can help the FM seniors and strategists to design their FM daily (...) strategies wisely and make their business more effective and productive. Second, it will also introduce what the research is practically doing in the stage of case study for test and verification. It is concluded that FM outsourcing categorization may help to define the appropriate relationships. This further detailed outcome generated from the AI can be considered a solid reference to define and explain the existing outsourcing relationships between the stakeholders and the service providers to assign an outsourcing category to the FM relationship between the client and service provider based on the learnt rules. (shrink)

An updated excerpt from the larger paper AI Human Impact. Excerpt explains why ESG investing requires Updating for the AI economy.

Artificial intelligence (AI) governance and auditing promise to bridge the gap between AI ethics principles and the responsible use of AI systems, but they require assessment mechanisms and metrics. Effective AI governance is not only about legal compliance; organizations can strive to go beyond legal requirements by proactively considering the risks inherent in their AI systems. In the past decade, investors have become increasingly active in advancing corporate social responsibility and sustainability practices. Including nonfinancial information related to environmental, social, and (...) governance (ESG) issues in investment analyses has become mainstream practice among investors. However, the AI auditing literature is mostly silent on the role of investors. The current study addresses two research questions: (1) how companies’ responsible use of AI is included in ESG investment analyses and (2) what connections can be found between principles of responsible AI and ESG ranking criteria. We conducted a series of expert interviews and analyzed the data using thematic analysis. Awareness of AI issues, measuring AI impacts, and governing AI processes emerged as the three main themes in the analysis. The findings indicate that AI is still a relatively unknown topic for investors, and taking the responsible use of AI into account in ESG analyses is not an established practice. However, AI is recognized as a potentially material issue for various industries and companies, indicating that its incorporation into ESG evaluations may be justified. There is a need for standardized metrics for AI responsibility, while critical bottlenecks and asymmetrical knowledge relations must be tackled. (shrink)

Digital transformation and “artificial intelligence (AI)”—which can more adequately be called “data-based systems (DS)”—comprise ethical opportunities and risks. Therefore, it is necessary to identify precisely ethical opportunities and risks in order to be able to benefit sustainably from the opportunities and to master the risks. The UN General Assembly has recently adopted a resolution aiming for ‘safe, secure and trustworthy artificial intelligence systems’. It is now urgent to implement and build on the UN General Assembly Resolution. Allowing humans and the (...) planet to flourish sustainably in peace and guaranteeing globally that human dignity is respected not only offline but also online, in the digital sphere, and in the domain of DS, requires two policy measures: (1) human rights-based data-based systems (HRBDS): HRBDS means that human rights serve as the basis of digital transformation and DS. (2) International Data-Based Systems Agency (IDA): IDA should be established at the UN as a platform for cooperation in the field of digital transformation and DS, fostering human rights, security, and peaceful uses of DS, as well as a global supervisory institution and regulatory authority in digital transformation and DS. The establishment of IDA is realistic because humanity has already shown in its past that we are able to not always “blindly” pursue the technically possible but also to limit ourselves to what is technically feasible when humanity and the planet are at stake. For instance, humans researched the field of nuclear technology, developed the atomic bomb, and detonated it several times. Nonetheless, the same humans limited research and development in the field of nuclear technology to prevent even worse consequences by establishing the International Atomic Energy Agency (IAEA) at the UN. (shrink)

Due to G-7 countries' commitment to sustaining United Nations Sustainable Development Goal 8, which focuses on sustainable economic growth, there is a need to investigate the impact of tax revenue and institutional quality on economic growth, considering the role of artificial intelligence (AI) in the G-7 countries from 2012 to 2022. Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique is used to analyze the data. The study's findings indicate a long-run equilibrium relationship among the variables under examination. The causality (...) results can be categorized as bidirectional, unidirectional, or indicating no causality. Based on the CS-ARDL results, the study recommends that G-7 governments and policymakers prioritize and strengthen the integration of AI into their institutions to stimulate growth in both the short- and long-term. However, the study cautions against overlooking the interaction between AI and tax revenue, as it did not demonstrate support for economic growth. While the interaction between AI and institutional quality shows potential for contributing to growth, it is crucial to implement robust measures to mitigate any potential negative effects that may arise from AI's interaction with tax systems. Therefore, the study suggests the development of AI-friendly tax policies within the G-7 countries, considering the nascent nature of the AI sector/industry. (shrink)

Sustainability is typically viewed as consisting of three forces, economic, social, and ecological, in tension with one another. In this paper, we address the dangers posed to societal sustainability. The concern being addressed is the very survival of societies where the rights of individuals, personal and collective freedoms, an independent judiciary and media, and democracy, despite its messiness, are highly valued. We argue that, as a result of various technological innovations, a range of dysfunctional impacts are threatening social and political (...) stability. For instance, robotics and automation are replacing human labor and decision-making in a range of industries; search engines, monetized through advertising, have access to, and track, our interests and preferences; social media, in connecting us to one another often know more about us than we ourselves do, enabling them to profit in ways which may not coincide with our well-being; online retailers have not only acquired the ability to track and predict our buying choices, but also they can squeeze vendors based on their outsize bargaining power; and, in general, virtual technologies have changed both the way we think and our sense of self. With the rising deployment of the Internet of Things, and developments in machine learning and artificial intelligence, the threats to individual freedoms and rights, societal cohesion and harmony, employment and economic well-being, and trust in democracy are being ratcheted up. This paper lauds the benefits and addresses the harm wrought by the high tech giants in Information and Communication Technologies. The search for rapidly growing revenues drives firms to accelerate product innovation without fully investigating the entire gamut of their impacts. As greater wealth accrues to the leaders of tech firms, inequalities within firms and societies are widening, creating social tensions and political ferment. We explore the ethical nature of the challenge employing a simple utilitarian calculus, complemented by approaches rooted in rights, justice, and the common good. Various options to address the challenges posed by ICTs are considered and evaluated. We argue that regulation may do little more than slow down the damage to society, particularly since societal values and political preferences vary internationally. Firms need to establish ethical standards, imbuing the upholders of these standards with sufficient authority, while creating a culture of morality. User involvement and activism, and shareholders’ concerns for the sustainability of societies on whose continued prosperity they depend, are imperative to humanity’s ability to decide the future direction of technology. (shrink)

A major challenge with the increasing use of Artificial Intelligence applications is to manage the long-term societal impacts of this technology. Two central concerns that have emerged in this respect are that the optimized goals behind the data processing of AI applications usually remain opaque and the energy footprint of their data processing is growing quickly. This study thus explores how much people value the transparency and environmental sustainability of AI using the example of personal AI assistants. The results from (...) a choice-based conjoint analysis with a sample of more than 1.000 respondents from Germany indicate that people hardly care about the energy efficiency of AI; and while they do value transparency through explainable AI, this added value of an application is offset by minor costs. The findings shed light on what kinds of AI people are likely to demand and have important implications for policy and regulation. (shrink)

Purpose The purpose of this paper is to present the conceptual model of an innovative methodology (SAT) to assess the social acceptance of technology, especially focusing on artificial intelligence (AI)-based technology. -/- Design/methodology/approach After a review of the literature, this paper presents the main lines by which SAT stands out from current methods, namely, a four-bubble approach and a mix of qualitative and quantitative techniques that offer assessments that look at technology as a socio-technical system. Each bubble determines the social (...) variability of a cluster of values: User-Experience Acceptance, Social Disruptiveness, Value Impact and Trust. -/- Findings The methodology is still in development, requiring further developments, specifications and validation. Accordingly, the findings of this paper refer to the realm of the research discussion, that is, highlighting the importance of preventively assessing and forecasting the acceptance of technology and building the best design strategies to boost sustainable and ethical technology adoption. -/- Social implications Once SAT method will be validated, it could constitute a useful tool, with societal implications, for helping users, markets and institutions to appraise and determine the co-implications of technology and socio-cultural contexts. -/- Originality/value New AI applications flood today’s users and markets, often without a clear understanding of risks and impacts. In the European context, regulations (EU AI Act) and rules (EU Ethics Guidelines for Trustworthy) try to fill this normative gap. The SAT method seeks to integrate the risk-based assessment of AI with an assessment of the perceptive-psychological and socio-behavioural aspects of its social acceptability. (shrink)

This paper is addressed to recent theoretical discussions of the Anthropocene, in particular Bernard Stiegler’s Neganthropocene (Open Universities Press, 2018), which argues: “As we drift past tipping points that put future biota at risk, while a post-truth regime institutes the denial of ‘climate change’ (as fake news), and as Silicon Valley assistants snatch decision and memory, and as gene-editing and a financially-engineered bifurcation advances over the rising hum of extinction events and the innumerable toxins and conceptual opiates that Anthropocene Talk (...) fascinated itself with—in short, as ‘the Anthropocene’ discloses itself as a dead-end trap…”. The objective of this paper is therefore twofold: (1) to discuss how the Anthropocene is appropriated to certain ideological discourses (paradoxically) to maintain the hegemony of precisely those systems of production that have most accelerated climate change etc.; (2) to consider how the factography of the Anthropocene is exploited in this process to mask the ideological character of industry-aligned “technocratic” environmental management. The paper is not concerned with specific case studies in terms of government and industry policy, or climate science, but rather with the ways in which the discourse of the Anthropocene has been inflected within the humanities and the broader cultural field—that is to say, ideologically, as a system or logic of meaning. How the Anthropocene “means” is, in this respect, a question of some importance. This paper does not attempt to address all the facets of this question, but focuses upon a central “apocalyptic” strain in the discourse of the Anthropocene drawn particularly from Francis Fukuyama’s millennial posthumanism and centred in the question of “sustainability” as catastrophe management—with the risk that real environmental degradation will become an alibi for a revived neoliberalism. In other words, that the critical Earth system transformations that characterise the Anthropocene are themselves commodities, and that the project of their amelioration is in process of defining a future (opportunistic) “crisis” rhetoric with a global political franchise. The ideological import of the Anthropocene stems precisely from the fact that it is planetary and, while catalysed by human agency, independent in its specific behaviour from it. The Anthropocene objectively presents as the contemporary counterpart of the Cold War doctrine of Mutually Assured Destruction and the most compelling argument for a new kind of technological “arms race.” But it also presents as the condition of an emerging ideological discourse which will determine how this race is run. From the discourse on “energy security” to the widespread “security crackdown” on environmental activists across the so-called developed & developing world, the Anthropocene has come to represent the co-option of a scientific factography for the thinly disguised resurgence of “ideological science” of the Fukuyamaesque variety (post-history, post-human). For Fukuyama, the true meaning of “posthuman” is thus the accomplishment of humanity’s historical mission. As the “End of History” designates an end of ideological struggle, so too the dénouement of the Anthropocene and the “ends of man” represent the accomplished purpose of species warfare: dominion, not simply over the world, but over all possible worlds. According to this narrative, science—like technology—must be uniquely at the service of the maintenance of the global order, organised around a universal appeal to “crisis management.” It is precisely for this reason that what calls itself post-human masks the return of an ever-more-apocalyptic Humanism. (shrink)

Artificial intelligence (AI) as a general-purpose technology has great potential for advancing the United Nations Sustainable Development Goals (SDGs). However, the AI×SDGs phenomenon is still in its infancy in terms of diffusion, analysis, and empirical evidence. Moreover, a scalable adoption of AI solutions to advance the achievement of the SDGs requires private and public actors to engage in coordinated actions that have been analysed only partially so far. This volume provides the first overview of the AI×SDGs phenomenon and its (...) related challenges and opportunities. The first part of the book adopts a programmatic approach, discussing AI×SDGs at a theoretical level and from the perspectives of different stakeholders. The second part illustrates existing projects and potential new applications. (shrink)

Galleries, libraries, archives and museums are striving to retain audience attention to issues related to cultural heritage, by implementing various novel opportunities for audience engagement through technological means online. Although born-digital assets for cultural heritage may have inundated the Internet in some areas, most of the time they are stored in “digital warehouses,” and the questions of the digital ecosystem’s sustainability, meaningful public participation and creative reuse of data still remain. Emerging technologies, such as artificial intelligence, are used to bring (...) born-digital archives to light, aiming to enhance the public’s engagement and participation. At the core of this debate lies both the openness of data and issues of privacy. How open to the public should born-digital archives be? Should everything be open and available online, and what does it take to achieve balance between openness and privacy, especially through AI initiatives? The study is qualitative and builds on the rationale of grounded theory. The role of AI development is critically investigated in relation to opening up born-digital archives online, by considering privacy and ethics issues. Grounded in the context of the author’s PhD research, the paper proposes a human-centred approach to AI development for democratising its development towards fairness and social inclusion, contrary to the stereotypical cliché of blackboxing, allowing space for the plurality of born-digital archives to flourish. (shrink)

Deepening and digging into the social side of AI is a novel but emerging requirement within the AI community. Future research should invest in an “AI for people”, going beyond the undoubtedly much-needed efforts into ethics, explainability and responsible AI. The article addresses this challenge by problematizing the discussion around AI shifting the attention to individuals and their awareness, knowledge and emotional response to AI. First, we outline our main argument relative to the need for a socio-technical perspective in the (...) study of AI social implications. Then, we illustrate the main existing narratives of hopes and fears associated with AI and robots. As building blocks of broader “sociotechnical imaginaries”, narratives are powerful tools that shape how society sees, interprets and organizes technology. An original empirical study within the University of Bologna collects the data to examine the levels of awareness, knowledge and emotional response towards AI, revealing interesting insights to be carried on in future research. Replete with exaggerations, both utopian and dystopian narratives are analysed with respect to some relevant socio-demographic variables (gender, generation and competence). Finally, focusing on two issues—the state of AI anxiety and the point of view of non-experts—opens the floor to problematizing the discourse around AI, sustaining the need for a sociological perspective in the field of AI and discussing future comparative research. (shrink)

This paper builds upon recent work in narrative theory and the philosophy of technology by examining the place of transparency and responsibility in discussions of AI, and what some of the implications of this might be for thinking ethically about AI and especially AI practices, that is, the structured social activities implicating and defining what AI is. In this paper, we aim to show how pursuing a narrative understanding of technology and AI can support knowledge of process and practice through (...) transparency, as well help summon us to responsibility through visions of possibility and of actual harms arising from AI practices. We provide reflections on the relations between narrative, transparency and responsibility, building an argument that narratives (about AI, practices, and those persons implicated in its design, implementation, and deployment) support the kind of knowing and understanding that is the aim of transparency, and, moreover, that such knowledge supports responsibility in informing agents and activating responsibility through creating knowledge about something that can and should be responded to. Furthermore, we argue for considering an expansion of the kinds of practices that we might legitimately consider ‘AI practices’ given the diverse set of (often materially embedded) activities that sustain and are sustained by AI that link directly to its ethical acceptability and which are rendered transparent in the narrative mode. Finally, we argue for an expansion of narratives and narrative sources to be considered in questions of AI, understanding that transparency is multi-faceted and found in stories from diverse sources and people. (shrink)

IntroductionIn highly developed countries, as many as 16 percent of children are physically abused each year. Traumatic brain injury (TBI) is the most common injury in non-accidental trauma (NAT) and is responsible for 80 percent of fatal NAT cases, with most deaths occurring in children younger than three years old. Cases of abusers who refuse withdrawal of life-sustaining medical treatment (LSMT) to avoid criminal charges have previously been reported. Therefore, we hypothesized that NAT is associated with a lower risk for (...) withdrawal LSMT in pediatric TBI.MethodsThe pediatric Trauma Quality Improvement Program database was analyzed (2014 to 2016) for patients aged 16 and younger with TBI and Glasgow Coma Scale (GCS) of 8 and lower on admission. Patients with a head Abbreviated Injury Scale (AIS) of 2 or less or who died within 48 hours were excluded. A multivariable logistic regression model was used for analysis.ResultsOf 2,209 TBI patients, 92 (4.2 percent) had withdrawal of LSMT. Compared to those without withdrawal of LMST, those with LMST had statistically similar median age (three years of age versus seven years, p = 0.07) and a higher rate of NAT (33.7 percent versus 13.5 percent, p < 0.001). The most common specified perpetrator was a father/stepfather/ male partner (70 percent). After adjusting for covariates, factors associated with higher risk for withdrawal of LSMT included age of less than three years (OR 2.38, CI 1.34-4.23, p < 0.05) and NAT (OR 1.86, CI 1.02-3.41, p < 0.05).ConclusionNAT is associated with increased risk for withdrawal of LSMT in pediatric TBI. Age of less than three years is similarly associated with a higher risk for withdrawal of LSMT. Future research in this population is needed to determine what other factors predict withdrawal of LSMT and what resources, such as social workers and/or ethics consults, are utilized. (shrink)