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This editorial introduces the special issue "The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.' We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important source of competitive advantage as it enables task augmentation and automation. However, while AI can create significant value, it is important to note that AI investments are fraught with risks and uncertainties. Thus, managers are likely to carefully evaluate potential AI investments before committing to investing. However, we know little about how managers' appraisal of AI influences their investment choices. Drawing upon theorization in the areas of business value of AI, agentic information systems (IS) appraisal, and time-situated agency, we extend existing theory in two ways: (1) development of an AI classification (foundational typology) that proposes two dimensions (action autonomy and learning autonomy) for classifying AI by type and level of autonomy; and (2) development of propositions that leverage time-situated agency and the AI classification to explicate how managers' delegation preferences influence their AI investment appraisal. This paper contributes a foundational theoretical platform for furthering AI investment appraisal research. In addition, the paper sets an agenda for future research in this area. [ABSTRACT FROM AUTHOR]

This editorial aims to present the papers accepted for the special issue (SI) 'Industry experiences of Artificial Intelligence (AI): benefits and challenges in operations and supply chain management.' First, we provide a brief introduction considering the relationship between AI and operations and supply chain management (OSCM) by highlighting some companies already using and practical insights. In sequence, we introduce the papers selected for the SI. The last section gives some intriguing and challenging research directions for scholars and industry practitioners by highlighting potential topics, research opportunities, and possible benefits. Through this SI, we look forward to helping industry practitioners, policy-makers, scholars, and all interested in this field to gain more knowledge about AI applications and insights in relation to OSCM. [ABSTRACT FROM AUTHOR]

The associability of artificial intelligence (AI) as a dual-use technology for nuclear material production (NMP) within the academic and practitioner communities remains widely neglected, and so a widening opportunity for AI to aid in illicit and covert non-peaceful applications exists. To address this emerging gap, this paper investigates the evolving and applicable uses of AI and finds broad evidence of its use to optimize performance, promote innovation, reduce costs, and enhance safety associated with the development and production of nuclear material. AI's use in this arena will, thereby, facilitate broader accessibility of peaceful uses of nuclear science and technology, while at the same time cause concerns that said improvements can aid the illicit development of nuclear weapons. As such, this paper advocates for a three-dimensional solution to manage the evolving dual-use concern of AI that involves advancing states-centric monitoring and regulation, promoting intellectual exchange between the nonproliferation sector and the AI industry, and encouraging AI industrial contributions. [ABSTRACT FROM AUTHOR]

Learning around artificial intelligence (AI)-powered technologies that attends to power is an urgent and widely felt priority among the learning sciences and CS ed broadly. Popular approaches to AI education focus on technical skills, with far less theoretical and practical work around critical and justice-centered AI learning. Adding to this literature, we discuss tool design and observed interactions in Drag vs AI workshops, where participants use hands-on makeup art as a medium for fooling, subverting, and refusing facial recognition. Our broader analysis asks how participants make sense of the technical and political aspects of AI, as they interact with AI through the Drag vs AI workshops' modes of aesthetic transformation, tinkering, and resistance. In this paper, we focus on participants' embodied algorithmic tinkering with AI and affordances for justice-centered computing education. Our analysis highlights how tinkering and play modes of interaction with AI materials can promote critical and agentive learning. [ABSTRACT FROM AUTHOR]

Deepfakes are a technological innovation that might be understood to violate the documentary film's relationship with the real. Yet documentary makers have been among the first screen producers to adopt this technology, using it to swap the faces or voices of people they present to their audiences, further disrupting the already complex relationship between the filmmaker and their subject. This paper investigates the growing practice of documentary deepfakes, using two case studies of documentary films from the period 2019–2022 that have used varied forms of deepfakery, examining the intentions of the filmmakers, the technical processes and the ways in which creative choice is both expanded and limited by the technology. An interview-based research methodology provides original insights into filmmakers' rationales when creating deepfakes. The paper reveals the contradictions inherent in deepfake practice, as described by one documentary filmmaker: 'I'm looking at deepfake as a way of telling the truth' (Benjamin Field, producer). A central theme is the ethics of documentary practice using manipulative AI. This paper contributes new insights to theoretical discourse around the digital manipulation of the moving image, discussing the growing disruption caused by deepfakes to documentary film culture and audiences. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of involving academic librarians in the development of ethical AI policies. The Academic Librarian Framework for Ethical AI Policy Development (ALF Framework) is introduced, recognizing librarians' unique skills and expertise. The paper discusses the benefits of their involvement, including expertise in information ethics and privacy, practical experience with AI tools, and collaborations. It also addresses challenges, such as limited awareness, institutional resistance, resource constraints, interdisciplinary collaboration, and evolving AI technologies, offering practical solutions. By actively involving librarians, institutions can develop comprehensive and ethical AI policies that prioritize social responsibility and respect for human rights. [ABSTRACT FROM AUTHOR]

Police departments around the world implement algorithmic systems to enhance various policing tasks. Ensuring such innovations take place responsibly – with public values upheld – is essential for public organisations. This paper analyses how public values are safeguarded in the case of MONOcam, an algorithmic camera system designed and used by the Netherlands police. The system employs artificial intelligence to detect whether car drivers are holding a mobile device. MONOcam can be considered a good example of value-sensitive design; many measures were taken to safeguard public values in this algorithmic system. In pursuit of responsible implementation of algorithms, most calls and literature focus on such value-sensitive design. Less attention is paid to what happens beyond design. Building on 120+ hours of ethnographic observations as well as informal conversations and three semi-structured interviews, this research shows that public values deemed safeguarded in design are re-negotiated as the system is implemented and used in practice. These findings led to direct impact, as MONOcam was improved in response. This paper thus highlights that algorithmic system design is often based on an ideal world, but it is in the complexities and fuzzy realities of everyday professional routines and sociomaterial reality that these systems are enacted, and public values are renegotiated in the use of algorithms. While value-sensitive design is important, this paper shows that it offers no guarantees for safeguarding public values in practice. [ABSTRACT FROM AUTHOR]

Research on Artificial Intelligence, especially in the field of machine learning, has exploded in the twenty-first century. AI research in universities has long been funded by a combination of government and corporate sources. The funding of AI research in the contemporary university includes technology companies as both funders and generators of research areas. This paper looks at the links between technology companies and AI research in three areas: first, the ways in which technology companies influence both the content and practices of AI research in universities; second, how university research policies enable conditions that blur traditional boundaries between corporate and academic AI research; and third, how an ethos of 'open science', that is increasingly corporatised, moves ideas about AI from universities to companies. We conclude that technology companies influence AI research within established feedback loops in the transformed relationships between economy, society, research, and the contemporary university. [ABSTRACT FROM AUTHOR]

We have entered an era where, in addition to us humans, systems can also think. It is imperative to decide how decision rights and authorities are allocated and distributed across interconnected co-cognitors: things that think. To address this question vital to the redesign of organizations, we reconceptualize cognitive reapportionment as the dynamic reallocation of decision rights and authorities across human and system co-cognitors capable of independent decision making. We articulate the Helix Model of Decision Journeys, wherein decision journeys, comprised of decision elements allocable to human or system co-cognitors, interweave and integrate business processes. The dynamic reapportionment of cognitive responsibilities across co-cognitors is dependent upon the type of scientific reasoning—deduction, induction, or abduction—at each decision element. We further propose two intertwined mechanisms for the art of letting go that facilitate the adoption of the Helix model. First, the nexus of omnipresent system co-cognitors collectively needs to evoke trust. Only when Technology Trust Thresholds are exceeded can decision making be reorganized and control relinquished to system co-cognitors. Second, the world of omnipresent cognitors will compel us to redesign, reprogram, and remix our workflow and business processes. Only through a purposeful effort to Redesign in the Remix Regime can the deduction-induction-abduction nature of decision tasks serve as the basis for their reapportionment. [ABSTRACT FROM AUTHOR]

Human activities are being increasingly regulated by means of technologies. Smart borders regulating human movement are no exception. I argue that the process of digitization – including through AI, Big Data and algorithmic processing – falls short of respecting (fundamental) rights to the extent to which it ignores what I term to be the problem of indeterminacy. While adopting a data feminist approach in this paper, assuming that data is the 'new oil', that is power, I begin theorizing indeterminacy from the imminent risks of datafication as a new instrument of oppression perpetuating injustice and widening inequality gaps. I conclude that technologies regulating human activities must stand ethical scrutiny, especially if they can and do result in (human) rights violations. Unlike the oil being extracted from the ground, data is de facto extracted from people endowed with agency, autonomy, rights and contexts – all which ought to be respected and protected. [ABSTRACT FROM AUTHOR]

Artificial Intelligence has been playing a profound role in the global economy, social progress, and people's daily life. With the increasing capabilities and accuracy of AI, the application of AI will have more impacts on manufacturing and service areas in the era of industry 4.0. This study conducts a systematic literature review to study the state-of-the-art on AI in industry 4.0. This paper describes the development of industries and the evolution of AI. This paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry 4.0. The findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of AI. In the era of industry 4.0, AI system will become an innovative and revolutionary assistance to the whole industry. [ABSTRACT FROM AUTHOR]

This paper examines how AI ethics has been developed at the national level in Japan, and what this process reveals about broader Japanese state imaginaries of how advanced technology should be developed and used, and what a future with these technologies should look like. Key developments in the Japanese government's approach to AI ethics and governance between 2014 and 2023 are laid out, based on an analysis of official reports and policy documents supplemented by data collected via semi-structured interviews with three expert members of the committees that formulated several key sets of ethical principles. The paper considers Japan's positioning in the global race to develop AI ethics principles over this period, as well as the imaginary of AI within the wider historical context of imaginaries about the knowledge society in Japan. I suggest three ways in which AI ethics has been understood and instrumentalized in the Japanese context, and argue that the main methodology used to date—ELSI—complements the government's utopian and techno-determinist imaginaries of the future while concealing a deeply conservative approach that serves to reproduce structural inequalities and discrimination despite the apparent internationalism and progressive values that are repeatedly expressed in state-promoted ethical principles. [ABSTRACT FROM AUTHOR]

We seem to be on the cusp of an AI-driven revolution in military affairs. Scholars have explored many aspects of this revolution but one of the most vibrant debates has addressed the question of lethal autonomous weapons. Some scholars believe that autonomous weapons, and especially autonomous drone swarms, are about to colonise the battlefield. This paper assesses this argument. It identifies three common mistakes in discussions of lethal autonomy. Scholars overestimate the capability of autonomous drone swarms. They underestimate their dependence on other weapon systems. Finally, they presume that autonomous weapons will favour the offence. This paper rejects all three claims. [ABSTRACT FROM AUTHOR]

AI is a technological advancement used nowadays extensively by telecoms to take decisions based on the vast amounts of data they own and to optimise their daily operations. Hence, a question being raised amongst experts in this industry is how AI can be applicable in the various functions of telecoms. This paper showcases how the telecommunication industry could adopt Artificial Intelligence mechanisms into daily tasks and operations, in order to better utilise available data and accelerate digital transformation. The scope of this paper is to analyse and explore the opportunities and the challenges that have risen in telecommunications organisations, but more precisely, describes experiences from the IT Innovation Center of OTE Group that investigates and validates AI-related technologies in real business scenarios, aiming to boost and even further the organisation's digital transformation and engagement in future markets. However, as depicted in this paper, although the numerous opportunities, telecoms face many obstacles which they try to overcome in this AI journey. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive survey of the state-of-the-art use of Artificial Intelligence (AI) and Machine Learning (ML) in the field of cybersecurity. The paper illuminates key applications of AI and ML in cybersecurity, while also addressing existing challenges and posing unresolved questions for future research. The paper also emphasizes the ethical and legal implications associated with their implementation. The researchers conducted a thorough survey by reviewing numerous papers and articles from respected sources such as IEEE, ACM, and Springer. Their focus centered on the latest AI and ML breakthroughs in cybersecurity, while also exploring current challenges and open research questions. The results indicate that integrating AI and ML into cybersecurity systems shows great potential for future research and development. Intrusion detection and response, malware detection, and network security are among the most promising applications identified. According to the survey, 45% of organizations have already implemented AI and ML in their cybersecurity systems, while an additional 35% plan to do so. However, 20% of organizations believe that it is not yet the right time for adopting these technologies. Overall, this paper serves as a reliable reference for researchers and practitioners in the field of cybersecurity, offering a comprehensive overview of the use of AI and ML. It not only highlights the potential applications but also addresses the challenges and research gaps. Additionally, the paper raises awareness about the ethical and legal considerations associated with leveraging AI and ML in the cybersecurity domain. [ABSTRACT FROM AUTHOR]

The present paper looks at one of the most thorough articles on the intelligence of GPT, research conducted by engineers at Microsoft. Although there is a great deal of value in their work, I will argue that, for familiar philosophical reasons, their methodology, ‘Black-box Interpretability’, is wrongheaded. But there is a better way. There is an exciting and emerging discipline of ‘Inner Interpretability’ (and specifically Mechanistic Interpretability) that aims to uncover the internal activations and weights of models in order to understand what they represent and the algorithms they implement. Black-box Interpretability fails to appreciate that how processes are carried out matters when it comes to intelligence and understanding. I can’t pretend to have a full story that provides both necessary and sufficient conditions for being intelligent, but I do think that Inner Interpretability dovetails nicely with plausible philosophical views of what intelligence requires. So the conclusion is modest, but the important point in my view is seeing how to get the research on the right track. Towards the end of the paper, I will show how some of the philosophical concepts can be used to further refine how Inner Interpretability is approached. [ABSTRACT FROM AUTHOR]

This paper examines ways in which the ethics of data-driven technologies might be (re)politicised, particularly where educational institutions are involved. The recent proliferation of principles, guidelines, and frameworks for ethical 'AI' (artificial intelligence) have emerged from a plethora of organisations in recent years, and seem poised to impact educational governance. This trend will be firstly shown to align with a narrow form of ethics - deontology - and overlook other potential ways ethical reasoning might contribute to thinking about 'AI'. Secondly, the attention to ethical principles will be suggested to focus excessively on the technology itself, with the effect of masking political concerns for equity and justice. Thirdly and finally, the paper will propose a more radical form of participation in ethical decision-making that not only challenges the assumption of universal consensus, but also draws more authentically on the capacities for debate, contestation, and exchange inherent in the educational institution. [ABSTRACT FROM AUTHOR]

The study seeks to understand how the AI ecosystem might be implicated in a form of knowledge production which reifies particular kinds of epistemologies over others. Using text mining and thematic analysis, this paper offers a horizon scan of the key themes that have emerged over the past few years during the AIEd debate. We begin with a discussion of the tools we used to experiment with digital methods for data collection and analysis. This paper then examines how AI in education systems are being conceived, hyped, and potentially deployed into global education contexts. Findings are categorised into three themes in the discourse: (1) geopolitical dominance through education and technological innovation; (2) creation and expansion of market niches, and (3) managing narratives, perceptions, and norms. [ABSTRACT FROM AUTHOR]

The continuing prominence of machine learning has led to an increased focus on enhancing the inference performance of edge devices to reduce latency and improve efficiency. Two widely adopted strategies for accelerating computational performance are quantisation and the utilisation of AI hardware accelerators. Each type of accelerator or inference engine offers distinct advantages, with accelerators primarily designed to optimise neural network operations. In this paper, we present an innovative method for integrating TVM's quantisation flow with the MediaTek Neuropilot AI accelerator. We outline the process of converting the TVM relay intermediate-representation quantised neural network dialect model to a tensor-oriented quantisation format, with the aim of harnessing the full potential of both TVM and MediaTek NeuroPilot. This integration enables more efficient neural network inference while preserving the accuracy of the results. We assessed the effectiveness of our proposed integration by conducting a series of experiments and comparing the performance of our approach with that of TVM equipped with an autotuning mechanism. The findings indicate that our approach substantially outperforms TVM in both floating-point model inference and quantised model inference, with inference speedups of up to 11× and up to 70×, respectively. These results underscore the potential of our approach in accelerating AI performance across a diverse range of applications and edge devices. Moreover, a key contribution of our work is providing a valuable practical method for other hardware companies interested in integrating TVM with their own accelerators to achieve performance gains. [ABSTRACT FROM AUTHOR]

Normative theorists of migration are beginning to shift their focus away from an earlier obsession with whether the 'liberal' or 'legitimate' state should have a right to exclude, and toward evaluation of how states engage in immigration control. However, with some notable exceptions – such as work of Rebecca Buxton, David Owen, Serena Parekh, and Alex Sager – this work tends not to focus on the global coordination of such control, and is still largely concerned with issues of membership. In this paper I aim to show the value of shifting normative attention to the fundamentally interdependent nature of state control of migration, and the management of all forms of movement – not just settlement. This global management is greatly facilitated by the rapid digitisation of border controls. As such, I outline three aspects of the way digital border controls work – profiling, biometric identification, and the data sharing practices upon which they rest – and highlight ethical challenges of accountability, consent and the reach of the state, and entrenching global inequalities in access to movement. Ultimately, I hope to show that the globally interconnected nature of migration management is a combination of practices that normative theorists of migration should turn their attention to. [ABSTRACT FROM AUTHOR]

The emergence of ChatGPT and its ability to imitate student work has precipitated a crisis in education. Responses range from practical efforts to detect or prevent ChatGPT use to existential reflection about learning and assessment. However, the crisis is also epistemological, with educators struggling to determine what students know and fearing that student knowledge is diminishing. The educational philosophies of Socrates, Dewey, and Freire suggest that recognition of ignorance of different kinds (inquisitive, predictive, and practical) on the part of educators is uncomfortable but essential for improving the aims and methods of education. In this way, ChatGPT and the philosophy of education can be mutually illuminating. [ABSTRACT FROM AUTHOR]

Critical to the resilience of utility power lines, tree failure assessments have historically been performed via costly manual inspections. In this paper, we develop a convolutional neural network (CNN) to predict tree failure likelihood categories (Probable, Possible, Improbable) under three classification strategies. The CNN produced the best performance under the Probable/Possible vs. Improbable strategy, achieving a recall score of 0.82. We also perform a visual analysis of the predictions via Grad-CAM++ heatmaps, indicating an approach for incorporating interpretability into model selection. Benchmarking the results of our model against those produced by two state-of-the-art CNNs (ResNet-50 and Inception-v3), we show that our relatively simple model produces better results in a computational time that is three times faster. Via this novel framework, we demonstrate the potential of artificial intelligence to automate and consequently reduce the costs of tree failure likelihood assessments in proximity to power lines, thereby promoting sustainable infrastructure. [ABSTRACT FROM AUTHOR]

The emergence of ChatGPT and its ability to imitate student work has precipitated a crisis in education. Responses range from practical efforts to detect or prevent ChatGPT use to existential reflection about learning and assessment. However, the crisis is also epistemological, with educators struggling to determine what students know and fearing that student knowledge is diminishing. The educational philosophies of Socrates, Dewey, and Freire suggest that recognition of ignorance of different kinds (inquisitive, predictive, and practical) on the part of educators is uncomfortable but essential for improving the aims and methods of education. In this way, ChatGPT and the philosophy of education can be mutually illuminating. [ABSTRACT FROM AUTHOR]

This paper examines how elite transnational policy and research organizations are framing emergent technologies as a hypermodern risk. It outlines how innovations in artificial intelligence and machine learning are feeding into global policy imaginaries and responses oriented to education and skills as adaption and minimization of potential disruption flowing from unpredictable workforce transitions. Drawing on research reports by the Organization for Economic Cooperation and Development, World Economic Forum, and McKinsey, the paper suggests that this emphasis on risk and uncertainty represents a shift in elite policy discourse. The paper discusses how automated uncertainty is feeding into educational policy trajectories that seek to mitigate disruption through digital learning and work synergies via agile learners of risk. The cognitive structuring of these policy trajectories reflects a closed ideological loop deflecting analysis from political economy and alternative policy futures within hypermodern capitalism. [ABSTRACT FROM AUTHOR]

AbstractThis column considers challenges and opportunities AI generative chatbots pose to proprietary library databases. It briefly reviews evolution from paper library indices to Web-based databases then reasons why current chatbots pose greater challenges to database relevancy than when Web engines first emerged. Library databases and free chatbots are compared in terms of identifying information needs, and locating, evaluating, and using information. Q/A with EBSCO are collected on challenges and opportunities chatbots provide them. Discussion centering around questions intended for database vendors ensues, along with a conclusion on potential information literacy benefits if library databases adopt chatbot search platforms. [ABSTRACT FROM AUTHOR]

This paper adds to the discussion on the legal personhood of artificial intelligence by focusing on one area not covered by previous works on the subject – ownership of property. The author discusses the nexus between property ownership and legal personhood. The paper explains the prevailing misconceptions about the requirements of rights or duties in legal personhood, and discusses the potential for conferring rights or imposing obligations on weak and strong AI. While scholars have discussed AI owning real property and copyright, there has been limited discussion on the nexus of AI property ownership and legal personhood. The paper discusses the right to own property and the obligations of property ownership in nonhumans, and applying it to AI. The paper concludes that the law may grant property ownership and legal personhood to weak AI, but not to strong AI. [ABSTRACT FROM AUTHOR]

Marie Darrieussecq's Notre vie dans les forêts (2017) is a science-fiction novel about cloning. Alternatively, it could be described as a notebook composed by a fugitive clone named Viviane writing before her imminent death in the tunnels constructed beneath a forest on the ghostly fringes of an unnamed city. Viviane has unplugged from this socially engineered illusion by following one of her former patients and a member of a fugitive group of scavengers and hackers who free their anesthetized clones. But, the fugitives comically struggle to prepare their clones—who just want to play—for the egalitarian, just, and liberatory future in which they hope. In this paper, I first provide a close reading of the narrative of Darrieussecq's novel, focusing on how it teaches us to read it subjunctively and contingently. I then argue that the clones' playfulness expresses an energy source resistant to exploitation. I conclude with a hypothesis: the book itself could be understood as an instance of artificial intelligence that resists exploitation because it teaches us to read subjunctively, contingently, and playfully. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is gaining importance as its applications are found in many critical infrastructure sectors (e.g., Industry 4.0, healthcare, transportation, and commercial facilities). This reinforces the importance of investigating the security risks associated with IoT deployment. Hence, in this paper, we perform a comprehensive review of the literature on penetration testing of IoT devices and systems. Specifically, a total of 99 articles published between 2015 and 2021 was reviewed to identify existing and potential IoT penetration testing applications and proposed approaches. We finally provide recent advances of AI-enabled penetration testing methods that can notably be performed at the network edge. [ABSTRACT FROM AUTHOR]

This paper examines the political economy of artificial intelligence (AI) and education in China, through an analysis of government policy and private sector enterprise. While media and policy discourse often portray China's AI development in terms of a unified national strategy, and a burgeoning geopolitical contestation for future global dominance, this analysis will suggest a more nuanced internal complexity, involving differing regional networks and international corporate activity. The first section considers two key policy documents published by the central Chinese government, which are shown to implicate educational institutions as influential actors in national and regional strategies for AI development, with a significant role in plans to train domestic expertise. The second section outlines three prominent private education companies: New Oriental Group, Tomorrow Advancing Life (TAL), and Squirrel AI. These companies are selected to represent important aspects of China's development of educational AI applications, including the influence of a well-established private education sector, and a growing interest in international corporate activity. The paper concludes with the suggestion that while central government policy reserves a significant role for education in the national AI strategy, the private sector is utilising favourable political conditions to rapidly develop educational applications and markets. [ABSTRACT FROM AUTHOR]

We are teetering on the precipice of the imminent Fourth Industrial Revolution. In this new age, systems are set to become more densely intraconnected and interconnected, and massive sociotechnical systems exhibiting unprecedented levels of complexity will increasingly take hold. At the dawning of this new age, the Ergonomics discipline must reflect on its preparedness for tackling problems in these novel systems. This paper engages in this reflection by putting forth a critical commentary on the implication of these changes on the discipline and discusses the utility of our current methods in this new paradigm. A resulting Radical Systems Thinking in Ergonomics Manifesto is put forward – a set of mandates to guide practitioners and researchers in the development of new methods capable of coping with these imminent challenges. From the manifesto are derived a series of capability requirements for future computational modelling approaches in Ergonomics. Practitioner summary: The goal of this paper was to inspire the Ergonomics community to pursue further applications involving computational modelling approaches such as Agent-Based Modelling. It presents a manifesto for the future of the discipline, and from this the capabilities that future computational modelling approaches need to possess. Abbreviations: 1IR: first industrial revolution; 2IR: second industrial revolution; 3IR: third industrial revolution; 4IR: fourth industrial revolution; ABM: agent based model; AI: artificial intelligence; AoF: allocation of function; CPA: cyber physical attack; CPS: cyber-physical system; CWA: cognitive work analysis; DDoS: distributed denial of service; EAST: event analysis of systemic teamwork; FRAM; functional resonance analysis method; HCI: human-computer interaction; HERA: human error and recovery assessment; HET: human error template; HMC: human-machine cooperation; IoT: internet of things; RSTEM: radical systems thinking in ergonomics manifesto; SAI: situated artificial intelligence; STAMP: systems theoretic accident model and processes; TRACEr: technique for the retrospective and predictive analysis of cognitive errors in air traffic control [ABSTRACT FROM AUTHOR]

The intelligent segmentation of high-resolution remote sensing (HRS) image, also called as dense prediction task for HRS image, has been and will continue to be important research in the remote sensing community. In recent years, the growing wave of artificial intelligence (AI) technology has introduced innovative paradigms to this domain, yielding outstanding results and overcoming many challenges with conventional segmentation techniques. This paper provides a comprehensive review of these intelligent segmentation methodologies, including traditional pattern recognition, convolution neural network (CNN)-based, and Transformer-based techniques. However, the explosive but incomplete development of intelligent segmentation techniques also poses more challenges for earth observation experts, the most of which is the technical interpretability. Consequently, we consider these segmentation techniques in the aspect of explainable artificial intelligence (XAI). Data-centric XAI thinks the practical applications of the segmentation model while model-centric XAI will facilitate the understanding of decision-making processes and the adjustment of structural features. Moreover, this review identifies novel research questions and provides constructive insights and recommendations to HRS image segmentation tasks, which may shed new light on the intelligent segmentation methods within the remote sensing image understanding community. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) rapidly adapts to diverse audience engagement modes in Digital Arts, either interactive or non-interactive forms. These engagements create potential alliances for intelligent eco-didactic environments in the public realm. Studies show that interactivity positively affects the learning experience; hence, the coalition between AI and Digital Art has the potential to result in enhanced eco-art experiences. This collaboration could augment the eco-message and lead to behavior shift. This paper explores the interactive engagement modes in different art mediums to identify their eco-didactic potential. The study adopts a mixed methods approach, engaging with causal-comparative qualitative content analysis research. We collected secondary data from various mediums to define the characteristics of the engagement modes in eco-art, digital art, and AI artworks. Finally, we interviewed mixed-media artists to explore the technologies used in these art mediums, the different engagement modes they adopt, and eco-didactic possibilities. As a result, we found that incorporating aspects such as interactivity, coherence, aesthetics, playfulness, and meaning, can increase the impact of eco-didactic experiences. In addition, AI creates new possibilities for these experiences with its popularity and features such as real-time data utilization, personalization, and generative reciprocal dialogues which facilitate the understanding of complex environmental issues. [ABSTRACT FROM AUTHOR]

Technological development is a key factor for shaping social life, transforming the ways in which societies organize their production, mobility and communication processes. Since the beginning of industrialization, the pace of the transformations brought about by technological change has increased dramatically, and has further accelerated since the advent of the new digital technologies. These development processes has also impacts in terms of social, economic and environmental costs, a fact that has been addressed in the past few decades by various social movements as well as theoreticians, becoming a key issue in political and social discussion agendas. In this paper, we outline a historical perspective of these changes and their effects, from pre-industrial, industrial, post-Fordism and network societies, and we focus on the mobilizing potential of technological change. We analyze the role that technological interfaces play today in social transformation, as well as the implications for our present day that our interactions become increasingly intermediated by digital technologies. Finally, we discuss digital technologies and their impacts on social inequity. We argue that a public and democratic agenda comprising both development and technological issues should be put in place for guaranteeing social development processes. [ABSTRACT FROM AUTHOR]

Despite their importance in the ethics of digital technologies, trust and trustworthiness do not appear in the international law on digital technologies. This surprising absence is the point of departure of this paper. On the basis of a survey of philosophical and empirical scholarship on trust in the context of digital technologies (especially AI), the article proceeds by identifying relevant norms in international law which can be considered to operationalise relevant dimensions of trust and trustworthiness. By clustering these norms, a technical conception of trust appears, focused on securing the trustworthiness of an AI system. This conception is based on the framing of an AI system as an agent and on the premise that the role of the law is to promote the uptake of technologies. This technical conception of trust should be enriched by way of three relevant extensions, each addressing in turn: the responsibility of developers, the power relations between users and developers of AI systems, and the benefits associated with the use of AI systems. This extended conception of trust also extends the realm of relevant legal norms that can be used to address AI-powered actions. [ABSTRACT FROM AUTHOR]

The ubiquitous use of artificial intelligence (AI) can bring about both positive and negative consequences for individuals and societies. On the negative side, there is a concern not only about the impact of AI on first-order discrete individual rights (such as the right to privacy, non-discrimination and freedom of opinion and expression) but also about whether the human rights framework is fit for purpose relative to second-order challenges that cannot be effectively addressed by discrete legal rights focused upon the individual. The purpose of this article is to map the contours and utility of the concept of human dignity in addressing the second-order challenges presented by AI. Four key interpretations of human dignity are identified, namely: non-instrumentalization of the human person; the protection of certain vulnerable classes of persons; the recognition and exercise of inherent self-worth (including the exercise of individual autonomy); and a wider notion of the protection of humanity. Applying these interpretations to AI affordances, the paper argues that human dignity should foreground three second-order challenges, namely: the disembodiment of empiric self-representation and contextual sense-making; the choice architectures for the exercise of cognitive autonomy; and, the experiential context of lived experiences using the normative framework of human vulnerability. [ABSTRACT FROM AUTHOR]

Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology's human-centering. The result is objective investment guidance, as well as investors empowered to act in accordance with their own values. Incorporating ethics into financial decisions is a strategy that will be recognized by participants in environmental, social, and governance investing, however, this paper argues that conventional ESG frameworks are inadequate to companies that function with AI at their core. Fully accounting for contemporary big data, predictive analytics, and machine learning requires specialized metrics customized from established AI ethics principles. With these metrics established, the larger goal is a model for humanist investing in AI-intensive companies that is intellectually robust, manageable for analysts, useful for portfolio managers, and credible for investors. [ABSTRACT FROM AUTHOR]

Asthma, an increasingly prevalent chronic respiratory condition, incurs significant economic costs worldwide. Artificial Intelligence (AI), particularly Machine Learning (ML), has been widely recognized as transformative when applied to asthma care. This commentary investigates how AI and ML may improve clinical outcomes while alleviating some of the costs associated with asthma care. AI's powerful analytical abilities could usher in an unprecedented era of preventive measures, particularly by identifying at-risk populations and anticipating environmental triggers. ML shows promise for enhancing real-time monitoring, early detection, and tailored treatment strategies in paediatric asthma, potentially reducing hospitalizations and emergency care costs. Emerging AI-powered wearable technologies are catalysing a revolutionary shift in patient monitoring, providing proactive interventions. Although optimistic, this commentary highlights a gap in empirical studies evaluating the cost-effectiveness of AI in asthma care and stresses the need for larger datasets to accurately represent the economic benefits of AI solutions. Additionally, this paper emphasizes the ethical considerations surrounding data privacy and algorithmic bias, which are vital for the successful and equitable integration of AI into healthcare settings. This editorial underscores the urgent necessity of conducting thorough analyses to assess all economic implications, facilitate optimized resource allocation, and foster a nuanced understanding of AI/ML technologies in asthma management that may reduce costs to healthcare systems. [ABSTRACT FROM AUTHOR]

In this paper, we assess how recent technology advances have changed the way we coordinate. After a brief discussion of the common challenges to effective coordination, we highlight some important implications of technology on addressing informational and behavioral frictions. We focus on discussing the effects of three specific technology developments including artificial intelligence (AI), automation, and blockchain, on the choice of coordination modes. We argue that technology is shifting the boundaries between firms and markets and is opening the door to new research directions. [ABSTRACT FROM AUTHOR]

The role of libraries in preparing students to thrive during their studies and innovate after university is growing in importance. Information is more easily accessible through digital channels and is increasingly abundant. Generative Artificial intelligence (AI) adds to this reality and increases the need for digitally driven information literacy skills. This paper aims to guide librarians by discussing the digitalization of information creation, retrieval, and use. It recommends the training of both digital and information literacy for students. Librarians are called upon to provide clear guidelines to their universities to steer the use of generative AI. The implications of digital information sources and generative AI are discussed with the role of librarians in context. Information literacy and digital literacy are related. Academic libraries should include digital topics in information literacy training. Information literacy should be trained at the start of university education and before students begin dissertation writing. University libraries should propose guidelines for the use of generative artificial intelligence tools by students. [ABSTRACT FROM AUTHOR]

In this paper, an Artificial Intelligence (AI) model is constructed for the behavior prediction, i.e. Young's modulus, of polymer/carbon-nanotube (CNTs) composites. The AI is proposed to overcome the difficulties when studying the properties of novel composite materials, for example the time-consuming of experimental studies of resource-consuming of other numerical methods. Artificial Neural Network (ANN) model was chosen and optimized in architecture based on a parametric study. The main objective of this study is to firstly confirm that the proposed AI method performs well for nanocomposites and it can then be optimized in terms of computational time and resources in further studies. The obtained results have shown that the proposed model exhibits great performance in both training and testing phases, where the correlation coefficient is 0.986 for training part and 0.978 for the testing part. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become an important driver of economic growth and innovation. With rapid advances in AI, firms have a strategic opportunity to re-envision the cognitive reapportionment of tasks between humans, AI, and non-AI technologies. In doing so, firms can transform and dramatically elevate value creation through their business models, processes, and market offerings. We focus on a key risk, patent infringement litigation (PIL), that can adversely impact a firm's value creation with AI. We posit and demonstrate that firms facing AI-PILs would have more negative short term abnormal returns compared to firms facing non-AI PILs. We further suggest that the abnormal returns are moderated by the types of plaintiffs and AI patents in which the abnormal returns are more negative when the plaintiffs are non-practicing entities (versus practicing entities) and when the AI patents at suit concern expertise-driven (versus data-driven) AI. Exploring the moderators jointly, we find evidence that for data-driven AI patents, the negative abnormal returns are stronger when the plaintiffs are practicing entities, but for expertise-driven AI patents, the negative abnormal returns are stronger when the plaintiffs are non-practicing entities. [ABSTRACT FROM AUTHOR]

The ethics of robots and artificial intelligence (AI) typically centers on "giving ethics" to as-yet imaginary AI with human-levels of autonomy in order to protect us from their potentially destructive power. It is often assumed that to do that, we should program AI with the true moral theory (whatever that might be), much as we teach morality to our children. This paper argues that the focus on AI with human-level autonomy is misguided. The robots and AI that we have now and in the near future are "semi-autonomous" in that their ability to make choices and to act is limited across a number of dimensions. Further, it may be morally problematic to create AI with human-level autonomy, even if it becomes possible. As such, any useful approach to AI ethics should begin with a theory of giving ethics to semi-autonomous agents (SAAs). In this paper, we work toward such a theory by evaluating our obligations to and for "natural" SAAs, including nonhuman animals and humans with developing and diminished capacities. Drawing on research in neuroscience, bioethics, and philosophy, we identify the ways in which AI semi-autonomy differs from semi-autonomy in humans and nonhuman animals. We conclude on the basis of these comparisons that when giving ethics to SAAs, we should focus on principles and restrictions that protect human interests, but that we can only permissibly maintain this approach so long as we do not aim at developing technology with human-level autonomy. [ABSTRACT FROM AUTHOR]

The AI-enabled intelligent learning system (AEILS) is able to provide personalised and intelligent tutoring and is more capable of meeting individuals’ need. Nevertheless, limited studies focused on the effect of AI-specific factors on user behaviour. To fill this research gap, we identified the AEILS-specific affordances (i.e. interactivity, personalisation, competition, convenience) and explored their effect on user engagement and foreign language speaking anxiety (FLSA). In this paper, we integrate quantitative and qualitative studies to explore user behaviour in AEILS. Survey data was collected from 457 respondents and analysed using structural equation modelling with the smart-PLS software. The results showed that AEILS-specific affordances significantly affect flow experience and self-expansion, thus facilitating user engagement and alleviating FLSA. Semi-structured interviews were conducted to corroborate the findings of the quantitative study. These findings highlighted the importance of technology affordance in AEILS. This study contributed to the literature on IS and education by incorporating context-specific factors into account. [ABSTRACT FROM AUTHOR]

Predicting the development of artificial intelligence (AI) is a difficult project – but a vital one, according to some analysts. AI predictions are already abound: but are they reliable? This paper starts by proposing a decomposition schema for classifying them. Then it constructs a variety of theoretical tools for analysing, judging and improving them. These tools are demonstrated by careful analysis of five famous AI predictions: the initial Dartmouth conference, Dreyfus's criticism of AI, Searle's Chinese room paper, Kurzweil's predictions in theAge of Spiritual Machines, and Omohundro's ‘AI drives’ paper. These case studies illustrate several important principles, such as the general overconfidence of experts, the superiority of models over expert judgement and the need for greater uncertainty in all types of predictions. The general reliability of expert judgement in AI timeline predictions is shown to be poor, a result that fits in with previous studies of expert competence. [ABSTRACT FROM AUTHOR]

Two films by Viera Čákanyová portray the momentous yet fragile landscapes of Antarctica. Frem (Slovakia 2019) depicts vast and arid vistas from the perspective of a drone whose movements are based on artificial intelligence algorithms. White on White (Slovakia 2020) was shot during the same expedition; this time the camera is held by Čákanyová. We see two quite different types of cinematography: one is a neutral, emotionally detached, machine-like view; the other is embodied, vulnerable and affective. A seal bleeding on an ice floe, a melting iceberg crumbling into the sea, a human seeking shelter in the biting cold – the scenes provide the viewer with very different potentialities for emotional responses depending on whether the camera is held by a human hand and guided by a human eye, or the gaze is that of a drone guided by computational algorithms. In this paper, I will examine the following questions: What role does imagining the non-human, the superhuman or the beyond human play in our visual culture? Does the gaze of the machine provide us with something useful? Or does it deprive us of something that is needed for us to understand our position in the world? Čákanyová's films are my main discussion partners in investigating these topics. My conclusions are inspired by Merleau-Ponty's redefinition of our relationship with nature. He writes about the non-human, not as a result of transcending human experience but rather as an acknowledgement of our kinship with and immersion in nature. [ABSTRACT FROM AUTHOR]

Firms are beginning to use chatbots to collect information from users (e.g., online reviews), a task traditionally done through forms. We used two experiments to investigate how collecting reviews using chatbots in place of forms might impact user experience and, in turn, alter the nature of the reviews submitted by users. Study 1 compares chatbots to web forms, finding that chatbots may improve perceived efficiency but otherwise have no discernible effect on satisfaction and usage intention. At the same time, chatbot usage results in shorter, lower-quality reviews. While structured templates enhance form-based reviews, introducing structure to chatbot interactions does not positively impact satisfaction and usage intent, potentially making the process less efficient. Nonetheless, a structured chatbot approach yields longer reviews and mitigates declines in quality. Adding structure to a chatbot takes the chat out of the chatbot, turning the interaction from casual conversation to a formal process, as demonstrated in Study 2. Hence, while this structured approach for chatbots improves review quality without harming satisfaction and usage intent, it may not be the most effective method for enhancing the reviewer experience. Our research shows that the chatbot and structure pulled users towards contradicting genre rules (the social structures that guide technology use) and triggered users to subconsciously enact distinctly different thought patterns as they composed reviews. [ABSTRACT FROM AUTHOR]

Algorithmic decision-making has the potential to radically reshape policy-making and policy implementation. Many of the moral examinations of AI in government take AI to be a neutral epistemic tool or the value-driven analogue of a policymaker. In this paper, I argue that AI systems in public administration are often better analogised to a street-level bureaucrat. Doing so opens up a host of questions about the moral dispositions of such AI systems. I argue that AI systems in public administration often act as indifferent bureaucrats, and that this can introduce a problematic homogeneity in the moral dispositions in administrative agencies. [ABSTRACT FROM AUTHOR]

The use of machine learning, or ‘artificial intelligence’ (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety. [ABSTRACT FROM AUTHOR]

This paper shows that humans perceive the value of paintings made by AI as lower than the value of those made by humans when the creator of the work is known. This phenomenon occurs regardless of which style (figurative or abstract) the evaluated paintings have. The negative impact of the information that AI is the author of a painting is mediated by the author's artistry (AA) and the overall impression (OI) the painting makes on participants. Moreover, it was found that people use context cues when assessing paintings made by humans and AI. The perceived value of a painting increases when the information about the value of the painting made by AI was provided as a context cue, indicating that people exclude works created by AI from the category of human-made art. The method used in the research is an experiment made with 296 respondents (42.6% female). The age of the participants ranged between 18 and 62 (M = 35.30, SD = 10.76). [ABSTRACT FROM AUTHOR]