We describe the rationale for an application ontology covering the domain of human body fluids that is designed to facilitate representation, reuse, sharing and integration of diagnostic, physiological, and biochemical data, We briefly review the Blood Ontology (BLO), Saliva Ontology (SALO) and Kidney and Urinary Pathway Ontology (KUPO) initiatives. We discuss the methods employed in each, and address the project of using them as starting point for a unified body fluids ontology resource. We conclude with a description of how the (...) body fluids ontology initiative may provide support to basic and translational science. (shrink)

This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large number (...) of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks. (shrink)

From 2010 to 2020, Indonesia’s tax-to-gross domestic product (GDP) ratio has been declining. A tax-to-GDP ratio trend of this magnitude indicates that the tax authority lacks the capacity to collect taxes. The tax administration system’s modernization utilizing information technology is thus deemed necessary. Artificial intelligence (AI) technology may serve as a solution to this issue. Using the theoretical frameworks of innovations in tax compliance, the cost of taxation, success factors for information technology governance (SFITG), and AI readiness, this study aims (...) to analyze the costs and benefits, the enablers and inhibitors, and the readiness of the government and related parties to apply AI to modernize the tax administration system in Indonesia. This study used qualitative approaches for the data’s collection and analysis. The data were obtained through a literature study and in-depth interviews. The findings show that AI application in the field of taxation can assist tax authorities in enforcing the law, provide taxpayers with convenience in fulfilling their tax obligations, improve justice for all taxpayers, and reduce tax compliance costs. The openness of Indonesia to technological developments, as evidenced by the AI National Strategy, is a supporting factor in the application of AI in Indonesia, particularly for the modernization of the tax administration system. The absence of specific regulations governing AI adoption, as well as a lack of human resources that can help the tax administration process, data, and infrastructure already support, are the impediments to implementing AI for the modernization of the tax administration system in Indonesia. (shrink)

The present comment concerns a recent AI & Ethics article which purports to report evidence of speciesist bias in various popular computer vision (CV) and natural language processing (NLP) machine learning models described in the literature. I examine the authors' analysis and show it, ironically, to be prejudicial, often being founded on poorly conceived assumptions and suffering from fallacious and insufficiently rigorous reasoning, its superficial appeal in large part relying on the sequacity of the article's target readership.

Artificial intelligence is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative (...) balancing approach of the Ethics Framework for Big Data in Health and Research. The analysis applies relevant values identified from the framework to demonstrate how decision-makers can draw on them to develop and implement AI-assisted support systems into healthcare and clinical practice ethically and responsibly. Please refer to Xafis et al. in this special issue of the Asian Bioethics Review for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end of this paper. (shrink)

This article focuses on having a perspective for the application of AI robots to moral education. Considering the features of the application of AI robots to education areas, we try to examine the necessity and arguments of AI robots in moral education. In that education robots in the classroom has already been produced and utilized like Lego Mindstorms, AI robots may also be applied to moral education. In the advance of robotics, many engineers and scientists have made AI robots communicating (...) and interacting with human beings. In that point, the arguments we have to examine are as follows: to make it desirable to utilize AI robots in moral education, first, the consideration on the limited boundary of AI robots in utilizing moral standards as educational instrument, and second, more attention is paid to emotion, which makes it very crucial to develop and build AI robots of moral education in future, while the general features of AI robots have developed and will advance the high technology of communicating and interacting with human beings based on emotion; in particular, the AI robot can be utilized in the education for the disabled or for autism. Therefore, by this paper, it is expected that for educational challenges of AI robots to moral education is to be investigated. (shrink)

There is much interest in moving AI out into real world applications, a move which has been encouraged by recent funding which has attempted to show industry and commerce can benefit from the Fifth Generation of computing. In this article I suggest that the legal application area is one which is very much more complex than it might — at first sight — seem. I use arguments from the sociology of law to indicate that the viewing of the legal (...) system as simply a rule-bound discipline is inherently nave. This, while not new in jurisprudence, is — as the literature of AI and law indicates — certainly novel to the field of artificial intelligence. The socio-legal argument provided is set within the context of AI as one more example of the failure of scientific success and method to easily transmit itself over into the social sciences. (shrink)

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of (...) artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (shrink)

A number of Artificial Intelligence (AI) ethics frameworks have been published in the last 6 years in response to the growing concerns posed by the adoption of AI in different sectors, including healthcare. While there is a strong culture of medical ethics in healthcare applications, AI-based Healthcare Applications (AIHA) are challenging the existing ethics and regulatory frameworks. This scoping review explores how ethics frameworks have been implemented in AIHA, how these implementations have been evaluated and whether they have (...) been successful. AI specific ethics frameworks in healthcare appear to have a limited adoption and they are mostly used in conjunction with other ethics frameworks. The operationalisation of ethics frameworks is a complex endeavour with challenges at different levels: ethics principles, design, technology, organisational, and regulatory. Strategies identified in this review are proactive, contextual, technological, checklist, organisational and/or evidence-based approaches. While interdisciplinary approaches show promises, how an ethics framework is implemented in an AI-based Healthcare Application is not widely reported, and there is a need for transparency for trustworthy AI. (shrink)

The ethics of artificial intelligence is an upcoming field of research that deals with the ethical assessment of emerging AI applications and addresses the new kinds of moral questions that the advent of AI raises. The argument presented in this article is that, even though there exist different approaches and subfields within the ethics of AI, the field resembles a critical theory. Just like a critical theory, the ethics of AI aims to diagnose as well as change society and (...) is fundamentally concerned with human emancipation and empowerment. This is shown through a power analysis that defines the most commonly addressed ethical principles and topics within the field of AI ethics as either to do with relational power or with dispositional power. Moreover, it is concluded that recognizing AI ethics as a critical theory and borrowing insights from the tradition of critical theory can help the field forward. (shrink)

This proposal introduces a novel decision-making framework to advance safe economic activities in cyberspace. We focus on identifying anomalies within crypto-asset trading, recognized as potential sources of criminal activity, severely undermining the credibility of such assets. Detecting and mitigating such anomalies holds significant societal implications, particularly in fostering trust within blockchain networks. We aim to bolster the “social trust” inherent to blockchain technology by facilitating informed economic activities in cyberspace. To achieve this, we propose integrating two artificial intelligence (AI) systems (...) into a blockchain-based decentralized autonomous organization (DAO). The first AI application involves amalgamating various anomaly indicators, spanning from cluster coefficient, entropy, triangular motif analysis, correlation tensor analysis, loop component by Hodge decomposition, loop causality detection, network classification using graph Laplacian, and persistent homology analysis, into a comprehensive indicator using a Boltzmann machine. The second AI application entails deploying conversational AI to guide and support traders, aiding them in making informed trading decisions. This system is designed to alert DAO members to anomalies based on the integrated indicators, especially during massive price fluctuations. We operate under the assumption of close collaboration between governments, experts, traders, system developers, and operators to effectively organize DAOs. The primary technical challenge in our proposal lies in developing a wallet assisted by an intelligent software agent capable of safe interactions with traders within a unified DAO. With this organization, we envision fostering a global economic ecosystem where physical and cyber worlds converge, allowing democratic economic participation. (shrink)

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that (...) AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft. (shrink)

One of the sectors for which Artificial Intelligence applications have been considered as exceptionally promising is the healthcare sector. As a public-facing sector, the introduction of AI applications has been subject to extended news coverage. This article conducts a quantitative and qualitative data analysis of English news media articles covering AI systems that allow the automation of tasks that so far needed to be done by a medical expert such as a doctor or a nurse thereby redistributing their (...) agency. We investigated in this article one particular framing of AI systems and their agency: the framing that positions AI systems as replacing and outperforming the human medical expert, and in which AI systems are personified and/or addressed as a person. The analysis of our data set consisting of 365 articles written between the years 1980 and 2019 will show that there is a tendency to present AI systems as outperforming human expertise. These findings are important given the central role of news coverage in explaining AI and given the fact that the popular frame of ‘outperforming’ might place AI systems above critique and concern including the Hippocratic oath. Our data also showed that the addressing of an AI system as a person is a trend that has been advanced only recently and is a new development in the public discourse about AI. (shrink)

Background: Artificial intelligence (AI) is transforming healthcare systems by improving operational efficiency, simplifying patient care procedures, and improving diagnostic accuracy. Artificial intelligence (AI) technologies, like machine learning and natural language processing, present previously unheard-of chances to quickly and accurately evaluate enormous volumes of healthcare data, assisting with clinical decision-making and enhancing patient outcomes. Aim thorough examination and analysis of artificial intelligence's impact on healthcare leadership, with a particular emphasis on present nursing trends and their implications for the future. The study (...) tries to uncover the advantages, difficulties, and consequences of AI integration by looking at how AI technologies including clinical decision support systems, predictive analytics, robots, natural language processing, and telehealth are being used in nursing practice. Method: A comprehensive analysis including research articles published between 2015 and 2024 was carried out. To give a thorough overview of AI's present and future uses in healthcare, major themes and trends were found and summarized. Results: By stressing AI's role in improving diagnostic accuracy and patient outcomes, the study highlights the technology's major contributions to drug discovery, virtual patient care, and medical imaging. Human-centered design concerns, the necessity of educational changes, and ethical challenges surrounding the application of AI surfaced as crucial topics needing attention. Conclusion: AI has enormous potential to transform healthcare by enhancing operational effectiveness, optimizing the delivery of care, and increasing diagnostic precision. Still, ethical issues must be resolved, interdisciplinary cooperation must be promoted, and educational frameworks must be improved in order to provide healthcare workers with the necessary AI skills. (shrink)

The growing demand for energy in urban environments, coupled with the urgent need to reduce carbon emissions, necessitates innovative approaches to power generation, distribution, and consumption. Artificial Intelligence (AI)-driven smart grids offer a transformative solution by optimizing energy efficiency, integrating renewable resources, and ensuring grid stability. This paper explores how machine learning and IoT-enabled predictive analytics can enhance smart grid performance in urban areas. By addressing challenges such as demand forecasting, load balancing, and renewable energy intermittency, this study demonstrates the (...) potential of AI to revolutionize sustainable energy management. Experimental results highlight improvements in grid reliability, cost reduction, and carbon footprint minimization, paving the way for resilient and eco-friendly urban energy systems. (shrink)

While early optimists have seen online discussions as potential spaces for deliberation, the reality of many online spaces is characterized by incivility and irrationality. Increasingly, AI tools are considered as a solution to foster deliberative discourse. Against the backdrop of previous research, we show that AI tools for online discussions heavily focus on the deliberative norms of rationality and civility. In the operationalization of those norms for AI tools, the complex deliberative dimensions are simplified, and the focus lies on the (...) detection of argumentative structures in argument mining or verbal markers of supposedly uncivil comments. If the fairness of such tools is considered, the focus lies on data bias and an input–output frame of the problem. We argue that looking beyond bias and analyzing such applications through a sociotechnical frame reveals how they interact with social hierarchies and inequalities, reproducing patterns of exclusion. The current focus on verbal markers of incivility and argument mining risks excluding minority voices and privileges those who have more access to education. Finally, we present a normative argument why examining AI tools for online discourses through a sociotechnical frame is ethically preferable, as ignoring the predicable negative effects we describe would present a form of objectionable indifference. (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)

There is now a robust literature on the ethics of artificial intelligence (AI) that pertains largely to non-military issues – issues of, among other things, bias, fairness, and unintended consequences. There is less published work, however, on how these lessons from industry and academia might inform the ethics of AI in the military context. In this article, I take small steps to demonstrate the ways in which the field of AI ethics might be relevant to military applications. Ultimately, I (...) argue that even though the outcomes of AI failures might look very different in the non-military context versus in the military context, the causes of those failures can be very similar; and addressing those potential causes of AI failures in the development of military AI systems will result in fewer ethical failures after deployment. (shrink)

Workplace AI is transforming labor but decisions on which AI applications are developed or implemented are made with little to no input from workers themselves. In this piece for AI & Society, I argue for nationalization as a strategy for democratizing AI.

Companies increasingly deploy artificial intelligence technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the novelty (...) of AI applications in recruiting practice, the subject is still an emerging topic in academic literature. To inform and strengthen the foundation for future research, this paper systematically reviews the extant literature on the ethicality of AI-enabled recruiting to date. We identify 51 articles dealing with the topic, which we synthesize by mapping the ethical opportunities, risks, and ambiguities, as well as the proposed ways to mitigate ethical risks in practice. Based on this review, we identify gaps in the extant literature and point out moral questions that call for deeper exploration in future research. (shrink)

To analyze potential negative consequences of adopting generative AI solutions in the digital afterlife industry (DAI), in this paper we present three speculative design scenarios for AI-enabled simulation of the deceased. We highlight the perspectives of the data donor, data recipient, and service interactant – terms we employ to denote those whose data is used to create ‘deadbots,’ those in possession of the donor’s data after their death, and those who are meant to interact with the end product. We draw (...) on the scenarios to map out several key ethical concerns posed by ‘re-creation services’ and to put forward recommendations on the ethical development of AI systems in this specific area of application. The recommendations, targeted at providers of AI-enabled re-creation services, include suggestions for developing sensitive procedures for retiring deadbots, ensuring meaningful transparency, restricting access to such services to adult users only, and adhering to the principle of mutual consent of both data donors and service interactants. While we suggest practical solutions to the socio-ethical challenges posed by the emergence of re-creation services, we also emphasize the importance of ongoing interdisciplinary research at the intersection of the ethics of AI and the ethics of the DAI. (shrink)

This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial intelligence is viewed as amongst the technological advances that will reshape modern societies and their relations. While the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories (...) use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us, ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all. (shrink)

Real engines of the artificial intelligence revolution, machine learning models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In (...) this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs. (shrink)

In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral dream — that of constructing an intelligent machine-and from its detractors, who described it as the latest fad worthy of quacks. Yet AI is still alive, well and blossoming, and has left a legacy of tools and applications almost unequalled by any other field-probably because, as the heir of Renaissance thought, it represents (...) a possible bridge between the humanities and the natural sciences, philosophy and neurophysiology, psychology and integrated circuits-including systems that today are taken for granted, such as the computer interface with mouse pointer and windows. This writing describes a few results of AI that have modified the scientific world, as well as the way a layman sees computers: thetechnology of programming languages, such asLISP-witness the unique excellence of academic departments that have contributed to them-thecomputing workstations-of which our modern PC is but a vulgarised descendant-theapplications to the educational field-e.g., the realisation of some ideas of genetic epistemology-and tointerdisciplinary philosophy-such as Hofstadter's associations between the arts and mathematics-and the use ofAI techniques in music and musicology. All this has led to a generalisation of AI towards Negrotti's overallTheory of the Artificial, which encompasses further specialisation such asartificial reality, artificial life, and applications ofneural networks among others. (shrink)

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)

By the early 2020s, emotional artificial intelligence will become increasingly present in everyday objects and practices such as assistants, cars, games, mobile phones, wearables, toys, marketing, insurance, policing, education and border controls. There is also keen interest in using these technologies to regulate and optimize the emotional experiences of spaces, such as workplaces, hospitals, prisons, classrooms, travel infrastructures, restaurants, retail and chain stores. Developers frequently claim that their applications do not identify people. Taking the claim at face value, this (...) paper asks, what are the privacy implications of emotional AI practices that do not identify individuals? To investigate privacy perspectives on soft non-identifying emotional AI, the paper draws upon the following: over 100 interviews with the emotion detection industry, legal community, policy-makers, regulators and NGOs interested in privacy; a workshop with stakeholders to design ethical codes for using data about emotions; a UK survey of 2068 citizens on feelings about emotion capture technologies. It finds a weak consensus among social stakeholders on the need for privacy, this driven by different interests and motivations. Given this weak consensus, it concludes that there exists a limited window of opportunity to societally agree principles of practice regarding privacy and the use of data about emotions. (shrink)

My aim in this article is to set out some counter-intuitive claims about the challenges posed by artificial intelligence (AI) applications to the protection and enjoyment of human rights and to be your guide through my unorthodox ideas. While there are familiar human rights issues raised by AI and its applications, these are perhaps the easiest of the challenges because they are already recognized by the human rights regime as problems. Instead, the more pernicious challenges are those that (...) have yet to be identified or articulated, because they arise from new affordances rather than directly through AI modeled as a technology. I suggest that we need to actively explore the potential problem space on this basis. I suggest that we need to adopt models and metaphors that systematically exclude the possibility of applying the human rights regime to AI applications. This orientation will present us with the difficult, intractable problems that most urgently require responses. There are convincing ways of understanding AI that lock out the very possibility for human rights responses and this should be grounds for serious concern. I suggest that responses need to exploit both sets of insights I present in this paper: first that proactive and systematic searches of the potential problem space need to be continuously conducted to find the problems that require responses; and second that the monopoly that the human rights regime holds with regards to addressing harm and suffering needs to be broken so that we can deploy a greater range of barriers against failures to recognize and remedy AI-induced wrongs. (shrink)

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address (...) this, we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

With Artificial Intelligence entering our lives in novel ways—both known and unknown to us—there is both the enhancement of existing ethical issues associated with AI as well as the rise of new ethical issues. There is much focus on opening up the ‘black box’ of modern machine-learning algorithms to understand the reasoning behind their decisions—especially morally salient decisions. However, some applications of AI which are no doubt beneficial to society rely upon these black boxes. Rather than requiring algorithms to (...) be transparent we should focus on constraining AI and those machines powered by AI within microenvironments—both physical and virtual—which allow these machines to realize their function whilst preventing harm to humans. In the field of robotics this is called ‘envelopment’. However, to put an ‘envelope’ around AI-powered machines we need to know some basic things about them which we are often in the dark about. The properties we need to know are the: training data, inputs, functions, outputs, and boundaries. This knowledge is a necessary first step towards the envelopment of AI-powered machines. It is only with this knowledge that we can responsibly regulate, use, and live in a world populated by these machines. (shrink)

AI systems, which receive vast amounts of information including privacy information, are emerging. Protecting the privacy of the general public is an important issue for democracies. In this study, “Public space watched by AI- connected cameras” is taken as an example of an AI-system that is expected to be used for public purposes and has a relatively high privacy violation risk. It is defined as a wide public area where every point is monitored by multiple AI-connected street cameras. The following (...) four socially acceptable applications of “Public space watched by AI- connected cameras” are discussed: (1) Human/vehicle tracking, (2) Watching over children, (3) Detection of abnormal situations, (4) Criminal and terrorist behavior pattern analysis. As for privacy protection measures for AI systems, in addition to reliable protection itself, reliable recording and auditing to deter misuse and abuse is considered a prerequisite to ensure that the general public can feel secure. In this paper, the concept of “Verifiable record of AI output”, which is a verifiable and tamper-proof record and disclosure of AI output is proposed. “Verifiable record of AI output” is based on the idea that it may be more realistic to apply a simple exit control only for AI output to confirm rule conformance, rather than anonymizing huge AI inputs such as Big Data or controlling internal AI processes that are black-boxed. A concrete example of implementing “Verifiable record of AI output” in “Public space watched by AI-connected cameras” is proposed. In the “Verifiable record of AI output”, the AI system is enclosed in an outbound firewall, and only AI outputs that are confirmed to be in line with “the purpose of use approved in advance through social discussion” are allowed to be transmitted. The process from application to permission of transmission is anonymized and disclosed in a verifiable and tamper-proof manner. This public record can be verified by the general public or an auditing organization to ensure that any misuse or abuse of the AI system will be exposed. The “Verifiable record of AI output” proposed in this paper is characterized by reliable measurement and recording of AI output, and its disclosure for auditing purposes. The possibility of using this as a template to apply to other AI systems that receive Big Data input, including the privacy of the general public, is considered. (shrink)

The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA with a framework developed by (...) the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This approach enables a nuanced analysis of AI risk by exploring the interplay between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We further refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment. Finally, we present three uses of this approach under the AIA: to implement the Regulation, to assess the significance of risks, and to develop internal risk management systems for AI deployers. (shrink)

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making (...) around cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts. (shrink)

Using an interdisciplinary approach, this book explores the emerging topics and rapid technological developments of robotics and artificial intelligence through the lens of the evolving role of sex robots, and how they should best be designed to serve human needs. An international panel of authors provides the most up-to-date, evidence-based empirical research on the potential sexual applications of artificial intelligence. Early chapters discuss the objections to sexual activity with robots while also providing a counterargument to each objection. Subsequent chapters (...) present the implications of robot sex as well as the security and data privacy issues associated with sexual interactions with artificial intelligence. The book concludes with a chapter highlighting the importance of a scientific, multidisciplinary approach to the study of human - robot sexuality. Topics featured in this book include: The Sexual Interaction Illusion Model. The personal companion system, Harmony, designed by RealbotixTM. An exposition of the challenges of personal data control and protection when dealing with artificial intelligence. The current and future technological possibilities of projecting three-dimensional holograms. Expert discussion notes from an international workshop on the topic. AI Love You will be of interest to academic researchers in psychology, robotics, ethics, medical science, sociology, gender studies as well as clinicians, policy makers, and the business sector. (shrink)

This paper reviews the history of AI & Law research from the perspective of argument schemes. It starts with the observation that logic, although very well applicable to legal reasoning when there is uncertainty, vagueness and disagreement, is too abstract to give a fully satisfactory classification of legal argument types. It therefore needs to be supplemented with an argument-scheme approach, which classifies arguments not according to their logical form but according to their content, in particular, according to the roles that (...) the various elements of an argument can play. This approach is then applied to legal reasoning, to identify some of the main legal argument schemes. It is also argued that much AI & Law research in fact employs the argument-scheme approach, although it usually is not presented as such. Finally, it is argued that the argument-scheme approach and the way it has been employed in AI & Law respects some of the main lessons to be learnt from Toulmin’s The Uses of Argument. (shrink)

Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, (...) I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. (shrink)

The applications of Artificial Intelligence lie all around us; in our homes, schools and offices, in our cinemas, in art galleries and - not least - on the Internet. The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle.As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human (...) beings. Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative or even conscious, and shows how the pursuit of Artificial Intelligence has helped us to appreciate how human and animal minds are possible. (shrink)

The focus of this chapter is on some of the ethical and philosophical issues at the intersection of robotics and artificial intelligence (AI) applications in the health care sector and medical assistance in dying (e.g. physician-assisted suicide and euthanasia), including: (1) Is there a role for robotic systems/AI to play in the orchestration or delivery of assisted dying?; (2) Can the use of robotic systems/AI make the orchestration of assisted dying more ethical?; and (3) What insights can be generated (...) in the ethical debate on physician assisted suicide and euthanasia from considering the prospect of robotic systems/AI assisting with the provision of or providing assistance in dying? The prospect of including robotic systems/AI in the context of assisted dying provides opportunity to revisit longstanding philosophical and ethical issues under new light. Indeed, reflecting on these questions may invigorate debate, for example in reconsidering the de-medicalization of assisted dying, reconsidering whether assisted dying is within the proper scope of medicine, and reconsidering which normative approach to the ethics of assisted dying is the most appropriate. (shrink)

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social (...) effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence. (shrink)

This paper examines a case where Amazon’s cloud-based AI assistant Alexa accidentally ordered a dollhouse for a 6-year-old girl. In the press, the case was defined as a technical recognition problem. Building on this idea, we argue that the dollhouse case helps us to analyze the limits of current AI applications. By drawing on the writings of Gilles Deleuze and François Laruelle, we argue that these limits are not merely technical but more deeply embedded in the structures where the (...) thinking of AI can potentially happen. We point out that AI research has been compromised by the concepts of what constitutes both ‘artificial’ and by what constitutes ‘intelligence’. First, we use the notion of artificial non-intelligence to explain how different modes of digital capitalism such as voice commerce establish limits for AI. Second, we use the notion of non-artificial intelligence to illustrate the limits of associating AI’s modes of thinking with human thought. (shrink)

This study investigates audience perceptions of AI-generated news across ten African countries, focusing on trust, bias, and transparency. Using a non-probability cross-sectional online survey, data were collected from 1960 participants between May and July 2024. The sample encompassed diverse demographics, leveraging social media for broad reach. The study revealed that trust in AI-generated news is generally neutral, with significant variations influenced by demographic factors, particularly age. A moderate positive correlation between perceived bias and trust suggests that awareness of potential biases (...) does not necessarily erode trust. Younger participants showed greater receptiveness to AI-generated content, especially when transparency and readability were prioritized. The findings underscore the need to address bias, transparency, and digital literacy to build trust in AI-generated news, particularly among older audiences. The study advances theoretical frameworks, including uses and gratifications, digital divide, and cultivation theory, while highlighting the importance of digital literacy in shaping media consumption patterns. Practical implications emphasize the need for culturally relevant AI applications in journalism and digital literacy programs to bridge the trust gap in AI-driven news across Africa. These insights are crucial for news organizations, policymakers, and educators aiming to understand the interaction between AI and Journalism in Africa. (shrink)

This study aims to explore the level of using artificial intelligence applications among university students and their impact on the credibility of e-learning. The study used the descriptive method because it is suitable for the study. The study included a sample of 94 Jordanian university students, who were selected by stratified random method. The study used a questionnaire of 40 items as the research instrument to determine the level of the use of artificial intelligence technology and distance education in (...) distorting the contemporary educational landscape. The results showed that universities try to limit the incorrect use of AI applications among students by using specific detection tools. The students showed that their universities have strict rules and regulations regarding the use of AI tools in their studies. Therefore, there were low estimations from the students on the large incorrect usage of AI tools. The results also revealed that gender and the specialization of the student do not affect their views toward the incorrect use of AI tools in their education, whether face-to-face education or online education. (shrink)

The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact (...) can give testimony. In section 3, I identify three assumptions underlying the view that rejects conversational AIs – AI-based technologies that converse, as testifiers: conversational AIs (1) lack intentions, (2) cannot be normatively assessed, and (3) cannot constitute an object in trust relations, while humans can. In section 4, I propose the concept ‘AI-testimony’ for analyzing outputs of conversational AIs, suggesting three conditions for technologies to deliver AI-testimony: (1) content is propositional, (2) generated and delivered with no other human directly involved, (3) the output is perceived as phenomenologically similar to that of a human. I conclude that this concept overcomes the limitations of the anthropocentric concept of testimony, opening future directions of research without associating conversational AIs with human-like agency. (shrink)

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

With the emergence of big data, cloud computing, and other technologies, artificial intelligence technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It (...) also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”. (shrink)

As AI technologies increase in capability and ubiquity, AI accidents are becoming more common. Based on normal accident theory, high reliability theory, and open systems theory, we create a framework for understanding the risks associated with AI applications. This framework is designed to direct attention to pertinent system properties without requiring unwieldy amounts of accuracy. In addition, we also use AI safety principles to quantify the unique risks of increased intelligence and human-like qualities in AI. Together, these two fields (...) give a more complete picture of the risks of contemporary AI. By focusing on system properties near accidents instead of seeking a root cause of accidents, we identify where attention should be paid to safety for current generation AI systems. (shrink)

In this paper, we provide an analysis of the concept of legal personality and discuss whether personality may be conferred on artificial intelligence systems (AIs). Legal personality will be presented as a doctrinal category that holds together bundles of rights and obligations; as a result, we first frame it as a node of inferential links between factual preconditions and legal effects. However, this inferentialist reading does not account for the ‘background reasons’ of legal personality, i.e., it does not explain why (...) we cluster different situations under this doctrinal category and how extra-legal information is integrated into it. We argue that one way to account for this background is to adopt a neoinstitutional perspective and to update the ontology of legal concepts with a further layer, the meta-institutional one. We finally argue that meta-institutional concepts can also support us in finding an equilibrium around the legal-policy choices that are involved in including (or not including) AIs among legal persons. (shrink)

The current “AI Summer” is marked by scientific breakthroughs and economic successes in the fields of research, development, and application of systems with artificial intelligence. But, aside from the great hopes and promises associated with artificial intelligence, there are a number of challenges, shortcomings and even limitations of the technology. For one, these challenges arise from methodological and epistemological misconceptions about the capabilities of artificial intelligence. Secondly, they result from restrictions of the social context in which the development of (...) class='Hi'>applications of machine learning is embedded. And third, they are a consequence of current technical limitations in the development and use of artificial intelligence. The paper intends to provide an overview of current challenges which the research and development of applications in the field of artificial intelligence and machine learning have to face, whereas all three mentioned areas are to be further explored in this paper. (shrink)