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

Artificial Intelligence technology presents a multitude of ethical concerns, many of which are being actively considered by organizations ranging from small groups in civil society to large corporations and governments. However, it also presents ethical concerns which are not being actively considered. This paper presents a broad overview of twelve topics in ethics in AI, including function, transparency, evil use, good use, bias, unemployment, socio-economic inequality, moral automation and human de-skilling, robot consciousness and rights, dependency, social-psychological effects, and spiritual (...) effects. Each of these topics will be given a brief discussion, though each deserves much deeper consideration. (shrink)

AI-driven decisions can draw data from virtually any area of your life to make a decision about virtually any other area of your life. That creates fairness issues. Effective regulation to ensure fairness requires that AI systems be transparent. That is, regulators must have sufficient access to the factors that explain and justify the decisions. One approach to transparency is to require that systems be explainable, as that concept is understood in computer science. A system is explainable if one (...) can provide a human-understandable explanation of why it makes any particular prediction. Explainability should not be equated with transparency. To address transparency and characterize its relation to explainability, we define transparency for a regulatory purpose. A system is transparent for a regulatory purpose (r-transparent) when and only when regulators have an explanation, adequate for that purpose, of why it yields the predictions it does. Explainability remains relevant to transparency but turns out to be neither necessary nor sufficient for it. The concepts of explainability and r-transparency combine to yield four possibilities: explainable and either r-transparent or not; and not explainable and either not r-transparent or r-transparent. Combining r-transparency with ideas from the Harvard computer scientist Cynthia Dwork, we propose four requirements on AI systems. (shrink)

A recent article by Herzog provides a much-needed integration of ethical and epistemological arguments in favor of explicable AI in medicine. In this short piece, I suggest a way in which its epistemological intuition of XAI as “explanatory interface” can be further developed to delineate the relation between AI tools and scientific research.

With the widespread use of artificial intelligence, it becomes crucial to provide information about these systems and how they are used. Governments aim to disclose their use of algorithms to establish legitimacy and the EU AI Act mandates forms of transparency for all high-risk and limited-risk systems. Yet, what should the standards for transparency be? What information is needed to show to a wide public that a certain system can be used legitimately and responsibly? I argue that process-based approaches (...) fail to satisfy, as knowledge about the development process is insufficient to predict the properties of the resulting system. Current outcome-based approaches [Mitchell et al., 2019; Loi et al., 2021] are also criticized for a lack of attention to the broader socio-technical system and failure to account for empirical results that show that people care about more than just the outcomes of a process [as reported by Meyerson et al. (Procedural justice and relational theory: Empirical, philosophical, and legal perspectives, Taylor & Francis, 2021)]. Instead, I propose value-based transparency, on which the information we need to provide is what values have been considered in the design and how successful these have been realized in the final system. This can handle the objections to other frameworks, matches with current best practices on the design of responsible AI and provides the public with information on the crucial aspects of a system’s design. (shrink)

The paper analysis the strategies and concepts developed in the world in modern directions: innova- tive economy, digital economy, artificial intelligence, Industry 4.0 and others. The problem is to determine the initial fundamental parameters of order and their prospects in the global world, the definition and principles of artificial intel- ligence systems, its structure and important aspects and principles of future science and technology in analysis and synthesis based on synergetic approaches, innovative, information, converged technologies, taking into account (...) the design of future and viable and safe and sustainable development in the context of Industry 4.0, digital economy and Society 5.0. The general scheme of the integrated model of sustainable and socio-humanitarian development in the global system is proposed, which the authors call the noosphere model of sustainable and safe development as an important fundamental platform for the design and creation of AIS. In the XXI century education, science and innova- tive technologies becomes the «basis» of the spiritual and material reproduction of man, transforming the society of the XXI century into an educational society, ensures compliance with the law of advanced development of human quality, quality of social intelligence and quality of educational systems in society. noosphere evolution. Industry 4.0 is the next stage in the digitalization of production technologies with the accelerated introduction of new developments and cyberphysical systems, which is changing business models. Accordingly, Industry 4.0 as a separate segment of digital manufacturing technologies takes its place among other digital sectors. The analysis of the Strategies and Concepts developed in the world in modern directions: innovative economy, digital economy, artificial intelli- gence, Industry 4.0 and others is carried out in the work. The problem is to determine the initial fundamental para- meters of order and their prospects in the global world, the definition and principles of artificial intelligence systems, its structure and important aspects and principles of future science and technology in analysis and synthesis ba- sed on synergetic approaches, innovative, information, converged technologies, taking into account the design of future and viable and safe and sustainable development in the context of Industry 4.0, DE and Society 5.0. The main scientific and technological factors in the 21st century will be the development of: Artificial Intelligence, Nano Technologies, Bio-Media Technologies, Cognitive Technologies and Socio-Humanitarian Technologies. More specifically – this is the modern development of Innovative Technologies: «NBIC SH». Therefore, it is necessary to note the importance of taking into account the principle of I2K2+SH, ie: Intellectualization, Integration, Convergence, Coevolution and Socio- humanitarian technologies. It is important and necessary to take into account modern principles of designing a su- stainable and secure AIS and solving the problem of Harmonization between 2 worlds: Real and Virtual, especially in their hybridization. Our immediate future is a hybrid nonlinear world. Note that digitalization, artificial intelligence and robotics will be the main drivers of global economic growth by 2030E. An important paradigm and platform is the Integrated System: for Industry 4.0, DE and AI, taking into account the complex principle of I2K2+SH. If the transition of the Ukrainian economy to an innovative one does not take place in the next 3-5 years, Ukraine will remain in the backyard of civilization. (shrink)

Purpose The purpose of this study is to examine public perceptions and attitudes toward using artificial intelligence (AI) in the US criminal justice system. Design/methodology/approach The authors took a quantitative approach and administered an online survey using the Amazon Mechanical Turk platform. The instrument was developed by integrating prior literature to create multiple scales for measuring public perceptions and attitudes. Findings The findings suggest that despite the various attempts, there are still significant perceptions of sociodemographic bias in (...) the criminal justice system and technology alone cannot alleviate them. However, AI can assist judges in making fairer and more objective decisions by using triangulation – offering additional data points to offset individual biases. Social implications Other scholars can build upon the findings and extend the work to shed more light on some problems of growing concern for society – bias and inequality in criminal sentencing. AI can be a valuable tool to assist judges in the decision-making process by offering diverse viewpoints. Furthermore, the authors bridge the gap between the fields of technology and criminal justice and demonstrate how the two can be successfully integrated for the benefit of society. Originality/value To the best of the authors’ knowledge, this is among the first studies to examine a complex societal problem like the introduction of technology in a high-stakes environment – the US criminal justice system. Understanding how AI is perceived by society is necessary to develop more transparent and unbiased algorithms for assisting judges in making fair and equitable sentencing decisions. In addition, the authors developed and validated a new scale that can be used to further examine this novel approach to criminal sentencing in the future. (shrink)

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

Value Sensitive Design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. (...) Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: 1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; 2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good; and 3) extending the VSD process to encompass the whole life cycle of an AI technology in order to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app. (shrink)

Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls (...) with respect to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking. (shrink)

Artificially intelligent (AI) technologies are increasingly being used in many workplaces. It is recognised that there are ethical dimensions to the ways in which organisations implement AI alongside, or substituting for, their human workforces. How will these technologically driven disruptions impact the employee–employer exchange? We provide one way to explore this question by drawing on scholarship linking Integrative Social Contracts Theory (ISCT) to the psychological contract (PC). Using ISCT, we show that the macrosocial contract’s ethical AI norms of beneficence, non-maleficence, (...) autonomy, justice, and explicability can feed into national- and organisational-level microsocial contracts. We also outline the role of employees’ technology frames in this process. We then use an illustrative example to demonstrate how this multilevel normative background helps to inform the content of individuals’ PCs in the context of working with AI technologies. (shrink)

As the use of artificial intelligence (AI) technologies, particularly generative AI (Gen AI), becomes increasingly prevalent in nursing education, it is paramount to address the ethical implications of their implementation. This article explores the realm of cyberethics (a field of applied ethics that focuses on the ethical, legal, and social implications of cybertechnology), highlighting the ethical principles of autonomy, nonmaleficence, beneficence, justice, and explicability as a roadmap for facilitating AI integration into nursing education. Research findings suggest that ethical dilemmas (...) that challenge these five principles can emerge within the context of nursing education; however, adherence to these very principles, which is essential to improving patient care, can offer solutions to these dilemmas. To ensure the ethical and responsible use of Gen AI in nursing education, these principles must be woven into the fabric of curricula, and appropriate guidelines must be developed. Nurse educators have a pivotal role in strategizing comprehensive approaches for ethical AI integration, establishing clear guidelines, and instilling critical thinking among students. Fostering lifelong learning and adaptability is key to ensuring that future nurses can successfully navigate the constantly evolving landscape of health care technology. Future research should investigate the long-term impacts of AI utilization on learning outcomes and ethical decision-making. (shrink)

The difficulty of explaining the outputs of artificial intelligence (AI) models and what has led to them is a notorious ethical problem wherever these technologies are applied, including in the medical domain, and one that has no obvious solution. This paper examines the proposal, made by Luciano Floridi and colleagues, to include a new ‘principle of explicability’ alongside the traditional four principles of bioethics that make up the theory of ‘principlism’. It specifically responds to a recent set of criticisms (...) that challenge the supposed need for such a principle to perform an enabling role in relation to the traditional four principles and therefore suggest that these four are sufficient without the addition of explicability. The paper challenges the critics’ premise that explicability cannot be an ethical principle like the classic four because it is explicitly subordinate to them. It argues instead that principlism in its original formulation locates the justification for ethical principles in a midlevel position such that they mediate between the most general moral norms and the contextual requirements of medicine. This conception of an ethical principle then provides a mold for an approach to explicability on which it functions as an enabling principle that unifies technical/epistemic demands on AI and the requirements of high-level ethical theories. The paper finishes by anticipating an objection that decision-making by clinicians and AI fall equally, but implausibly, under the principle of explicability’s scope, which it rejects on the grounds that human decisions, unlike AI’s, can be explained by their social environments. (shrink)

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general (...) ethical frameworks to clinical decision-making entails conceptual questions: Is explicability a free-standing principle? Is it already covered by the well-established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual-ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to? (shrink)

In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making environments. With the rise of artificial intelligence and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds (...) different promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant contextual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought. (shrink)

This paper explores artificial intelligence (AI) ethics from an Islamic perspective at a critical time for AI ethical norm-setting. It advocates for a pluralist approach to ethical AI benchmarking. As rapid advancements in AI technologies pose challenges surrounding autonomy, privacy, fairness, and transparency, the prevailing ethical discourse has been predominantly Western or Eurocentric. To address this imbalance, this paper delves into the Islamic ethical traditions to develop a framework that contributes to the global debate on optimal norm setting (...) for designing and using AI technologies.The paper outlines Islamic parameters for ethical values and moral actions in the context of AI's ethical uncertainties. It emphasizes the significance of both textual and non-textual Islamic sources in addressing these uncertainties while placing a strong emphasis on the notion of "good" or "maṣlaḥa" as a normative guide for AI's ethical evaluation. Defining maṣlaḥa as an ethical state of affairs in harmony with divine will, the paper highlights the coexistence of two interpretations of maṣlaḥa: welfarist/utility-based and duty-based. Islamic jurisprudence allows for arguments supporting ethical choices that prioritize building the technical infrastructure for AI to maximize utility. Conversely, it also supports choices that reject consequential utility calculations as the sole measure of value in determining ethical responses to AI advancements. (shrink)

Artificially intelligent (AI) technologies are increasingly being used in many workplaces. It is recognised that there are ethical dimensions to the ways in which organisations implement AI alongside, or substituting for, their human workforces. How will these technologically driven disruptions impact the employee–employer exchange? We provide one way to explore this question by drawing on scholarship linking Integrative Social Contracts Theory (ISCT) to the psychological contract (PC). Using ISCT, we show that the macrosocial contract’s ethical AI norms of beneficence, non-maleficence, (...) autonomy, justice, and explicability can feed into national- and organisational-level microsocial contracts. We also outline the role of employees’ technology frames in this process. We then use an illustrative example to demonstrate how this multilevel normative background helps to inform the content of individuals’ PCs in the context of working with AI technologies. (shrink)

The paper explores integrating AI into scholarly communication for enhanced human cognitive abilities. The conception of human-machine communication (HMC) approach that regards AI-based technologies not as interactive objects, but communicative subjects, throws issues that are more philosophical in scholarly communication. It is a known fact that, there is increased interaction between humans and machines especially consolidated by COVID-19 pandemic, which heightened the development of Individual Adaptive Learning System thereby necessarily requiring inputs from NI to strengthen AI. This positioned university (...) at the juxtaposition for ensuring the production of highly talented individuals who can continue to think beyond the thinkable intelligently and enabling AI experts to carry on building algorithms that can further the development of technology to the next level. To perform such a task, research output is paramount in connecting university and publishing industry. The paper critically examines important components and tries to do justice for emanating of critical questions from researchers (NI) thus feeding the development of technology (AI). (shrink)

This paper navigates the relationship between the growing influence of Artificial Intelligence (AI) and the foundational principles of academic integrity. It offers an in-depth analysis of how key academic stakeholders—publishers and universities—are crafting strategies and guidelines to integrate AI into the sphere of scholarly work. These efforts are not merely reactionary but are part of a broader initiative to harness AI’s potential while maintaining ethical standards. The exploration reveals a diverse array of stances, reflecting the varied applications of AI (...) in academic settings. These range from conservative approaches emphasizing the need for stringent AI regulations to more progressive strategies that seek to leverage AI’s capabilities. The paper accentuates the importance of guidelines that are adaptable, transparent, and culturally sensitive, acknowledging the global context of academic research. It also highlights the pressing need for effective implementation and enforcement of these guidelines to ensure that the integration of AI into academia does not compromise the integrity of scholarly work. The conclusion emphasizes the dynamic balance required between embracing AI’s transformative potential and upholding the timeless values of academic rigor and ethical research. This comprehensive analysis aims to provide a detailed perspective on the evolving role of AI in academia, fostering a dialog on how best to navigate this new technological frontier while preserving the sanctity of academic scholarship. (shrink)

Monitoring and assistive technologies (MATs) are being used more frequently in healthcare. A central ethical concern is the compatibility of these systems with the moral preferences of their users—an issue especially relevant to participatory approaches within the ethics-by-design debate. However, users’ incapacity to communicate preferences or to participate in design processes, e.g., due to dementia, presents a hurdle for participatory ethics-by-design approaches. In this paper, we explore the question of how the value preferences of users in the field of dementia (...) care can be integrated into AI-based MATs. First, we briefly introduce different ethics-by-design approaches and explain the challenges they face in dementia care. Next, we introduce a novel approach for addressing these challenges. Through a qualitative content analysis of interviews with persons with dementia and their family caregivers (n = 27), we identified multiple ideal–typical value preference profiles. We then applied these profiles in a computer simulation, by which we also introduce the concept of ethical compliance quantification to scale the moral preferences of the interviewees for the simulation. Finally, we discuss study results, the advantages of using computer simulations, and general study limitations before drawing conclusions for further research. The study contributes to the ongoing debate on participatory ethics-by-design by defining on the basis of empirical data ideal–typical value preference frameworks that can be used to guide MAT actions and their outcomes. Going forward, MAT end-users with dementia and other communication-impaired persons could be enabled to choose a value profile that best fits their moral preferences. (shrink)

National Artificial Intelligence (AI) strategies articulate imaginaries of the integration of AI into society and envision the governing of AI research, development and applications accordingly. To integrate these central aspects of national AI strategies under one coherent perspective, this paper presented an analysis of Germany’s strategy ‘AI made in Germany’ through the conceptual lens of ordoliberal political rationality. The first part of the paper analyses how the guiding vision of a human-centric AI not only adheres to ethical and legal (...) principles consistent with Germany’s liberal democratic constitutional system but also addresses the risks and promises inherent to the ordoliberal problematization of freedom. Second, it is scrutinized how the strategy cultivates the fear of not achieving technological sovereignty in the AI sector. Thereby, it frames the global AI race as a race of competing (national) approaches to governing AI and articulates an ordoliberal approach to governing AI (the ‘third way’), according to which government has to operate between the twin dangers of governing too much and not governing enough. Third, the paper analyses how this ordoliberal proportionality of governing structures Germany’s Science Technology & Innovation Policy. It is shown that the corresponding risk-based approach of regulating AI constitutes a security apparatus as it produces an assessment of fears: weighting the fear of the failure to innovate with the fear of the ramifications of innovation. Finally, two lines of critical engagement based on this analysis are conducted. (shrink)

The General Data Protection Regulation is a European Union regulation that will replace the existing Data Protection Directive on 25 May 2018. The most significant change is a huge increase in the maximum fine that can be levied for breaches of the regulation. Yet fewer than half of UK companies are fully aware of GDPR—and a number of those who were preparing for it stopped doing so when the Brexit vote was announced. A last-minute rush to become compliant is therefore (...) expected, and numerous companies are starting to offer advice, checklists and consultancy on how to comply with GDPR. In such an environment, artificial intelligence technologies ought to be able to assist by providing best advice; asking all and only the relevant questions; monitoring activities; and carrying out assessments. The paper considers four areas of GDPR compliance where rule based technologies and/or machine learning techniques may be relevant: Following compliance checklists and codes of conduct; Supporting risk assessments; Complying with the new regulations regarding technologies that perform automatic profiling; Complying with the new regulations concerning recognising and reporting breaches of security. It concludes that AI technology can support each of these four areas. The requirements that GDPR state for explanation and justification of reasoning imply that rule-based approaches are likely to be more helpful than machine learning approaches. However, there may be good business reasons to take a different approach in some circumstances. (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)

Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. (...) Which levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements. (shrink)

In contemporary artificial intelligence, the challenge is making intricate connectionist systems—comprising millions of parameters—more comprehensible, defensible, and rationally grounded. Two prevailing methodologies address this complexity. The inaugural approach amalgamates symbolic methodologies with connectionist paradigms, culminating in a hybrid system. This strategy systematizes extensive parameters within a limited framework of formal, symbolic rules. Conversely, the latter strategy remains staunchly connectionist, eschewing hybridity. Instead of internal transparency, it fabricates an external, transparent proxy system. This ancillary system’s mandate is elucidating the (...) principal system’s decisions, essentially approximating its outcomes. Leveraging natural language processing as our analytical lens, this paper elucidates both methodologies: the hybrid method is underscored by the compositional vector semantics, whereas the purely connectionist method evolves as a derivative of neural semantic parsers. This discourse extols the merits of the purely connectionist approach for its inherent flexibility and for a pivotal delineation: segregating the explanatory apparatus from the operational core, thereby rendering artificial intelligence systems reminiscent of human cognition. (shrink)

Peer review facilitates quality control and integrity of scientific research. Although publishing policies have adapted to include the use of Artificial Intelligence (AI) tools, such as Chat Generative Pre-trained Transformer (ChatGPT), in the preparation of manuscripts by authors, there is a lack of guidelines or policies on whether peer reviewers can use such tools. The present article highlights the lack of policies on the use of AI tools in the peer review process (PRP) and argues that we need to (...) go beyond policies by creating transparent procedures that will enable journals to investigate allegations of non-compliance and take decisions that will protect the integrity of the peer review system. Reviewers found to violate relevant policies must be excluded from the process to safeguard the integrity of the peer review system. (shrink)

This groundbreaking proceedings volume explores the integration of Artificial Intelligence (AI) across key domains—healthcare, finance, education, robotics, industrial and other engineering applications —unveiling its transformative potential and practical implications. With a multidisciplinary lens, it transcends technical aspects, fostering a comprehensive understanding while bridging theory and practice. Approaching the subject matter with depth, the book combines theoretical foundations with real-world case studies, empowering researchers, professionals, and enthusiasts with the knowledge and tools to effectively harness AI. Encompassing diverse AI topics—machine learning, (...) natural language processing, computer vision, data analytics and supervisory control — the volume showcases state-of-the-art techniques propelling AI advancements. Structured into four parts: Part 1: Artificial Intelligence (AI), explores evolving deep neural networks, reinforcement learning, and explainable AI, providing a deep dive into the technical foundations of AI advancements. Part 2: Robotics and Control Systems, delves into the integration of AI in robotics and automatic control, addressing supervisory control, automated robotic movement coordination, anomaly detection, dynamic programming, and fault tolerance, offering insights into the evolving landscape of intelligent automation. Part 3: AI and Society, examines the societal impact of AI through chapters on ethical considerations, economic growth, environmental engagements, and hazard management, providing a holistic perspective on AI's role in shaping society. Part 4: PhD Symposium, presents the future of AI through cutting-edge research, covering legal and ethical dimensions, privacy considerations, and computationally efficient solutions, offering a glimpse into the next generation of AI advancements. Catering to a diverse audience—from industry leaders to students—the volume consolidates the expertise of renowned professionals, serving as a comprehensive resource for navigating the ever-evolving AI landscape. An essential reference for those staying at the forefront of AI developments. (shrink)

Artificial intelligence (AI) is being tightly integrated into healthcare today. Even though AI is being utilized in healthcare, its application in clinical settings and health professions education is still controversial. The study described the perceptions of AI and its integration into health professions education and healthcare among health professions students. This descriptive phenomenological study analyzed the data from a purposive sample of 33 health professions students at a university in Kazakhstan using the thematic approach. Data collection was conducted (...) from March 30 to May 5, 2023, using an online questionnaire with four open-ended questions. Four significant themes describing the perceptions of the health professions students on AI and its integration into health profession education and healthcare was derived: enhanced interactive learning experiences, integrating artificial intelligence-powered diagnostic and decision-support tools into health professions education, AI technologies assist students in preparing for future roles in healthcare and integrating AI technologies presents several challenges. Health professions students perceived that integrating AI into health professions education and healthcare enhances their interactive learning experiences, increases knowledge and application of complicated medical topics, and prepares them for future healthcare roles. (shrink)

Philosophical and sociological approaches in technology have increasingly shifted toward describing AI (artificial intelligence) systems as ‘(moral) agents,’ while also attributing ‘agency’ to them. It is only in this way – so their principal argument goes – that the effects of technological components in a complex human-computer interaction can be understood sufficiently in phenomenological-descriptive and ethical-normative respects. By contrast, this article aims to demonstrate that an explanatory model only achieves a descriptively and normatively satisfactory result if the concepts of (...) ‘(moral) agent’ and ‘(moral) agency’ are exclusively related to human agents. Initially, the division between symbolic and sub-symbolic AI, the black box character of (deep) machine learning, and the complex relationship network in the provision and application of machine learning are outlined. Next, the ontological and action-theoretical basic assumptions of an ‘agency’ attribution regarding both the current teleology-naturalism debate and the explanatory model of actor network theory are examined. On this basis, the technical-philosophical approaches of Luciano Floridi, Deborah G. Johnson, and Peter-Paul Verbeek will all be critically discussed. Despite their different approaches, they tend to fully integrate computational behavior into their concept of ‘(moral) agency.’ By contrast, this essay recommends distinguishing conceptually between the different entities, causalities, and relationships in a human-computer interaction, arguing that this is the only way to do justice to both human responsibility and the moral significance and causality of computational behavior. (shrink)

Purpose Artificial intelligence (AI) has sparked interest in various areas, including marketing. However, this exhilaration is being tempered by growing concerns about the moral and legal implications of using AI in marketing. Although previous research has revealed various ethical and legal issues, such as algorithmic discrimination and data privacy, there are no definitive answers. This paper aims to fill this gap by investigating AI’s ethical and legal concerns in marketing and suggesting feasible solutions. Design/methodology/approach The paper synthesises information (...) from academic articles, industry reports, case studies and legal documents through a thematic literature review. A qualitative analysis approach categorises and interprets ethical and legal challenges and proposes potential solutions. Findings The findings of this paper raise concerns about ethical and legal challenges related to AI in the marketing area. Ethical concerns related to discrimination, bias, manipulation, job displacement, absence of social interaction, cybersecurity, unintended consequences, environmental impact, privacy and legal issues such as consumer security, responsibility, liability, brand protection, competition law, agreements, data protection, consumer protection and intellectual property rights are discussed in the paper, and their potential solutions are discussed. Research limitations/implications Notwithstanding the interesting insights gathered from this investigation of the ethical and legal consequences of AI in marketing, it is important to recognise the limits of this research. Initially, the focus of this study is confined to a review of the most important ethical and legal issues pertaining to AI in marketing. Additional possible repercussions, such as those associated with intellectual property, contracts and licencing, should be investigated more deeply in future studies. Despite the fact that this study gives various answers and best practices for tackling the stated ethical and legal concerns, the viability and efficacy of these solutions may differ depending on the context and industry. Thus, more research and case studies are required to evaluate the applicability and efficacy of these solutions in other circumstances. This research is mostly based on a literature review and may not represent the experiences or opinions of all stakeholders engaged in AI-powered marketing. Further study might involve interviews or surveys with marketing professionals, customers and other key stakeholders to offer a full knowledge of the practical difficulties and solutions. Because of the rapid speed of technical progress, AI’s ethical and regulatory ramifications in marketing are continually increasing. Consequently, this work should be a springboard for more research and continuing conversations on this subject. Practical implications This study’s findings have several practical implications for marketing professionals. Emphasising openness and explainability: Marketing professionals should prioritise transparency in their use of AI, ensuring that customers are fully informed about data collection and utilisation for targeted advertising. By promoting openness and explainability, marketers can foster customer trust and avoid the negative consequences of a lack of transparency. Establishing ethical guidelines: Marketing professionals need to develop ethical rules for the creation and implementation of AI-powered marketing strategies. Adhering to ethical principles ensures compliance with legal norms and aligns with the organisation’s values and ideals. Investing in bias detection tools and privacy-enhancing technology: To mitigate risks associated with AI in marketing, marketers should allocate resources to develop and implement bias detection tools and privacy-enhancing technology. These tools can identify and address biases in AI algorithms, safeguard consumer privacy and extract valuable insights from consumer data. Social implications This study’s social implications emphasise the need for a comprehensive approach to address the ethical and legal challenges of AI in marketing. This includes adopting a responsible innovation framework, promoting ethical leadership, using ethical decision-making frameworks and conducting multidisciplinary research. By incorporating these approaches, marketers can navigate the complexities of AI in marketing responsibly, foster an ethical organisational culture, make informed ethical decisions and develop effective solutions. Such practices promote public trust, ensure equitable distribution of benefits and risk, and mitigate potential negative social consequences associated with AI in marketing. Originality/value To the best of the authors’ knowledge, this paper is among the first to explore potential solutions comprehensively. This paper provides a nuanced understanding of the challenges by using a multidisciplinary framework and synthesising various sources. It contributes valuable insights for academia and industry. (shrink)

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which (...) draws attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or “patients” of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency. (shrink)

The present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated vehicles (...) (CAVs). On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail. (shrink)

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)

The problem of epistemic opacity in Artificial Intelligence is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this claim, I (...) first contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system. While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties. (shrink)

This article develops a conceptual framework for regulating Artificial Intelligence (AI) that encompasses all stages of modern public policy-making, from the basics to a sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2010 and 2020, a dispersed body of knowledge loosely centred around the “framework” concept was organised, described, and pictured for better understanding. The resulting integrative framework encapsulates 21 prior depictions of the policy-making process, aiming to achieve (...) gold-standard societal values, such as fairness, freedom and long-term sustainability. This challenge of integrating the AIR literature was matched by the identification of a structural common ground among different approaches. The AIR framework results from an effort to identify and later analytically deduce synthetic, and generic tool for a country-specific, stakeholder-aware analysis of AIR matters. Theories and principles as diverse as Agile and Ethics were combined in the “AIR framework”, which provides a conceptual lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated. Moreover, the AIR framework serves as a theoretically sound starting point for endeavours related to AI regulation, from legislation to research and development. As we know, the (potential) impacts of AI on society are immense, and therefore the discourses, social negotiations, and applications of this technology should be guided by common grounds based on contemporary governance techniques, and social values legitimated via dialogue and scientific research. (shrink)

What does it mean to consider the world of AI through a Christian lens? Rapid developments in AI continue to reshape society, raising new ethical questions and challenging our understanding of the human person. Encountering Artificial Intelligence draws on Pope Francis’ discussion of a culture of encounter and broader themes in Catholic social thought in order to examine how current AI applications affect human relationships in various social spheres and offers concrete recommendations for better implementation. The document also explores (...) questions regarding personhood, consciousness, and the kinds of relationships humans might have with even the most advanced AI. Through these discussions, this book investigates the theoretical and practical challenges to interpersonal encounter raised by the age of AI. (AI Research Group for the Centre for Digital Culture of the Dicastery of Culture and Education of the Holy See). (shrink)

Artificial intelligence tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews and hiring, and actual and potential bias can arise in each of these stages. One major challenge is to mitigate AI bias and promote fairness in opaque AI systems. This paper argues that the equal opportunity merit principle is an ethical approach for fair AI employment decision-making. Further, explainable AI can mitigate the opacity problem by placing greater emphasis on enhancing the (...) understanding of reasonable users and affected persons as to the AI output. Both the equal opportunity merit principle and explainable AI should be integrated in the design and implementation of AI employment decision-making systems so as to ensure, as far as possible, that the AI output is arrived at through a fair process. (shrink)

The increasing use of Artificial Intelligence for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily focused (...) on how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring. (shrink)

The rapid development of artificial intelligence has brought many opportunities and challenges to organization. Some studies have shown that AI can improve organizational creativity. However, the existing research lacks an effective transformation path. This paper makes an innovative approach from the perspective of knowledge sharing, establishes an integration model of artificial intelligence capability, knowledge sharing and organizational creativity. Based on 189 questionnaire data, we use multi-level regression analysis and bootstrap method to analyze the influence mechanism. The results (...) show that artificial intelligence has a positive effect on knowledge sharing, knowledge sharing has a positive effect on organizational creativity, knowledge sharing mediates the relationship between artificial intelligence and organizational creativity, and organizational cohesion has a positive moderating effect on the relationship between artificial intelligence and knowledge sharing. The results supplement the existing research on the relationship between artificial intelligence capability and organizational creativity, expand the theoretical boundary and application space from the perspective of knowledge sharing at the organizational level, and provide reference for organizations to improve creativity. (shrink)

Artificial Intelligence (AI) is increasingly adopted in society, creating numerous opportunities but at the same time posing ethical challenges. Many of these are familiar, such as issues of fairness, responsibility and privacy, but are presented in a new and challenging guise due to our limited ability to steer and predict the outputs of AI systems. This chapter first introduces these ethical challenges, stressing that overviews of values are a good starting point but frequently fail to suffice due to the (...) context sensitivity of ethical challenges. Frequently, additional (ethical) values emerge for specific applications, as e.g. challenges with fraud detection are very different from those around language technologies. Second, methods to tackle these challenges are discussed. Main ethical theories (virtue ethics, consequentialism, and deontology) are shown to provide a starting point, but often lack the details needed for actionable AI Ethics. Instead, mid-level philosophical theories coupled to design-approaches such as Design for Values, together with interdisciplinary working methods, offer the best way forwards. The chapter aims to show how these approaches can lead to an ethics of AI that is actionable and that can be proactively integrated in the design of AI systems. (shrink)

Resumen: El presente trabajo analiza si los Sistemas de apoyo a la decisión (DSSs) y otros asistentes para su uso, como las Reflection machines o los Personal Assistants that Learn (PAL), contribuyen de hecho a una mejora cognitiva, como habitualmente se tiende a asumir. Es decir, se examina si su potencial para expandir e impulsar la acción de las facultades cognoscitivas se ve efectivamente actualizado y, en consecuencia, si sirven para reafirmar el sentido capacitante de la IA y la extensión (...) de la agencia del sujeto a través de ella. Para ello, se pone de manifiesto que el diseño y aplicación de estos sistemas están mediados por una comprensión del conocimiento que actúa a modo de marco que limita sus posibilidades de mejorar las capacidades cognoscitivas humanas. Se analizan las condiciones en que la mejora cognitiva es posible a través del uso de estos sistemas, que remiten a una aproximación integradora que no tiene en cuenta solo el factor tecnológico, sino que repara en cómo, en su uso, intervienen factores gnoseológicos, así como psicológicos y sociológicos, que median en la capacidad efectiva de estos sistemas de ampliar las facultades de conocimiento, y de los que las tecnologías se sirven para ejercer su influencia. Abstract: The present article questions whether Decision Support Systems (DSSs) and other assistants for their use, such as Reflection machines or Personal Assistants that Learn (PAL), do in fact contribute to cognitive enhancement, as it is usually taken for granted. In other words, it is analysed whether their potential to effectively expand or augment cognitive capacities is actualised and, therefore, if the capabilities and the agency of the person appear in fact extended through artificial intelligence. For that purpose, it is highlighted that the design and implementation of AI systems are dependent on a certain understanding of human knowledge that acts as a framework that limits their actual capacity to enhance the cognitive faculties of the mind. The conditions under which this cognitive enhancement is possible through these systems are analysed and require an integrative approach that not only takes technology into account but realises that, in its use, gnoseological, as well as psychological and sociological factors are involved and mediate in the actual capacity of these systems to extend cognitive functions. These factors are also crucial for these technologies to exert their influence. (shrink)

The article presents grounds for defining the fetish of artificial intelligence (AI). We highlight the fundamental differences of AI from all earlier technological advances, as they are primarily related to its introduction into the human cognitive sphere and generating fundamentally new uncontrollable consequences for society. We provide solid evidence that the leaders of the globalist project are the main beneficiaries of the AI fetish. This is clearly manifested in the works of philosophers who are close to major technology corporations (...) and their mega-projects. We suggest considering the problem of how to use the capabilities of AI to overcome the growing international conflicts and the global crisis. The focus is on the problem of agency, which solution from the standpoint of an anthropomorphic approach to AI is fraught with serious negative consequences. Endowing AI with agency, responsibility is implicitly removed from the person who uses the technology, and the established legislative practice is also destroyed. We present AI as an agent endowed with a set of invariant generalized qualities that is similar to natural subjects. These qualities include: the ability to deliberation, reflexivity, communication and elements of sociability. Such a representation of AI as an agent (pseudo-subject) is consistent with the principle of distributed control in biology and psychology, which was called the principle of a dual subject. In combination with the systems of principles and ontologies specified in the concept of post-nonclassical cybernetics of self-developing environments, this will allow the use of AI as a means of social innovation, while maintaining control over AI technologies. This will also help to pose and solve the problem of integrating formations of artificial and natural intelligence while maintaining the basic qualities of carriers of natural intelligence. (shrink)

The article presents grounds for defining the fetish of artificial intelligence (AI). We highlight the fundamental differences of AI from all earlier technological advances, as they are primarily related to its introduction into the human cognitive sphere and generating fundamentally new uncontrollable consequences for society. We provide solid evidence that the leaders of the globalist project are the main beneficiaries of the AI fetish. This is clearly manifested in the works of philosophers who are close to major technology corporations (...) and their mega-projects. We suggest considering the problem of how to use the capabilities of AI to overcome the growing international conflicts and the global crisis. The focus is on the problem of agency, which solution from the standpoint of an anthropomorphic approach to AI is fraught with serious negative consequences. Endowing AI with agency, responsibility is implicitly removed from the person who uses the technology, and the established legislative practice is also destroyed. We present AI as an agent endowed with a set of invariant generalized qualities that is similar to natural subjects. These qualities include: the ability to deliberation, reflexivity, communication and elements of sociability. Such a representation of AI as an agent (pseudo-subject) is consistent with the principle of distributed control in biology and psychology, which was called the principle of a dual subject. In combination with the systems of principles and ontologies specified in the concept of post-nonclassical cybernetics of self-developing environments, this will allow the use of AI as a means of social innovation, while maintaining control over AI technologies. This will also help to pose and solve the problem of integrating formations of artificial and natural intelligence while maintaining the basic qualities of carriers of natural intelligence. (shrink)

BackgroundThe emergence of artificial intelligence (AI) in medicine has prompted the development of numerous ethical guidelines, while the involvement of patients in the creation of these documents lags behind. As part of the European PROFID project we explore patient perspectives on the ethical implications of AI in care for patients at increased risk of sudden cardiac death (SCD).AimExplore perspectives of patients on the ethical use of AI, particularly in clinical decision-making regarding the implantation of an implantable cardioverter-defibrillator (ICD).MethodsSemi-structured, future (...) scenario-based interviews were conducted among patients who had either an ICD and/or a heart condition with increased risk of SCD in Germany (n = 9) and the Netherlands (n = 15). We used the principles of the European Commission’s Ethics Guidelines for Trustworthy AI to structure the interviews.ResultsSix themes arose from the interviews: the ability of AI to rectify human doctors’ limitations; the objectivity of data; whether AI can serve as second opinion; AI explainability and patient trust; the importance of the ‘human touch’; and the personalization of care. Overall, our results reveal a strong desire among patients for more personalized and patient-centered care in the context of ICD implantation. Participants in our study express significant concerns about the further loss of the ‘human touch’ in healthcare when AI is introduced in clinical settings. They believe that this aspect of care is currently inadequately recognized in clinical practice. Participants attribute to doctors the responsibility of evaluating AI recommendations for clinical relevance and aligning them with patients’ individual contexts and values, in consultation with the patient.ConclusionThe ‘human touch’ patients exclusively ascribe to human medical practitioners extends beyond sympathy and kindness, and has clinical relevance in medical decision-making. Because this cannot be replaced by AI, we suggest that normative research into the ‘right to a human doctor’ is needed. Furthermore, policies on patient-centered AI integration in clinical practice should encompass the ethics of everyday practice rather than only principle-based ethics. We suggest that an empirical ethics approach grounded in ethnographic research is exceptionally well-suited to pave the way forward. (shrink)

Artificial intelligence is a general-purpose technology (GPT), term given to technologies that shape an entire era and reorient innovations by reconfiguring the economy’s logic and functioning and bringing in new business models. AI offers unprecedented opportunities and risks. The benefits of AI are extraordinary, as are its potential harms. Potential damage does not have the same degree of problematization, since the intensity and extent of the damage varies according to the domain and the object of application. To address the (...) scale of this challenge, regulation is necessary but not sufficient. Standards, unwritten codes of compliance and arbitration procedures, supervision and auditability, AI governance, international agreements, compliance with current local and global standards and laws. All of this needs to be integrated. Society seems to have no alternative to facing the challenges of at least mitigating the damage already identified and trying to predict future damage in advance. The purpose of this article is to encourage reflection regarding the main initiatives that are available to society to protect its citizens and organizations from the potential harm caused by AI models, vis-à-vis the technology’s own limits to act in ethical and legal compliance. (shrink)

This article conducts a literature review of current and future challenges in the use of artificial intelligence in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT (...) in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies. We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices in cyber physical systems. To support this argument, taxonomic methodology is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework. (shrink)

As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it through a latent class choice model. The core idea being that moral heterogeneity in society can be codified in terms of a small number of (...) classes with distinct moral preferences and that this codification can be used to express moral uncertainty of an AI. Choice analysis allows for the identification of classes and their moral preferences based on observed choice data. Our reformulation of the metanormative framework is theory-rooted and practical in the sense that it avoids runtime issues in real time applications. To illustrate our approach we conceptualize a society in which AI Systems are in charge of making policy choices. While one of the systems uses a baseline morally certain model, the other uses a morally uncertain model. We highlight cases in which the AI Systems disagree about the policy to be chosen, thus illustrating the need to capture moral uncertainty in AI systems. (shrink)

In this article, I establish a theory of knowledge approach for evaluating the use of computers for educational purposes at the university. In so doing, I trace part of the history of the “enabling factor” of Artificial Intelligence in this sector, an important element that has been integrated into everyday learning environments. The result of my reflection is a dialogical structure, directly inspired by past technology assessment research, which illustrates the conceptual advancement of researchers in the field of (...) learning technologies. The notions covered have implications in future policy-related discourse with regards to education. (shrink)

This article analyzes emerging artificial intelligence (AI)-enhanced lie detection systems from ethical and human resource (HR) management perspectives. I show how these AI enhancements transform lie detection, followed with analyses as to how the changes can lead to moral problems. Specifically, I examine how these applications of AI introduce human rights issues of fairness, mental privacy, and bias and outline the implications of these changes for HR management. The changes that AI is making to lie detection are altering the (...) roles of human test administrators and human subjects, adding machine learning-based AI agents to the situation and establishing invasive data collection processes as well as introducing certain biases in results. I project that the potentials for pervasive and continuous lie detection initiatives (“truth machines”) are substantial, displacing human-centered efforts to establish trust and foster integrity in organizations. I argue that if it is possible for HR managers to do so, they should cease using technologically-based lie detection systems entirely and work to foster trust and accountability on a human scale. However, if these AI-enhanced technologies are put into place by organizations by law, agency mandate, or other compulsory measures, care should be taken that the impacts of the technologies on human rights and wellbeing are considered. The article explores how AI can displace the human agent in some aspects of lie detection and credibility assessment scenarios, expanding the prospects for inscrutable, “black box” processes and novel physiological constructs (such as “biomarkers of deceit”) that may increase the potential for such human rights concerns as fairness, mental privacy, and bias. Employee interactions with autonomous lie detection systems rather with than human beings who administer specific tests can reframe organizational processes and rules concerning the assessment of personal honesty and integrity. The dystopian projection of organizational life in which analyses and judgments of the honesty of one’s utterances are made automatically and in conjunction with one’s personal profile provides unsettling prospects for the autonomy of self-representation. (shrink)

Journals and publishers are increasingly using artificial intelligence (AI) to screen submissions for potential misconduct, including plagiarism and data or image manipulation. While using AI can enhance the integrity of published manuscripts, it can also increase the risk of false/unsubstantiated allegations. Ambiguities related to journals’ and publishers’ responsibilities concerning fairness and transparency also raise ethical concerns. In this Topic Piece, we offer the following guidance: (1) All cases of suspected misconduct identified by AI tools should be carefully reviewed by (...) humans to verify accuracy and ensure accountability; (2) Journals/publishers that use AI tools to detect misconduct should use only well-tested and reliable tools, remain vigilant concerning forms of misconduct that cannot be detected by these tools, and stay abreast of advancements in technology; (3) Journals/publishers should inform authors about irregularities identified by AI tools and give them a chance to respond before forwarding allegations to their institutions in accordance with Committee on Publication Ethics guidelines; (4) Journals/publishers that use AI tools to detect misconduct should screen all relevant submissions and not just random/purposefully selected submissions; and (5) Journals should inform authors about their definition of misconduct, their use of AI tools to detect misconduct, and their policies and procedures for responding to suspected cases of misconduct. (shrink)