Recent AI progress led to a boost in beneficial applications from multiple research areas including VR. Simultaneously, in this newly unfolding deepfake era, ethically and security-relevant disagreements arose in the scientific community regarding the epistemic capabilities of present-day AI. However, given what is at stake, one can postulate that for a responsible approach, prior to engaging in a rigorous epistemic assessment of AI, humans may profit from a self-questioning strategy, an examination and calibration of the experience of their own (...) epistemic agency – especially to counteract both intentional misdirection by unethical actors and unintentional epistemic self-sabotage. In this paper, we expound on a new avenue of utilizing AIVR tools to advance an AI-related misdirection awareness of humans in the deepfake era. Firstly, we harness scientific knowledge from the psychology and neuroscience of magic where the study of misdirection techniques is center stage. Secondly, we connect the latter to creativity research linking human creative potential to inspiration from the seemingly impossible. Overall, AIVR could become an empowering experiential testbed for human epistemic agency enabling a better rational evaluation of AI capabilities. However, a misuse of the same type of tools could yield AIVR safety risks if not counteracted preemptively. (shrink)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act. As we are an interdisciplinary group, we address the multi-faceted implications of the mass-scale diffusion of AI-driven technologies. The result of (...) our exercise lead us to postulate the necessity of a behavioural theory that standardizes the interaction process of interdisciplinary groups. In light of this, we conduct a review of the existing approaches to interdisciplinary research on AI appliances, leading to the development of methodologies like ethics-by-design and value-sensitive design, evaluating their strengths and weaknesses. We then put forth an iterative process theory hinging on a narrative approach consisting of four phases: definition of the hypothesis space, building-up of a common lexicon, scenario-building, interdisciplinary self-assessment. Finally, we identify the most relevant fields of application for such a methodology and discuss possible case studies. (shrink)

This paper provides a starting point for thinking beyond a research–practice divide and discusses possible new conceptualizations of intervention and the role of IT research in contemporary organizational settings. ‘IT research’ denotes a conglomerate of overlapping research conducted under the headings of Information Systems, Systems Development, Critical IS Research and Participatory Design. The paper applies this joint notion of IT research and the IT researcher to draw parallels across these niches of research regarding (...) the question of intervention. Through an analysis of selected field study events, a prominent notion of intervention (as being active as opposed to being passive) is reworked in terms of intervention as circumstance, a circumstantial interplay of situated practices. In closing, subsequent possibilities for repositioning the IT researcher are discussed in terms of reflexivity, facilitation or being a trickster. (shrink)

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI. We consider the model's origins, analyze the challenges IRBs are facing in the contexts of both industry and academia, and offer concrete recommendations for how these committees might be adapted (...) in order to provide an effective mechanism of oversight for health‐related AI research. (shrink)

Purpose Digital transformation leverages digital technologies to change current processes and introduce new processes in any organisation’s business model, customer/user experience and operational processes. Artificial intelligence plays a significant role in achieving DT. As DT is touching each sphere of humanity, AI led DT is raising many fundamental questions. These questions raise concerns for the systems deployed, how they should behave, what risks they carry, the monitoring and evaluation control we have in hand, etc. These issues call for the need (...) to integrate ethics in AI led DT. The purpose of this study is to develop an “AI led ethical digital transformation framework”. Design/methodology/approach Based on the literature survey, various existing business ethics decision-making models were synthesised. The authors mapped essential characteristics such as intensity and the individual, organisational and opportunity factors of ethics models with the proposed AI led ethical DT. The DT framework is evaluated using a thematic analysis of 23 expert interviews with relevant AI ethics personas from industry and society. The qualitative data of the interviews and opinion data has been analysed using MAXQDA software. Findings The authors have explored how AI can drive the ethical DT framework and have identified the core constituents of developing an AI led ethical DT framework. Backed by established ethical theories, the paper presents how DT pillars are related and sequenced to ethical factors. This research provides the potential to examine theoretically sequenced ethical factors with practical DT pillars. Originality/value The study establishes deduced and induced ethical value codes based on thematic analysis to develop guidelines for the pursuit of ethical DT. The authors identify four unique induced themes, namely, corporate social responsibility, perceived value, standard benchmarking and learning willingness. The comprehensive findings of this research, supported by a robust theoretical background, have substantial implications for academic research and corporate applicability. The proposed AI led ethical DT framework is unique and can be used for integrated social, technological and economic ethical research. (shrink)

The Research Handbook on Health, AI and the Law explores the use of AI in healthcare, identifying the important laws and ethical issues that arise from its use. Adopting an international approach, it analyses the varying responses of multiple jurisdictions to the use of AI and examines the influence of major religious and secular ethical traditions. Bringing together a diverse range of carefully selected legal experts, the Research Handbook critically assesses the different uses of AI in healthcare and (...) its promise to provide greater accuracy for healthcare professionals and patients by diagnosing diseases and detecting illness earlier and helping hospitals run more efficiently. It also highlights a series of legal and ethical challenges AI raises relating to bias, privacy, data security, medical liability, informed consent and intellectual property. AI governance is rigorously examined in countries across the globe spanning Asia, Europe and the US while different responses from international organisations towards AI in healthcare are also evaluated. This Research Handbook is a key resource for scholars and law students and for those interested in current and developing legal paradigms. Its legal and practical dimensions will also be beneficial to lawyers practising in health law and internet and technology law, policymakers and medical professionals. (shrink)

ObjectsHealth Behaviours in School-aged Children is an international survey programme aiming to investigate adolescents’ health behaviours, subjective perception of health status, wellbeing, and the related contextual information. Our scoping review aimed to synthesise the evidence from HBSC about the relationship between family environmental contributors and adolescents’ health-related outcomes.MethodsWe searched previous studies from six electronic databases. Two researchers identified the qualified publications independently by abstract and full-text screening with the assistance of an NLP-based AI instrument, ASReview. Publications were included (...) if they were based on HBSC data and investigated the effects of family environment on adolescents’ health outcomes. Researches addressed family-related factors as mediators or moderators were also included.ResultsA total of 241 articles were included. Family environmental contributors could be mapped into six categories: Demographic backgrounds ; General family’s psycho-socio functions ; Parenting behaviours ; Parental health behaviours ; Family activities ; and Siblings. Except for 75 papers that assessed family variables as moderators and mediators, the others suggested family environment was an independent variable. Only five studies employed the data-driven approach.ConclusionOur results suggest most research studies focussed on the influences of family demographic backgrounds on adolescents’ health. The researches related to parental health behaviours and siblings are most inadequate. Besides, we recommend further research studies to focus on the mediator/moderator roles of the family, for exploring the deep mechanism of the family’s impacts. Also, it would be valuable to consider data-driven analysis more in the future, as HBSC has mass variables and data. (shrink)

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

The article explores the use of the “ChatGPT” artificial intelligence language model in the Human Sciences field. ChatGPT uses natural language processing techniques to imitate human language and engage in artificial conversations. While the platform has gained attention from the scientific community, opinions on its usage are divided. The article presents some conversations with ChatGPT to examine ethical, relational and linguistic issues related to human-computer interaction (HCI) and assess its potential for Human Sciences research. The interaction with the (...) platform recalls the “uncanny valley” phenomenon still known to Social Robotics. While ChatGPT can be beneficial, it requires proper supervision and verification of its results. Furthermore, new research methods must be developed for qualitative, quantitative, and mixed methods. (shrink)

This paper focuses on the potential of “equitech”—AI technology that improves equity. Recently, interventions have been developed to reduce the harm of implicit bias, the automatic form of stereotype or prejudice that contributes to injustice. However, these interventions—some of which are assisted by AI-related technology—have significant limitations, including unintended negative consequences and general inefficacy. To overcome these limitations, we propose a two-dimensional framework to assess current AI-assisted interventions and explore promising new ones. We begin by using the case of (...) human resource recruitment as a focal point to show that existing approaches have exploited only a subset of the available solution space. We then demonstrate how our framework facilitates the discovery of new approaches. The first dimension of this framework helps us systematically consider the analytic information, intervention implementation, and modes of human-machine interaction made available by advancements in AI-related technology. The second dimension enables the identification and incorporation of insights from recent research on implicit bias intervention. We argue that a design strategy that combines complementary interventions can further enhance the effectiveness of interventions by targeting the various interacting cognitive systems that underlie implicit bias. We end with a discussion of how our cognitive interventions framework can have positive downstream effects for structural problems. (shrink)

AI research is growing rapidly raising various ethical issues related to safety, risks, and other effects widely discussed in the literature. We believe that in order to adequately address those issues and engage in a productive normative discussion it is necessary to examine key concepts and categories. One such category is anthropomorphism. It is a well-known fact that AI’s functionalities and innovations are often anthropomorphized (i.e., described and conceived as characterized by human traits). The general public’s anthropomorphic attitudes (...) and some of their ethical consequences (particularly in the context of social robots and their interaction with humans) have been widely discussed in the literature. However, how anthropomorphism permeates AI research itself (i.e., in the very language of computer scientists, designers, and programmers), and what the epistemological and ethical consequences of this might be have received less attention. In this paper we explore this issue. We first set the methodological/theoretical stage, making a distinction between a normative and a conceptual approach to the issues. Next, after a brief analysis of anthropomorphism and its manifestations in the public, we explore its presence within AI research with a particular focus on brain-inspired AI. Finally, on the basis of our analysis, we identify some potential epistemological and ethical consequences of the use of anthropomorphic language and discourse within the AI research community, thus reinforcing the need of complementing the practical with a conceptual analysis. (shrink)

Upon previous research, the matrilineal system seems to oppose Islamic teaching. However, the matrilineal system practiced by the Minangkabau society in West Sumatra, Indonesia has its uniqueness. Thus, this study aims to examine the correlation between the Qur’an and gender roles within the context of Minangkabau customs, specifically focusing on the matrilineal aspect. The present study employs qualitative methods for conducting library research through critical analysis. This study discovered that the matrilineal system practiced by the Minangkabau society aligns (...) with Qur’anic teachings that respect the position of women in Islam. In the matrilineal system of Minangkabau, women hold significant strength and play an influential role in cultural and traditional practices. The Minangkabau tribe’s matrilineal system encompasses three key elements: marriage, inheritance and offspring. The marriage system is exogamous. Additionally, the practice of marriage is localised, with the husband typically residing in the wife’s household. Their inheritance is divided into pusako tinggi [from ancestors from generation to generation] and pusako randah (from the efforts of married couple). Women get pusako tinggi as they serve as protectors and a symbol of their community. In comparison, men take the responsibility of overseeing its preservation to prevent any form of misuse. Pusako randah is distributed as Islamic teaching. Lineage determination is primarily based on paternal lineage, aligning with Islamic teaching. Upon comprehensive analysis, it becomes evident that there is no conflict between the matrilineal system and Islamic teachings, particularly concerning principles of gender equality. Both women and men have significant roles, as the Qur’an provides extensive explanations regarding gender equality.Contribution: This research holds significant findings in establishing a great relation between Islamic teachings based on the Qur’an and the matrilineal principles practiced by the Minangkabau tribe. (shrink)

Human computer interaction (HCI) has grown into a mature field of research. With artificial intelligence (AI) finding ubiquitous applications, HCI research is now moving ahead towards accommodating and integrating these approaches. A part of the research community is of the opinion that HCI and AI are fundamentally opposed to each other: with AI-powered devices being demonic and humans are to lose in a race against them and HCI-oriented products being human centred being the flag bearers of the (...) human race. In this paper, we look at and analyse this perspective of how HCI relates to AI and the potential HCI–AI integration. We suggest that humans evolve by having dynamic identities. We further discuss its implications on the AI critique and suggest some new perspectives on the possible integration of HCI and AI research which we hope would be useful for the community. (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)

This is the first book to provide a coherent overview over the ethical implications of AI-related technologies in medicine. It explores how these technologies transform practices, relationships, and environments in the clinical field. It provides an introduction into ethical issues such as data security and privacy protection, bias and algorithmic fairness, trust and transparency, challenges to the doctor-patient relationship, and new perspectives for informed consent. The book focuses on the transformative impact that technology is having on medicine, and discusses (...) several strategies for dealing with the resulting challenges. It also introduces innovative methods of ethics research for addressing existing desiderata and future challenges. This book is written to inform health care professionals, policy-makers, and researchers in medicine, health sciences, nursing science, social sciences, and ethics, but may also function as a primary textbook for graduate as well as undergraduate university courses. (shrink)

Integrating artificial intelligence (AI) into critical domains such as healthcare holds immense promise. Nevertheless, significant challenges must be addressed to avoid harm, promote the well-being of individuals and societies, and ensure ethically sound and socially just technology development. Innovative approaches like Embedded Ethics, which refers to integrating ethics and social science into technology development based on interdisciplinary collaboration, are emerging to address issues of bias, transparency, misrepresentation, and more. This paper aims to develop this approach further to enable future projects (...) to effectively deploy it. Based on the practical experience of using ethics and social science methodology in interdisciplinary AI-related healthcare consortia, this paper presents several methods that have proven helpful for embedding ethical and social science analysis and inquiry. They include (1) stakeholder analyses, (2) literature reviews, (3) ethnographic approaches, (4) peer-to-peer interviews, (5) focus groups, (6) interviews with affected groups and external stakeholders, (7) bias analyses, (8) workshops, and (9) interdisciplinary results dissemination. We believe that applying Embedded Ethics offers a pathway to stimulate reflexivity, proactively anticipate social and ethical concerns, and foster interdisciplinary inquiry into such concerns at every stage of technology development. This approach can help shape responsible, inclusive, and ethically aware technology innovation in healthcare and beyond. (shrink)

Argumentation schemes have played a key role in our research projects on computational models of natural argument over the last decade. The catalogue of schemes in Walton, Reed and Macagno’s 2008 book, Argumentation Schemes, served as our starting point for analysis of the naturally occurring arguments in written text, i.e., text in different genres having different types of author, audience, and subject domain (genetics, international relations, environmental science policy, AI ethics), for different argument goals, and for different possible future (...) applications. We would often first attempt to analyze the arguments in our corpora in terms of those schemes, then adapt schemes as needed for the goals of the project, and in some cases implement them for use in computational models. Among computational researchers, the main interest in argumentation schemes has been for use in argument mining by applying machine learning methods to existing argument corpora. In contrast, a primary goal of our research has been to learn more about written arguments themselves in various contemporary fields. Our approach has been to manually analyze semantics, discourse structure, argumentation, and rhetoric in texts. Another goal has been to create sharable digital corpora containing the results of our studies. Our approach has been to define argument schemes for use by human corpus annotators or for use in logic programs for argument mining. The third goal is to design useful computer applications based upon our studies, such as argument diagramming systems that provide argument schemes as building blocks. This paper describes each of the various projects: the methods, the argument schemes that were identified, and how they were used. Then a synthesis of the results is given with a discussion of open issues. (shrink)

Scholars, policymakers and organizations in the EU, especially at the level of the European Commission, have turned their attention to the ethics of (trustworthy and human-centric) Artificial Intelligence (AI). However, there has been little reflexivity on (1) the history of the ethics of AI as an institutionalized phenomenon and (2) the comparison to similar episodes of “ethification” in other fields, to highlight common (unresolved) challenges.Contrary to some mainstream narratives, which stress how the increasing attention to ethical aspects of AI is (...) due to the fast pace and increasing risks of technological developments, Science and Technology Studies(STS)-informed perspectives highlight that the rise of institutionalized assessment methods indicates a need for governments to gain more control of scientific research and to bring EU institutions closer to the public on controversies related to emerging technologies.This article analyzes how different approaches of the recent past (i.e. bioethics, technology assessment (TA) and ethical, legal and social (ELS) research, Responsible Research and Innovation (RRI)) followed one another, often “in the name of ethics”, to address previous criticisms and/or to legitimate certain scientific and technological research programs. The focus is on how a brief history of the institutionalization of these approaches can provide insights into present challenges to the ethics of AI related to methodological issues, mobilization of expertise and public participation. (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)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

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

Artificial Intelligence (AI) is taking centre stage in economic growth and business operations alike. Public discourse about the practical and ethical implications of AI has mainly focussed on the societal level. There is an emerging knowledge base on AI risks to human rights around data security and privacy concerns. A separate strand of work has highlighted the stresses of working in the gig economy. This prevailing focus on human rights and gig impacts has been at the expense of a closer (...) look at how AI may be reshaping traditional workplace relations and, more specifically, workplace health and safety. To address this gap, we outline a conceptual model for developing an AI Work Health and Safety (WHS) Scorecard as a tool to assess and manage the potential risks and hazards to workers resulting from AI use in a workplace. A qualitative, practice-led research study of AI adopters was used to generate and test a novel list of potential AI risks to worker health and safety. Risks were identified after cross-referencing Australian AI Ethics Principles and Principles of Good Work Design with AI ideation, design and implementation stages captured by the AI Canvas, a framework otherwise used for assessing the commercial potential of AI to a business. The unique contribution of this research is the development of a novel matrix itemising currently known or anticipated risks to the WHS and ethical aspects at each AI adoption stage. (shrink)

The relationship between philosophy and research on artificial intelligence (AI) has been difficult since its beginning, with mutual misunderstanding and sometimes even hostility. By contrast, we show how an approach informed by both philosophy and AI can be productive. After reviewing some popular frameworks for computation and learning, we apply the AI methodology of “build it and see” to tackle the philosophical and psychological problem of characterizing perception as distinct from sensation. Our model comprises a network of very simple, (...) but interacting agents which have binary experiences of the “yes/no”-type and communicate their experiences with each other. When does such a network refer to a single agent instead of a distributed network of entities? We apply machine learning techniques to address the following related questions: i) how can the model explain stability of compound entities, and ii) how could the model implement a single task such as perceptual inference? We thereby find consistency with previous work on “interface” strategies from perception research. While this reflects some necessary conditions for the ascription of agency, we suggest that it is not sufficient. Here, AI research, if it is intended to contribute to conceptual understanding, would benefit from issues previously raised by philosophy. We thus conclude the article with a discussion of action-selection, the role of embodiment, and consciousness to make this more explicit. We conjecture that a combination of AI research and philosophy allows general principles of mind and being to emerge from a “quasi-empirical” investigation. (shrink)

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

In this paper we, an epistemologist and a machine learning scientist, argue that we need to pursue a novel area of philosophical research in AI – the ethics of belief for AI. Here we take the ethics of belief to refer to a field at the intersection of epistemology and ethics concerned with possible moral, practical, and other non-truth-related dimensions of belief. In this paper we will primarily be concerned with the normative question within the ethics of belief (...) regarding what agents – both human and artificial – ought to believe, rather than with questions concerning whether beliefs meet certain evaluative standards such as being true, being justified, constituting knowledge, etc. We suggest four topics in extant work in the ethics of (human) belief that can be applied to an ethics of AI belief: doxastic wronging by AI (morally wronging someone in virtue of beliefs held about them); morally owed beliefs (beliefs that agents are morally obligated to hold); pragmatic and moral encroachment (cases where the practical or moral features of a belief is relevant to its epistemic status, and in our case specifically to whether an agent ought to hold the belief); and moral responsibility for AI beliefs. We also indicate two relatively nascent areas of philosophical research that haven’t yet been generally recognized as ethics of AI belief research, but that do fall within this field of research in virtue of investigating various moral and practical dimensions of belief: the epistemic and ethical decolonization of AI; and epistemic injustice in 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)

Artificial intelligence (AI) has become nearly ubiquitous in modern society, from components of mobile applications to medical support systems, and everything in between. In societally impactful systems imbued with AI, there has been increasing concern related to opaque AI, that is, artificial intelligence where it is unclear how or why certain decisions are reached. This has led to a recent boom in research on “explainable AI” (XAI), or approaches to making AI more explainable and understandable to human users. (...) In the military domain, numerous bodies have argued that autonomous and AI-enabled weapon systems ought not incorporate unexplainable AI, with the International Committee of the Red Cross and the United States Department of Defense both explicitly including explainability as a relevant factor in the development and use of such systems. In this article, I present a cautiously critical assessment of this view, arguing that explainability will be irrelevant for many current and near-future autonomous systems in the military (which do not incorporate any AI), that it will be trivially incorporated into most military systems which do possess AI (as these generally possess simpler AI systems), and that for those systems with genuinely opaque AI, explainability will prove to be of more limited value than one might imagine. In particular, I argue that explainability, while indeed a virtue in design, is a virtue aimed primarily at designers and troubleshooters of AI-enabled systems, but is far less relevant for users and handlers actually deploying these systems. I further argue that human–machine teaming is a far more important element of responsibly using AI for military purposes, adding that explainability may undermine efforts to improve human–machine teamings by creating aprima faciesense that the AI, due to its explainability, may be utilized with little (or less) potential for mistakes. I conclude by clarifying that the arguments are not against XAI in the military, but are instead intended as a caution against over-inflating the value of XAI in this domain, or ignoring the limitations and potential pitfalls of this approach. (shrink)

Applications of artificial intelligence (AI) bear great transformative potential in the economic, technological and social sectors, impacting especially future work environments. Ethical regulation of AI requires a relational understanding of the technology by relevant stakeholder groups such as researchers, developers, politicians, civil servants, affected workers or other users applying AI in their work processes. The purpose of this paper is to support relational AI discourse for an improved ethical framing and regulation of the technology. The argumentation emphasizes a widespread reembodied (...) understanding of AI technology as critical requirement for capable ethical and regulatory frameworks. A sociotechnical perspective encourages the material interpretation of AI as reembodied adaptation of biological intelligence. Reviewing Cartesian dualism as motivating the disembodiment of human intelligence for its transfer to machines, the argumentation develops an integrated embodiment concept of AI in its mechanistic, naturalistic, combined AI and neuroethical, and relational contexts. This concept is discussed in relation to basic phenomenological and postphenomenological assumptions, and is applied to the example of AI-based neurotechnology potentially disrupting future work processes. Strengthening a human-centered approach, the presented concept for a reembodied understanding of AI technology enables better integrated ethical and regulatory debates, and improves social discourse and human agency in developing and regulating AI technology. (shrink)

Artificial intelligence (AI) is embedded in a wide variety of Smart City applications and infrastructures, often without the citizens being aware of the nature of their “intelligence”. AI can affect citizens’ lives concretely, and thus, there may be uncertainty, concerns, or even fears related to AI. To build acceptable futures of Smart Cities with AI-enabled functionalities, the Human-Centered AI (HCAI) approach offers a relevant framework for understanding citizen perceptions. However, only a few studies have focused on clarifying the citizen (...) perceptions of AI in the context of smart city research. To address this gap, we conducted a two-phased study. In the pre-study, we explored citizen perceptions and experiences of AI with a short survey (N = 91). Second, scenario-based interviews (N = 7) were utilized to gain in-depth insights of citizen perceptions of AI in the Smart City context. Five central themes were recognized: (1) I don’t like them monitoring me, (2) I want maximum gain for minimum effort, (3) I don’t want AI to mimic people, (4) I’ll avoid using AI if I consider the risk too high, and (5) I don’t need to be concerned about AI. These offer an idea of human-centered requirements worth considering while designing AI applications for future Smart Cities. (shrink)

AI companies are racing to create artificial general intelligence, or “AGI.” If they succeed, the result will be human-level AI systems that can independently pursue high-level goals by formulating and executing long-term plans in the real world. Leading AI researchers agree that some of these systems will likely be “misaligned”–pursuing goals that humans do not desire. This goal mismatch will put misaligned AIs and humans into strategic competition with one another. As with present-day strategic competition between nations with incompatible goals, (...) the result could be violent and catastrophic conflict. Existing legal institutions are unprepared for the AGI world. New foundations for AGI governance are needed, and the time to begin laying them is now, before the critical moment arrives. -/- This Article begins to lay those new legal foundations. It is the first to think systematically about the dynamics of strategic competition between humans and misaligned AGI. The Article begins by showing, using formal game-theoretic models, that, by default, humans and AIs will be trapped in a prisoner’s dilemma. Both parties’ dominant strategy will be to permanently disempower or destroy the other, even though the costs of such conflict would be high. -/- The Article then argues that a surprising legal intervention could transform the game theoretic equilibrium and avoid conflict: AI rights. Not just any AI rights would promote human safety. Granting AIs the right not to be needlessly harmed–as humans have granted to certain non-human animals–would, for example, have little effect. Instead, to promote human safety, AIs should be given those basic private law rights–to make contracts, hold property, and bring tort claims–that law already extends to non-human corporations. Granting AIs these economic rights would enable long-run, small-scale, mutually-beneficial transactions between humans and AIs. This would, we show, facilitate a peaceful strategic equilibrium between humans and AIs for the same reasons economic interdependence tends to promote peace in international relations. Namely, the gains from trade far exceed those from war. Throughout, we argue that human safety, rather than AI welfare, provides the right framework for developing AI rights. This Article explores both the promise and the limits of AI rights as a legal tool for promoting human safety in an AGI world. (shrink)

Dr. Viktor Dörfler combines his background in developing and implementing AI with scholarly research on knowledge and cultivating talent to address misconceptions about AI. The Element explains what AI can and cannot do, carefully delineating facts from beliefs or wishful thinking. Filled with examples, this practical Element is thought-provoking. The purpose is to help CEOs figure out how to make the best use of AI, suggesting how to extract AI’s greatest value through appropriate task allocation between human experts and (...) AI. The author challenges the attribution of characteristics like understanding, thinking, and creativity to AI, supporting his argument with the ideas of the finest AI philosophers. He also discusses in depth one of the most sensitive AI-related topics: ethics. The readers are encouraged to make up their own minds about AI and draw their own conclusions rather than accepting opinions from people with vested interests or an agenda. (shrink)

Probability plays an essential role in many branches of AI, where it is typically assumed that we have a complete probability distribution when addressing a problem. But this is unrealistic for problems of real-world complexity. Statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(P/Q) (the probability of P given Q) and prob(P/R), but what we really want is prob(P/Q&R), and (...) we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(P/Q) and prob(P/R), it is consistent with the probability calculus for prob(P/Q&R) to have any value between 0 and 1. Is there any way to make a reasonable estimate of the value of prob(P/Q&R)? A related problem occurs when probability practitioners adopt undefended assumptions of statistical independence simply on the basis of not seeing any connection between two propositions. This is common practice, but its justification has eluded probability theorists, and researchers are typically apologetic about making such assumptions. Is there any way to defend the practice? This paper shows that on a certain conception of probability — nomic probability — there are principles of “probable probabilities” that license inferences of the above sort. These are principles telling us that although certain inferences from probabilities to probabilities are not deductively valid, nevertheless the second-order probability of their yielding correct results is 1. This makes it defeasibly reasonable to make the inferences. Thus I argue that it is defeasibly reasonable to assume statistical independence when we have no information to the contrary. And I show that there is a function Y(r,s:a) such that if prob(P/Q) = r, prob(P/R) = s, and prob(P/U) = a (where U is our background knowledge) then it is defeasibly reasonable to expect that prob(P/Q&R) = Y(r,s:a).. (shrink)

While debate is heating up regarding the development of AI and its perceived impacts on human society, policymaking is struggling to catch up with the demand to exercise some regulatory control over its rapid advancement. This paper aims to introduce the concept of entangled AI that emerged from participatory backcasting research with an AI expert panel. The concept of entanglement has been adapted from quantum physics to effectively capture the envisioned form of artificial intelligence in which a strong interconnectedness (...) between AI, humans, society, and nature is reflected. Entanglement assumes that AI should serve nature, social well-being, justice, and the resilience of this intertwined network simultaneously and promote a dynamic balance among these factors. This approach allows us to understand the pervasive role of this technology and the scope of human agency in its development. The study shows how such concepts seem to transcend the dominant discourses related to expectations, technological determinism, and humanism. An additional aim of this paper is to demonstrate how backcasting can contribute to generating useful understandings of the future of AI and fruitful insights for policymaking. (shrink)

Deep learning AI systems have proven a wide capacity to take over human-related activities such as car driving, medical diagnosing, or elderly care, often displaying behaviour with unpredictable consequences, including negative ones. This has raised the question whether highly autonomous AI may qualify as morally responsible agents. In this article, we develop a set of four conditions that an entity needs to meet in order to be ascribed moral responsibility, by drawing on Aristotelian ethics and contemporary philosophical research. (...) We encode these conditions and generate a flowchart that we call the Moral Responsibility Test. This test can be used as a tool both to evaluate whether an entity is a morally responsible agent and to inform human moral decision-making over the influencing variables of the context of action. We apply the test to the case of Artificial Moral Advisors and conclude that this form of AI cannot qualify as morally responsible agents. We further discuss the implications for the use of AMAs as moral enhancement and show that using AMAs to offload human responsibility is inadequate. We argue instead that AMAs could morally enhance users if they are interpreted as enablers for moral knowledge of the contextual variables surrounding human moral decision-making, with the implication that such a use might actually enlarge human moral responsibility. (shrink)

The widespread tendency, even within AI, to anthropomorphize machines makes it easier to convince us of their intelligence. How can any putative demonstration of intelligence in machines be trusted if the AI researcher readily succumbs to make-believe? This is (what I shall call) the forensic problem of anthropomorphism. I argue that the Turing test provides a solution. This paper illustrates the phenomenon of misplaced anthropomorphism and presents a new perspective on Turingʼs imitation game. It also examines the role of the (...) Turing test in relation to the current dispute between human-level AI and ‘mindless intelligence’. (shrink)

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.,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.,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.,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.,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.,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.,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)

Although the AI paradigm is useful for building knowledge-based systems for the applied natural sciences, there are dangers when it is extended into the domains of business, law and other social systems. It is misleading to treat knowledge as a commodity that can be separated from the context in which it is regularly used. Especially when it relates to social behaviour, knowledge should be treated as socially constructed, interpreted and maintained through its practical use in context. The meanings of terms (...) in a knowledge-base are assumed to be references to an objective reality whereas they are instruments for expressing values and exercising power. Expert systems that are not perspicuous to the expert community will lose their meanings and cease to contain genuine knowledge, as they will be divorced from the social processes essential for the maintenance of both meaning and knowledge. Perspicuity is usually sacrificed when knowledge is represented in a formalism, with the result that the original problem is compounded with a second problem of penetrating the representation language. Formalisms that make business and legal problems easier to understand are one essential research goal, not only in the quest for intelligent machines to replace intelligent human beings, but also in the wiser quest for computers to support collaborative work and other forms of social problem solving. (shrink)

AI has become the poster child for a certain kind of thinking which holds that some technologies can become objective, independent and emergent entities which can evolve beyond the control of their creators. This thinking is not new however. It is a product of certain philosophical ideas such as materialism, a common-sense world of objective and independent objects, a correspondence theory of truth, and so forth, which are centered around the pre-eminence of science, epistemology, and logical reasoning, among others, as (...) the supremely valid modes of engaging experience. This paper aims to critically examine the synthesis of the development program for AI to be found in the writings of computer scientist John McCarthy (1927–2011). McCarthy has been called one of the founding fathers of AI. First, some of the main themes which recur in his writings from as early as the mid nineteen fifties in the Dartmouth proposal, up to his late writings, are considered. A discussion of the goals that such a program implies, follows, in terms of society’s relation with AI based technology and the nature and purpose of AI itself. These implications are arguably related to social control and are also moral. Ultimately, McCarthy’s idea that AI is and can be built upon a paradigm of reasoning and logic so as to simulate a perfectly rational abstraction of human thinking is shown to be motivated by his view that AI systems should be controlled, as servants. This understanding of human thinking is questionable, however, given current research on human irrationality, as well as on the argumentative role of reasoning. And yet, the process of development toward the unachievable goal of controllable, rational, logical AI systems, has handily come to serve as a blind for increasing techno-corporate control of society, with techno-corporate interests displacing blame for devaluations and harms caused by their push to develop the technology to AI systems which they can conveniently ‘lose control of.’. (shrink)

Although related studies have examined the impact of different images of artificial intelligence products on consumer evaluation, exploring the impact on consumer purchase decisions from the perspective of algorithmic decision autonomy remains under-explored. Based on the self-determination theory, this research discusses the influence of the agent decision-making role played by different AI algorithmic decision autonomy on consumer purchase decisions. The results of the 3 studies indicate that algorithmic decision autonomy has an inverted U-shaped effect on consumer’s purchase decisions, (...) consumer’s self-efficacy mediates the relationship between algorithmic decision autonomy and purchase decisions, and consumer’s power distance moderates the relationship between algorithmic decision autonomy, self-efficacy, and purchase decisions. The research results can provide references for marketers, retailers, algorithm designers, and other parties to formulate algorithm marketing strategies, make AI algorithm decisions better serve consumers, and achieve value co-creation with consumers. (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)

Narratives of artificial intelligence frame public perceptions and expectations, and have a performative role, potentially leading to increased attention and resource allocation, acceptance of AI, or resistance to the technology. However, research on AI narratives frequently produces generalized and decontextualized accounts. This paper argues for closer examination of the specific processes that shape AI narratives in particular contexts. To explore this, nine AI-related exhibitions held in German museums from 2022 to 2023 were analyzed. The study draws on interviews (...) with curatorial teams and exhibition materials to investigate what narratives of AI museums are formed and how those relate to the dominant AI narratives described in previous research. It is shown that curators are critical of dominant AI narratives and aim to deconstruct common myths and present more nuanced portrayals of AI. Yet, they also face challenges in balancing this critical stance with the need for creating engaging, accessible exhibitions. The analysis reveals that while some curatorial strategies successfully counter AI stereotypes, others fail or inadvertently reproduce mainstream imaginaries. Curators discover that avoiding problematic understandings of AI in the way they had initially intended is challenging or even impossible. In other cases, when elements of mainstream AI discourse resurface in museum exhibits despite the critical stance of the curators, they remain unexamined, or are regarded as insignificant and inconsequential. (shrink)

Despite the increase in the research field of ethics in artificial intelligence, most efforts have focused on the debate about principles and guidelines for responsible AI, but not enough attention has been given to the “how” of applied ethics. This paper aims to advance the research exploring the gap between practice and principles in AI ethics by identifying how companies are applying those guidelines and principles in practice. Through a qualitative methodology based on 22 semi-structured interviews and two (...) focus groups, the goal of the current study is to understand how companies approach ethical issues related to AI systems. A structured analysis of the transcripts brought out many actual practices and findings, which are presented around the following main research topics: ethics and principles, privacy, explainability, and fairness. The interviewees also raised issues of accountability and governance. Finally, some recommendations are suggested such as developing specific sector regulations, fostering a data-driven organisational culture, considering the algorithm’s complete life cycle, developing and using a specific code of ethics, and providing specific training on ethical issues. Despite some obvious limitations, such as the type and number of companies interviewed, this work identifies real examples and direct priorities to advance the research exploring the gap between practice and principles in AI ethics, with a specific focus on Spanish companies. (shrink)

Sovereignty and strategic autonomy are felt to be at risk today, being threatened by the forces of rising international tensions, disruptive digital transformations and explosive growth of cybersecurity incidents. The combination of AI and cybersecurity is at the sharp edge of this development and raises many ethical questions and dilemmas. In this commentary, I analyse how we can understand the ethics of AI and cybersecurity in relation to sovereignty and strategic autonomy. The analysis is followed by policy recommendations, some of (...) which may appear to be controversial, such as the strategic use of ethics. I conclude with a reflection on underlying concepts as an invitation for further research. The goal is to inspire policy-makers, academics and business strategists in their work, and to be an input for public debate. (shrink)

According to a recent survey by the HR Research Institute, as the presence of artificial intelligence (AI) becomes increasingly common in the workplace, HR professionals are worried that the use of recruitment algorithms will lead to a “dehumanization” of the hiring process. Our main goals in this paper are threefold: i) to bring attention to this neglected issue, ii) to clarify what exactly this concern about dehumanization might amount to, and iii) to sketch an argument for why dehumanizing the (...) hiring process is ethically suspect. After distinguishing the use of the term “dehumanization” in this context (i.e., removing the human presence) from its more common meaning in the interdisciplinary field of dehumanization studies (i.e., conceiving of other humans as subhuman), we argue that the use of hiring algorithms may negatively impact the employee-employer relationship. We argue that there are good independent reasons to accept a substantive employee-employer relationship, as well as an applicant-employer relationship, both of which are consistent with a stakeholder theory of corporate obligations. We further argue that dehumanizing the hiring process may negatively impact these relationships because of the difference between the values of human recruiters and the values embedded in recruitment algorithms. Drawing on Nguyen’s (2021) critique of how Twitter “gamifies communication”, we argue that replacing human recruiters with algorithms imports artificial values into the hiring process. We close by briefly considering some ways to potentially mitigate the problems posed by recruitment algorithms, along with the possibility that some difficult trade-offs will need to be made. (shrink)

The integration of artificial intelligence (AI) into healthcare and research introduces sophisticated diagnostic and treatment capabilities but also raises significant ethical challenges from development to deployment and evaluation, requiring comprehensive ethical training and interdisciplinary collaboration to ensure the responsible use of AI technologies. The “CONNECT with AI”- Collaborative Opportunity to Navigate and Negotiate Ethical Challenges and Trials with Artificial Intelligence) workshop was a three-day event, engaging multi-institutional interdisciplinary and interprofessional participants (both industry professionals and academicians) from diverse fields such (...) as Medicine, Dentistry, Nursing, Engineering, Law, and Allied Health Sciences from India. There was equal distribution of participants from diverse fields. A mixed-method approach utilizing pre- and post-workshop assessments, confidence level surveys and thematic analyses of small group discussions and open-ended questions to evaluate the impact of the workshop on participants’ ethical understanding and decision-making skills regarding AI. Post-workshop evaluations indicated a significant improvement in both theoretical knowledge and practical application of AI ethics, with assessment scores rising markedly from pre-workshop levels (p < 0.0001). Confidence in handling ethical dilemmas related to AI saw substantial increases across all domains, particularly in ensuring patient privacy and data security (p < 0.0001). Qualitative feedback highlighted diverse perceptions and sector-specific concerns about AI, emphasizing the importance of customized ethical guidelines and the integration of AI into healthcare practices. The workshop effectively enhanced participants’ competence and confidence in addressing AI-related ethical issues, demonstrating the critical role of interprofessional education in fostering an ethical AI implementation framework. Continuous, interdisciplinary and interprofessional training is essential to navigate the evolving ethical landscape of AI in healthcare, ensuring safe, effective, and responsible technology use. (shrink)

Public sector institutions are increasingly investing resources in data collection and data analytics to provide better public services at lower cost, to anticipate demand for services, to identify high-risk groups, and to develop targeted interventions. Prior research has shown that the media shape understanding of the possibilities of technology and creates related expectations. In this article we explore how artificial intelligence and emerging data-driven technologies are made familiar and by whose voices they are talked about in the media. (...) Empirically, we explore communication materials and media coverage from the joint AI experiment between the City of Espoo and Tieto Corporation in 2017–18. In this experiment, Tieto Corporation combined customer data from the City of Espoo’s social and health care and early childhood education from 2002–2016 and analysed this data using artificial intelligence methods. Theoretically, we draw on the tradition of the social construction of technology and especially its branch that focuses on the domestication of technologies. The results show that the means of narration used by the media – repetition and highlighting the voice of the representatives of the institutional actors involved in the AI experiment – strengthen the agency and interests of the parties involved as well as their visibility in the media, while reducing the space for possible critical discussion. We argue that this way, AI is made familiar and acceptable to citizens and in public services in Finland. (shrink)

This open access book tackles the pressing problem of integrating concerns related to Artificial Intelligence (AI) ethics into higher education curriculums aimed at future AI developers in Africa and beyond. For doing so, it analyzes the present and future states of AI ethics education in local computer science and engineering programs. The authors share relevant best practices and use cases for teaching, develop answers to ongoing organizational challenges, and reflect on the practical implications of different theoretical approaches to AI (...) ethics. The book is of great interest to faculty members, researchers, and students in the fields of artificial intelligence, computer science, mathematics, computer engineering, and related areas, as well as higher education administration. (shrink)

The popularisation of Artificial Intelligence (AI) technologies has sparked discussion about their ethical implications. This development has forced governmental organisations, NGOs, and private companies to react and draft ethics guidelines for future development of ethical AI systems. Whereas many ethics guidelines address values familiar to ethicists, they seem to lack in ethical justifications. Furthermore, most tend to neglect the impact of AI on democracy, governance, and public deliberation. Existing research suggest, however, that AI can threaten key elements of western (...) democracies that are ethically relevant. In this paper, Rawls’s theory of justice is applied to draft a set of guidelines for organisations and policy-makers to guide AI development towards a more ethical direction. The goal is to contribute to the broadening of the discussion on AI ethics by exploring the possibility of constructing AI ethics guidelines that are philosophically justified and take a broader perspective of societal justice. The paper discusses how Rawls’s theory of justice as fairness and its key concepts relate to the ongoing developments in AI ethics and gives a proposition of how principles that offer a foundation for operationalising AI ethics in practice could look like if aligned with Rawls’s theory of justice as fairness. (shrink)

Today, open source intelligence (OSINT), i.e., information derived from publicly available sources, makes up between 80 and 90 percent of all intelligence activities carried out by Law Enforcement Agencies (LEAs) and intelligence services in the West. Developments in data mining, machine learning, visual forensics and, most importantly, the growing computing power available for commercial use, have enabled OSINT practitioners to speed up, and sometimes even automate, intelligence collection and analysis, obtaining more accurate results more quickly. As the infosphere expands to (...) accommodate ever-increasing online presence, so does the pool of actionable OSINT. These developments raise important concerns in terms of governance, ethical, legal, and social implications (GELSI). New and crucial oversight concerns emerge alongside standard privacy concerns, as some of the more advanced data analysis tools require little to no supervision. This article offers a systematic review of the relevant literature. It analyzes 571 publications to assess the current state of the literature on the use of AI-powered OSINT (and the development of OSINT software) as it relates to the GELSI framework, highlighting potential gaps and suggesting new research directions. (shrink)