Background Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions (...) that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. Methods This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. Results There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. Conclusions Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice. (shrink)

Healthcare data warehousing test automation is becoming a success through the help of AI-based technology that results in accuracy, efficiency, and data integrity on the automated test. The one central to personalized patient data that forms the core of traditional data warehousing solutions frequently faces problems of complexity and dissatisfaction. Deep learning with test automation solutions makes data ingestion, processing, and testing conversant through machine learning algorithms [1]. These systems encompass separate acts such as testing for data integration, testing the (...) quality of data as well as performance testing removing the factors of time and human error. In healthcare organizations, this approach delivers the message of enhancing quality, compliance and organizational efficiency in value-based care and precision medicine initiatives. As such, artificial intelligence tools can be used to address the essential issues regarding data warehousing, and this can in turn enhance decision-making, and thus, enhance the health of patient and the overall population. (shrink)

The use of Artificial Intelligence (AI) in educational gamification marks a significant advancement, transforming traditional learning methods by offering interactive, adaptive, and personalized content. This approach makes historical content more relatable and promotes active learning and exploration. This research presents an innovative approach to heritage education, combining AI and gamification, explicitly targeting the Silk Roads. It represents a significant progression in a series of research, transitioning from basic 2D textual interactions to a 3D environment using photogrammetry, combining historical authenticity and (...) immersive gameplay. It features AI-driven characters developed in Unreal Engine, which provides heritage insights augmented by personalized interactions, highlighting the importance of collaborative tools like interactive quizzes for enriching education and promoting critical thinking and community among learners. In conclusion, this research underlines the transformative impact of AI and gamification in heritage education enhancement, integrating real-time voice conversation and interactive pedagogical methods into immersive 3D environments as a supplementary tool. (shrink)

The aim of this paper is to find how much AI can be useful or as an aid in developing listening skill. A review has been made to past studies as well as the recent developments and innovations made by linguists and teaching community, yielding relevant observations. The present writing is an attempt at exhibiting how much the latest technology is effective which has been tailor-made to suit determined purpose directed at developing the listening skills of EFL learners. It is (...) well established being significant and continues to gain importance in serving needs of education, sparking interest among students and teachers, the latter for innovating delivery of lessons. Teaching EFL college level students uses English as the medium of instruction for language teaching subjects. It is, however, noticed that difficulties crop up at school, where the students use native language for learning syllabi-related subjects, and where it is noticed that there is low comprehension capacity in the learners that needs to be pushed up to learn second language with success. The present research effort has brought to light the difficulties experienced by students in English learning as they struggle to reach a reasonable level of learning with success. This is despite there being other materials to use where old/traditional system is otherwise thought adequate. Modern approaches were brought into use by which AI-powered tools have removed difficulties they used to experience learning the traditional way resorting to written prescribed books; they experienced that books were monotonous and boring. The researchers made quite a few studies in different educational institutions and recognized the ability of AI aided media to make listening skill learning and teaching of English interesting and easy. AI has enabled educators to develop material and make it user-friendly and student-oriented for facilitating language learning easy. Language Listening builds enhanced capacity enabling EFL students to hear sounds of words of English using AI-aided tools. The present paper has discovered the utility of modern virtual tools as significant in facilitating learners’ success in developed listening skill. Tools which are created using AI have proved very help as they extend tremendous help in increasing and developing practical listening skills of not only the teachers but also, and primarily, the EFL students. (shrink)

Advancements and deployments of AI-based systems, especially Deep Learning-driven generative language models, have accomplished impressive results over the past few years. Nevertheless, these remarkable achievements are intertwined with a related fear that such technologies might lead to a general relinquishing of our lives’s control to AIs. This concern, which also motivates the increasing interest in the eXplainable Artificial Intelligence (XAI) research field, is mostly caused by the opacity of the output of deep learning systems and the way that it (...) is generated, which is largely obscure to laypeople. A dialectical interaction with such systems may enhance the users’ understanding and build a more robust trust towards AI. Commonly employed as specific formalisms for modelling intra-agent communications, dialogue games prove to be useful tools to rely upon when dealing with user’s explanation needs. The literature already offers some dialectical protocols that expressly handle explanations and their delivery. This paper fully formalises the novel Explanation–Question–Response (EQR) dialogue and its properties, whose main purpose is to provide satisfactory information (i.e., justified according to argumentative semantics) whilst ensuring a simplified protocol, in comparison with other existing approaches, for humans and artificial agents. (shrink)

Artificial intelligence and automation have become pervasive in news media, influencing journalism from news gathering to news distribution. As algorithms are increasingly determining editorial decisions, specific concerns have been raised with regard to the responsible and accountable use of AI-driven tools by news media, encompassing new regulatory and ethical questions. This contribution aims to analyze whether and to what extent the use of AI technology in news media and journalism is currently regulated and debated within the European Union and (...) the Council of Europe. Through a document analysis of official policy documents, combined with a data mining approach and an inductive thematic analysis, the study looks at how news media are dealt with, in particular regarding their responsibilities towards their users and society. The findings show that regulatory frameworks about AI rarely include media, but if they do, they associate them with issues such as disinformation, data, and AI literacy, as well as diversity, plurality, and social responsibility. (shrink)

The integration of artificial intelligence into bioethics education represents a new pedagogical approach that addresses complex moral issues in healthcare. The use of AI-driven platforms like ChatGPT in bioethics education can enhance critical thinking and decision-making skills among students by providing a diverse range of perspectives and solutions. To assess the ability of ChatGPT-4 to understand and resolve ethical dilemmas using validated clinical case vignettes, thereby determining its suitability as a teaching aid in bioethics. Ten clinical scenarios, each with (...) inherent ethical issues, were presented to ChatGPT-4. These scenarios were expert-validated to ensure relevance and complexity. ChatGPT's responses were evaluated by two blinded external raters to eliminate bias. The assessment criteria included identification of bioethical principles, recognition of stakeholders and their outcomes, and comprehension of ethical and legal frameworks. Additionally, the quality of responses was scored on a scale of 1–10. ChatGPT showed proficiency in identifying the pertinent bioethical principles and stakeholders involved in the ethical dilemmas presented. It achieved an overall score of 84.5%, with high marks for its analytical capabilities in complex scenarios. However, the study identified areas for improvement, particularly in proposing comprehensive solutions and alternative actions. ChatGPT demonstrates potential as an effective tool for bioethics education, capable of guiding learners through the resolution of ethical issues in clinical practice. However, to fully integrate ChatGPT into bioethics education, it requires enhancements in delivering detailed, actionable guidance in ethical decision-making. Future development should focus on refining ChatGPT’s ability to offer nuanced solutions aligned with patient values and clinician needs. (shrink)

AI-driven journalism refers to various methods and tools for gathering, verifying, producing, and distributing news information. Their potential is to extend human capabilities and create new forms of augmented journalism. Although scholars agreed on the necessity to embed journalistic values in these systems to make AI systems accountable, less attention was paid to data quality, while the results’ accuracy and efficiency depend on high-quality data in any machine learning task. Assessing data quality in the context of AI-driven journalism (...) requires a broader and interdisciplinary approach, relying on the challenges of data quality in machine learning and the ethical challenges of using machine learning in journalism. To better identify these, we propose a data quality assessment framework to support the collection and pre-processing stages in machine learning. It relies on three of the core principles of ethical journalism—accuracy, fairness, and transparency—and participates in the shift from model-centric to data-centric AI, by focusing on data quality to reduce reliance on large datasets with errors, making data labelling consistent, and better integrating journalistic knowledge. (shrink)

This study explores the capacity of Artificial Intelligence (AI) to revolutionize educational pedagogy, aiming to foster a more personalized and effective learning experience. Methodology: Through a bibliometric analysis of publications in Scopus and Web of Science, the research examines AI's impact on adaptive learning, personalized instruction, and effective teaching methods. It also evaluates AI's role in assessment, creation of simulated learning environments, and widening access to education, while addressing the integration challenges. Conclusions: The investigation demonstrates that AI has considerable potential (...) to improve teaching and learning. AI-driven tools like intelligent tutoring systems, machine learning algorithms, and virtual simulations can facilitate a learner-centric approach. Nonetheless, it also highlights the necessity to confront issues such as technological overdependence, privacy concerns, and the potential displacement of teachers. A thoughtful adoption of AI in education could lead to a more equitable and engaging learning system. The advent of this educational revolution demands a focus on augmenting human capabilities with AI and transforming pedagogical strategies to enhance student outcomes. The research advocates for further development of ethical standards and informed policies to guide AI integration in the educational sector. (shrink)

Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework—a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI (...) agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI-specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future. (shrink)

Artificial Intelligence (AI) and machine learning (ML) are rapidly evolving and have already had major impacts on military capabilities in the battlefield, making new kinds of tools and tactics available. A less examined area of application for AI in a military context, however, is its impact on human strategic decision making. This article focuses on the more subtle cognitive influences of AI and how they can be strategically deployed to aid decision making around the state-level resort to force, in particular. (...) I will argue that AI-driven technologies can be used to improve certain critical cognitive resources (e.g. memory, planning, mind-modelling, etc.) of decision makers, thereby providing valuable strategic advantages to those actors who use them successfully. At the same time, I will also caution against the risks of human decision makers becoming overly reliant on AI-support systems. Both the potential advantages and risks are areas that demand further study and consideration. (shrink)

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionary tools that have opened up a new technology-driven horizon in academic learning to enhance and accelerate the higher education journey. This article describes and discusses the convergence of academic integrity and AI, both of which are fundamental to our intellectual environment. An effective approach to AI-integrated learning can balance the use of AI-generated tools and techniques to stimulate students' creativity, analytical thinking, and real-life observation. As we strive for academic excellence, (...) it is essential to integrate AI in academics and all other spheres of development with balance and rationality, through appropriate policies and regulated ethical and value-based use. Educational institutes should have many open platforms to discuss, debate, and smoothly integrate AI technology into academic practices while maintaining the integrity that underpins our scholarly pursuits. Investing in academic and policy debates and discussions regarding the ethicality of AI applications can create a brighter future for academia. This will allow us to leverage the power of AI to advance our knowledge and understanding and to build a stronger nation with greater impact. (shrink)

The rapid digital transformation driven by Artificial Intelligence (AI) is reshaping innovation and operational efficiency across industries. As autonomous AI systems are becoming prevalent, they significantly influence traditional business models, societal norms, and legal frameworks. AI technologies are evolving beyond mere tools to become independent economic agents capable of generating assets, making decisions, commercializing products and services, and being accountable for their actions. This evolution requires a reassessment of traditional concepts of corporate and moral personhood, particularly as AI-driven (...) businesses need to operate inside conventional legal frameworks. This paper explores the rise of entirely AI-driven entities with fully autonomous decision-making processes, advocating for the legal status of artificial personhood. It underscores the need for an ethical and regulatory framework to ensure these entities will proliferate and operate for human progress with integrity and responsibility. (shrink)

As artificial intelligence (AI) continues to reshape our world, the spectre of technological domination looms large. This paper delves into the equilibrium model of balancing as a legal safeguard against AI-driven power imbalances. First, the study unveils the sources of domination: control over resources and events. Subsequently, potential legal tools of counterbalancing are identified and discussed. Employing a proactive, theory-building approach, the research synthesises legal rules, case studies, and scholarly insights to construct a framework for understanding and implementing balancing (...) in AI contexts. The paper’s findings offer valuable insights for policymakers, legal scholars, and AI developers seeking to navigate the complex landscape of power relations in the age of AI. (shrink)

This research delves into the potential of implementing artificial intelligence in architecture. It specifically provides a critical assessment of AI-enabled workflows, encompassing creative ideation, representation, materiality, and critical thinking, facilitated by prompt-based generative processes. In this context, the paper provides an examination of the concept of hybrid human–machine intelligence. In an era characterized by pervasive data bias and engineered injustices, the concept of hybrid intelligence emerges as a critical tool, enabling the transcendence of preconceived stereotypes, clichés, and linguistic prejudices. This (...) paper not only explores the applied and generative capacities of AI-enabled workflows but also suggests fundamental approaches that can enhance the creative process and confront the embedded biases and injustices within data-driven systems. (shrink)

In this chapter, I introduce three different types of AI-based moral enhancement proposals discussed in the literature – substitutive enhancement, value-driven enhancement, and value-open moral enhancement. I analyse them based on the following criteria: effectiveness, examining whether they bring about tangible moral changes; autonomy, assessing whether they infringe on human autonomy and agency; and developmental impact, considering whether they hinder the development of natural moral skills. This analysis demonstrates that no single approach to AI enhancement can satisfy all proposed (...) criteria, suggesting a need for pluralism in devising AI-based enhancement tools. The most advisable approach to AI-based enhancement is to explore and develop a variety of tools that cater to the diverse needs of users and address different weaknesses in moral decision-making. (shrink)

In this paper, we argue that one way to approach what is known in the literature as the “Trust Gap” in Medical AI is to focus on explanations from an Explainable AI (xAI) perspective. Against the current framework on xAI – which does not offer a real solution – we argue for a pragmatist turn, one that focuses on understanding how we provide explanations in Traditional Medicine (TM), composed by human agents only. Following this, explanations have two specific relevant components: (...) they are usually (i) social and (ii) abductive. Explanations, in this sense, ought to provide understanding by answering contrastive _why-questions_: “Why had P happened instead of Q?” (Miller in AI 267:1–38, 2019) (Sect. 1 ). In order to test the relevancy of this concept of explanation in medical xAI, we offer several reasons to argue that abductions are crucial for medical reasoning and provide a crucial tool to deal with trust gaps between human agents (Sect. 2 ). If abductions are relevant in TM, we can test the capability of Artificial Intelligence systems on this merit. Therefore, we provide an analysis of the capacity for social and abductive reasoning of different AI technologies. Accordingly, we posit that Large Language Models (LLMs) and transformer architectures exhibit a noteworthy potential for effective engagement in abductive reasoning. By leveraging the potential abductive capabilities of LLMs and transformers, we anticipate a paradigm shift in the integration of explanations within AI systems. This, in turn, has the potential to enhance the trustworthiness of AI-driven medical decisions, bridging the Trust Gap that has been a prominent challenge in the field of Medical AI (Sect. 3 ). This development holds the potential to not only improve the interpretability of AI-generated medical insights but also to guarantee that trust among practitioners, patients, and stakeholders in the healthcare domain is still present. (shrink)

Background Big data and AI applications now play a major role in many health contexts. Much research has already been conducted on ethical and social challenges associated with these technologies. Likewise, there are already some studies that investigate empirically which values and attitudes play a role in connection with their design and implementation. What is still in its infancy, however, is the comparative investigation of the perspectives of different stakeholders. Methods To explore this issue in a multi-faceted manner, we conducted (...) semi-structured interviews as well as focus group discussions with patients and clinicians. These empirical methods were used to gather interviewee’s views on the opportunities and challenges of medical AI and other data-intensive applications. Results Different clinician and patient groups are exposed to medical AI to differing degrees. Interviewees expect and demand that the purposes of data processing accord with patient preferences, and that data are put to effective use to generate social value. One central result is the shared tendency of clinicians and patients to maintain individualistic ascriptions of responsibility for clinical outcomes. Conclusions Medical AI and the proliferation of data with import for health-related inferences shape and partially reconfigure stakeholder expectations of how these technologies relate to the decision-making of human agents. Intuitions about individual responsibility for clinical outcomes could eventually be disrupted by the increasing sophistication of data-intensive and AI-driven clinical tools. Besides individual responsibility, systemic governance will be key to promote alignment with stakeholder expectations in AI-driven and data-intensive health settings. (shrink)

This study examines the role of Artificial Intelligence (AI) in enhancing English vocabulary acquisition among university students at Al-Hussein Bin Talal University in Jordan. A quasi-experimental design was employed, involving 40 EFL students, evenly split by gender (20 males and 20 females), all of whom were enrolled in the Basic English Language Course. The students were randomly assigned to either an experimental group, which utilized the Duolingo app for vocabulary learning, or a control group, which followed traditional methods of vocabulary (...) instruction. The intervention was conducted during the first semester of the 2023/2024 academic year, and the results were analyzed using SPSS. Analytical methods included (MANOVA), T-tests, and (ANCOVA), which helped assess the impact of AI-driven tools compared to conventional approaches. The findings revealed that students using Duolingo showed significant improvement in vocabulary acquisition compared to the control group. Furthermore, no significant differences in learning outcomes were observed between male and female participants, suggesting that AI-driven tools are equally effective for all learners. The study concludes by recommending the integration of AI-based platforms like Duolingo into EFL curricula and calls for further research to explore their long-term effects on various aspects of language learning. (shrink)

The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show (...) that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%. (shrink)

This paper interrogates the growing pervasiveness of affect recognition tools as an emerging layer human-centric automated management in the global workplace. While vendors tout the neoliberal incentives of emotion-recognition technology as a pre-eminent tool of workplace wellness, we argue that emotional AI recalibrates the horizons of capital not by expanding outward into the consumer realm (like surveillance capitalism). Rather, as a new genus of digital Taylorism, it turns inward, passing through the corporeal exterior to extract greater surplus value and managerial (...) control from the affective states of workers. Thus, empathic surveillance signals a profound shift in the ontology of human labor relations. In the emotionally quantified workplace, employees are no longer simply seen as physical capital, but conduits of actuarial and statistical intelligence gleaned from their most intimate subjective states. As a result, affect-driven automated management means that priority is often given to actuarial rather than human-centered managerial decisions. (shrink)

Biometric technologies are becoming more pervasive in the workplace, augmenting managerial processes such as hiring, monitoring and terminating employees. Until recently, these devices consisted mainly of GPS tools that track location, software that scrutinizes browser activity and keyboard strokes, and heat/motion sensors that monitor workstation presence. Today, however, a new generation of biometric devices has emerged that can sense, read, monitor and evaluate the affective state of a worker. More popularly known by its commercial moniker, Emotional AI, the technology stems (...) from advancements in affective computing. But whereas previous generations of biometric monitoring targeted the exterior physical body of the worker, concurrent with the writings of Foucault and Hardt, we argue that emotion-recognition tools signal a far more invasive disciplinary gaze that exposes and makes vulnerable the inner regions of the worker-self. Our paper explores attitudes towards empathic surveillance by analyzing a survey of 1015 responses of future job-seekers from 48 countries with Bayesian statistics. Our findings reveal affect tools, left unregulated in the workplace, may lead to heightened stress and anxiety among disadvantaged ethnicities, gender and income class. We also discuss a stark cross-cultural discrepancy whereby East Asians, compared to Western subjects, are more likely to profess a trusting attitude toward EAI-enabled automated management. While this emerging technology is driven by neoliberal incentives to optimize the worksite and increase productivity, ultimately, empathic surveillance may create more problems in terms of algorithmic bias, opaque decisionism, and the erosion of employment relations. Thus, this paper nuances and extends emerging literature on emotion-sensing technologies in the workplace, particularly through its highly original cross-cultural study. (shrink)

The rapid integration of these artificial intelligence (AI) tools into our daily lives is reshaping how we think and make decisions. We propose that data-driven AI systems, by transcending individual artefacts and interfacing with a dynamic, multiartefact ecosystem, constitute a distinct psychological system. We call this ‘system 0’, and position it alongside Kahneman’s system 1 (fast, intuitive thinking) and system 2 (slow, analytical thinking).System 0 represents the outsourcing of certain cognitive tasks to AI, which can process vast amounts of (...) data and perform complex computations beyond human capabilities. It emerges from the interaction between users and AI systems, which creates a dynamic, personalized interface. Authors: Massimo Chiriatti, Marianna Ganapini, Enrico Panai, Mario Ubiali & Giuseppe Riva. (shrink)

Public administrations, including national government agencies as well as the EU institutions, face not only structural changes, but are increasingly talking about the need to transform management, foster innovation, and share responsibilities. The role of AI is the key composite element of this process. However, not only from the technological point of view. AI also needs the human “perspective”. It is very important to include the so-called soft components (institutional culture, inter-cultural awareness, and organizational needs) to facilitate the mental shift (...) and thus the buy-in. Technological advances in the area of AI inevitably result in further change and this alteration has to be managed. A need to break silo mentality moves organizations towards matrix-based structures with advanced reporting relationships. With the focus on the people side of change and the boundaries of the data-driven decision-making, this study provides insights in the correlations between AI as a tool and change process in public administrations. (shrink)

This study addresses the current lack of empirical data on the experiences and attitudes of clinical research professionals towards AI-powered clinical trial management tools. Clinical research professionals affiliated with various Swiss and international clinical research networks were invited to participate in an online survey. The survey focused on nine use cases of AI-powered clinical trial management tools. Participants were asked to share their ethical considerations, and their experiences were assessed at both the individual and institutional levels. Answers from 110 participants, (...) primarily from Swiss academic institutions, were included in the data analysis. While participants generally held positive attitudes, mixed views were also common. Governmental attitudes were widely perceived as unclear or cautious, with the associated regulatory opacity possibly contributing to the limited adoption of AI in trial management observed in this study. In addition to a clear AI governance, ethical compliance and training emerged as crucial aspects. There was also broad consensus on the necessity of informing patients about tools’ uses and of ensuring an independent certification of the respective tools. The widespread adoption of AI-powered clinical trial management tools remains distant, entangled in a net of complexities that require further empirical investigation. Ethical concerns appear to play a crucial role, driven by distrust and the absence of a clear regulatory framework. Urgent actions are needed at both institutional and governmental levels, including the establishment of a governance framework and training provision. (shrink)

Email archives are important historical resources, but access to such data poses a unique archival challenge and many born-digital collections remain dark, while questions of how they should be effectively made available remain. This paper contributes to the growing interest in preserving access to email by addressing the needs of users, in readiness for when such collections become more widely available. We argue that for the content of email to be meaningfully accessed, the context of email must form part of (...) this access. In exploring this idea, we focus on discovery within large, multi-custodian archives of organisational email, where emails’ network features are particularly apparent. We introduce our prototype search tool, which uses AI-based methods to support user-driven exploration of email. Specifically, we integrate two distinct AI models that generate systematically different types of results, one based upon simple, phrase-matching and the other upon more complex, BERT embeddings. Together, these provide a new pathway to contextual discovery that accounts for the diversity of future archival users, their interests and level of experience. (shrink)

Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely studied and critically evaluated, often with an emphasis on ethical concerns. In this article, I analyze three canonical unsupervised learning problems: clustering, abstraction, and generative modeling. I argue that these methods raise unique epistemological and (...) ontological questions, providing data-driven tools for discovering natural kinds and distinguishing essence from contingency. This analysis goes some way toward filling the lacuna in contemporary philosophical discourse on unsupervised learning, as well as bringing conceptual unity to a heterogeneous field more often described by what it isnot(i.e., supervised or reinforcement learning) than by what itis. I submit that unsupervised learning is not just a legitimate subject of philosophical inquiry but perhaps the most fundamental branch of all AI. However, an uncritical overreliance on unsupervised methods poses major epistemic and ethical risks. I conclude by advocating for a pragmatic, error-statistical approach that embraces the opportunities and mitigates the challenges posed by this powerful class of algorithms. (shrink)

From his earliest published writings to his last, Roman Ingarden displayed an interest in theoretical biology and its efforts to clarify what distinguishes living organisms from other types of entities. However, many of his explorations of such issues are easily overlooked, because they don’t appear in works that are primarily ontological, metaphysical, or anthropological in nature but are “hidden” within his works on literary aesthetics, where Ingarden sought to define the nature of living organisms in order to compare literary works (...) to such entities. This article undertakes a historical textual analysis that traces the evolution of Ingarden’s thought regarding the nature of the literary work of art as an organism-like entity and uncovers its links with the simultaneous development of his systems theory and its central concept of the “relatively isolated system”: for Ingarden, a literary work and an organism are each a systematically transforming, “living,” functional-structural whole that comprises a system of hierarchically arranged and partially isolated elements whose harmonious interaction allows the literary work or organism to fulfill its chief function. Having completed that historical analysis, we test Ingarden’s assessment of works of art as organism-like entities in a novel context by investigating the organism-like qualities of the contemporary computer game; insofar as their AI-driven behavior displays a form of agency, such games might appear to be even more “alive” than traditional works of art. We show that Ingarden’s conceptual framework provides a useful tool for understanding the “organicity” of such games as works of art, despite the fact that they differ qualitatively from those art forms with which Ingarden was directly familiar. (shrink)

The article explores the transformative impact on human and social sciences in response to anticipated societal shifts driven by the forthcoming proliferation of artificial systems, whose intelligence will match human capabilities. Initially, it was posited that artificial intelligence (AI) would excel beyond human abilities in computational tasks and algorithmic operations, leaving creativity and humanities as uniquely human domains. However, recent advancements in large language models have significantly challenged these conventional beliefs about AI’s limitations and strengths. It is projected that, (...) in the near future, generative AI models will adeptly replicate individual qualities, desires, beliefs, opinions, and the essence of human identity and consciousness to a degree that is nearly indistinguishable from that of humans. This lends support to the connectionist approach to understanding consciousness, suggesting an inherent similarity between biological and artificial neural networks. The discussion posits two innovative areas of scientific inquiry: “mathematical anthropology” and “multi-dimensional calculus of value.” The former suggests that viewing human nature through a mathematical and calculative lens not only preserves but enriches our understanding of the complexity of anthropological experience and its perceived contradictions. The latter hypothesis explores how mathematical models could facilitate various social interactions. The advanced simulation capabilities of neural networks suggest that traditional social and political frameworks face a growing vulnerability to AI-driven manipulations. This trend underscores the urgency of developing social interaction models that incorporate explicitly defined calculative rules. In conclusion, the paper advocates for a paradigm shift in how mathematics is perceived – not merely as a tool for computation but as a foundational science for crafting sophisticated models. In conclusion, the article advocates for transitioning from perceiving mathematics solely as a science of computation to viewing it as a discipline dedicated to constructing various formalized models, thereby deepening our insight into the complexities of human and social phenomena. (shrink)

The rapid adoption of artificial intelligence (AI) chatbots in academic peer review (PR) has sparked both excitement and concern, raising critical questions about the future of scientific integrity. This paper examined how AI tools, particularly Chat Generative Pre-Trained Transformer, are reshaping scientific PR. As these tools become more prevalent in academic evaluation, they bring both opportunities and challenges to scholarly communication. AI assistance offers valuable benefits: it can speed up review processes, help non-native English speakers express their ideas clearly, and (...) improve overall text readability. However, our research revealed growing concerns about whether AI-assisted reviews can maintain the depth and authenticity that quality PR demands. We explored how the scientific community can balance these technological capabilities with the need for thorough, expert-driven evaluation. The potential for AI to introduce bias, overlook novel contributions, and promote uniformity in feedback threatens the nuanced insights traditionally offered by human experts. In this review we critically evaluated the ethical implications of AI use in PR, focusing on three main issues: (i) The risks associated with over-reliance on AI tools by reviewers, including diminished engagement and critical thinking; (ii) The need for transparency and disclosure when AI tools assist in review generation; and (iii) The creation of ethical guidelines to balance AI’s capabilities with human expertise. With AI’s increasing role in academia, the academic community shall address these ethical challenges by establishing robust policies that ensure AI complements rather than replacing human judgment. We call for immediate action to develop clear guidelines governing AI’s role in academic evaluation, promoting a transparent and responsible use of AI that upholds the integrity of PR, which is essential for the advancement of scientific knowledge. (shrink)

This paper introduces the concept of Operator Contestability in AI systems: the principle that those overseeing AI systems (operators) must have the necessary control to be accountable for the decisions made by these algorithms. We argue that designers have a duty to ensure operator contestability. We demonstrate how this duty can be fulfilled by applying the'Design for Defeaters' framework, which provides strategies to embed tools within AI systems that enable operators to challenge decisions. Defeaters are designed to contest either the (...) justification for the AI’s data inputs (undercutting defeaters) or the validity of the conclusions drawn from that data (rebutting defeaters). To illustrate the necessity and application of this framework, we examine case studies such as AI-driven recruitment processes, where operators need tools and authority to uncover and address potential biases, and autonomous driving systems, where real-time decision-making is crucial. The paper argues that operator contestability requires ensuring that operators have (1) epistemic access to the relevant normative reasons and (2) the authority and cognitive capacity to act on these defeaters. By addressing these challenges, the paper emphasizes the importance of designing AI systems in a way that enables operators to effectively contest AI decisions, thereby ensuring that the appropriate individuals can take responsibility for the outcomes of human-AI interactions. (shrink)

Background Artificial intelligence (AI) has revolutionized various healthcare domains, where AI algorithms sometimes even outperform human specialists. However, the field of clinical ethics has remained largely untouched by AI advances. This study explores the attitudes of anesthesiologists and internists towards the use of AI-driven preference prediction tools to support ethical decision-making for incapacitated patients. Methods A questionnaire was developed and pretested among medical students. The questionnaire was distributed to 200 German anesthesiologists and 200 German internists, thereby focusing on physicians (...) who often encounter patients lacking decision-making capacity. The questionnaire covered attitudes toward AI-driven preference prediction, availability and utilization of Clinical Ethics Support Services (CESS), and experiences with ethically challenging situations. Descriptive statistics and bivariate analysis was performed. Qualitative responses were analyzed using content analysis in a mixed inductive-deductive approach. Results Participants were predominantly male (69.3%), with ages ranging from 27 to 77. Most worked in nonacademic hospitals (82%). Physicians generally showed hesitance toward AI-driven preference prediction, citing concerns about the loss of individuality and humanity, lack of explicability in AI results, and doubts about AI’s ability to encompass the ethical deliberation process. In contrast, physicians had a more positive opinion of CESS. Availability of CESS varied, with 81.8% of participants reporting access. Among those without access, 91.8% expressed a desire for CESS. Physicians' reluctance toward AI-driven preference prediction aligns with concerns about transparency, individuality, and human-machine interaction. While AI could enhance the accuracy of predictions and reduce surrogate burden, concerns about potential biases, de-humanisation, and lack of explicability persist. Conclusions German physicians frequently encountering incapacitated patients exhibit hesitance toward AI-driven preference prediction but hold a higher esteem for CESS. Addressing concerns about individuality, explicability, and human-machine roles may facilitate the acceptance of AI in clinical ethics. Further research into patient and surrogate perspectives is needed to ensure AI aligns with patient preferences and values in complex medical decisions. (shrink)

This essay describes trends in the organization of work that have laid the groundwork for the adoption of interactive AI-driven instruction tools, and the technological innovations that will make intelligent tutoring systems truly competitive with human teachers. Since the origin of occupational specialization, the collection and transmission of knowledge have been tied to individual careers and job roles, specifically doctors, teachers, clergy, and lawyers, the paradigmatic knowledge professionals. But these roles have also been tied to texts and organizations that (...) can disseminate knowledge independently from professionals. Professionals and organizations turn knowledge into texts and tools that enable lay people to access knowledge without the intermediation of professionals or organizations. In the 21st century, one emerging tool for transmitting knowledge is the intelligent tutoring system. This paper examines how technological, epistemic, and economic trends in education are supporting the routinization, proletarianization, and automation of the occupation of teaching, leading to the increasing substitution of intelligent tutoring systems for human instruction. Some trends, such as standardized curricula and testing, both restrict teachers’ professional autonomy and facilitate the creation of pedagogical tools. Other trends reduce teachers’ ability to resist automation. The growth of adjunct teaching and paraprofessional roles in higher education allows organizations to take over and rationalize parts of the traditional teacher role. Faculty evaluations and learning outcomes assessment weaken professional claims to be the sole arbiters of instructional quality and student learning. The widespread use of intelligent tutoring systems also depends on the sophistication of software capable of performing the social-emotional and cognitive roles that educators perform. Eventually, pedagogical software will be able to interactively individualize curricula to the needs and interests of every learner, more cheaply, quickly, and accurately than any human teacher. Assessment of learning will be continuous, and certification of learning will be for specific skills instead of broad area competencies. Intelligent tutoring systems will help transition education from its medieval and industrial-era model to more accessible and flexible continuing education for employment and life enrichment. (shrink)

This participatory study, conducted in an introductory psychology class, recounts self-reflections of 22 undergraduate students and their instructor engaging in an GenAI-mediated storyboard generation process. It relies on Gordon Pask’s conversation theory, structuring out the nature of interactions between students, instructor, and GenAI, and then uses a qualitative narrative to describe these conversational feedback loops constituting the creation of draft and final storyboards. Results suggest students engaged in cyclical feedback driven processes to master their creations, used elements of photography (...) related to the relationships between objects in frame, image processing, and cultural objects/themes to generate narratives. Issues were faced with generating text on images, and the consistency and style of generated frames as they interacted with GenAI tools. This study provides guidelines to university teaching professionals to design effective activities mediated by text-to-image GenAI. (shrink)

Purpose As data-driven tools increasingly shape our life and tech ethics crises become strikingly frequent, data ethics coursework is urgently needed. The purpose of this study is to map the field of data ethics curricula, tracking relations between courses, instructors, texts and writers, and present a proof-of-concept interactive website for exploring these relations. This method is designed to be used in curricular research and development and provides multiple vantage points on this multidisciplinary field. Design/methodology/approach The authors use data science (...) methods to foster insights about the field of data ethics education and literature. The authors present a semantic, linked open data graph in the Resource Description Framework, along with proof-of-concept analyses and an exploratory website. Its framework is open-source and language-agnostic, providing the seed for future contributions of code, syllabi and resources from the global data ethics community. Findings This method provides a convenient means of exploring an overview of the field of data ethics’ social and textual relations. For educators designing or refining a course, the authors provide a method for curricular introspection and discovery of transdisciplinary curricula. Research limitations/implications The syllabi the authors have collected are self-selected and represent only a subset of the field. Furthermore, this method exclusively represents a course’s assigned literature rather than a holistic view of what courses teach. The authors present a prototype rather than a finished product. Originality/value This curricular survey provides a new way of modeling a field of study, using existing ontologies to organize graph data into a comprehensible overview. This framework may be repurposed to map the institutional knowledge structures of other disciplines, as well. (shrink)

The rapid evolution of scientific research has created a pressing need for efficient and versatile tools to aid researchers. While using artificial intelligence (AI) to write scientific articles is unethical and unreliable due to the potential for inaccuracy, AI can be a valuable tool for assisting with other aspects of research, such as language editing, reference formatting, and journal finding. Two of the latest AI-driven assistants that have become indispensable assets to scientists are ChatGPT and Gemini (Bard). These assistants (...) offer comprehensive support from literature review to journal suggestion, and they have the potential to revolutionize research. In the present study, a comprehensive set of queries and responses were designed to assess the capabilities of ChatGPT-3.5 and Gemini as scientific assistants. The results showed that Gemini achieved a perfect score of 100%, while ChatGPT-3.5 scored a less impressive 70%. Notably, ChatGP-3.5 fell short in specific areas that includes providing assistance with scientific paper explanations, exploring bibliographic databases, and formatting references. The qualitative assessment of responses also suggests that both the AI chatbots can be valuable tools for researchers, however, Gemini seems to be more appealing and accurate through the whole procedure of scientific writing. This work shall open new research dimensions in identifying adequate scientific utilization of the evolving AI tools and technologies, as embracing these advancements will be essential for staying at the forefront of scientific research. (shrink)

Cultural heritage preservation, as the approach is known, and the enduring role it plays in safeguarding the culture and cultural values of Shanxi Opera is becoming even more important these days. Despite uncertainty and apprehension about utilising artificial intelligence, this matter is worth scrutiny in relation to the field of opera music. This may be so, as apart from these two occurrences, this field does not receive mention. This research aims to explore the effect of AI as a conduit for (...) message emulation between the Shanxi Opera tradition and the preservation of heritage and the opera music genre. The study employed a combination of qualitative and quantitative research methods, which used the survey data and cultural heritage preservation and opera music experts' perception data. I then employed partial least squares structural equation modelling (PLS-SEM) to interpret existing AI utilisation and to understand the role of AI as a mediator in this scenario. The outcome of the study implies that the existence of AI will not only boost the contribution of cultural heritage conservation but also positively impact the early stages of the contemporary revival of Shanxi Opera music. AI not only helps in heritage preservation but also makes it easier to include modern audiences. It introduces new musical trends and styles that are more engaging with the current audience. This paper lays out certain practical applications of artificial intelligence AI as a key to appreciating the human cultural heritage. It is the AI-driven contribution to opera music superiority that stands as the hallmark of technology. The outcomes showcase a one-of-a-kind perspective that indicates the ability of policymakers, cultural heritage preservers, and opera commuters to bridge the gap between traditional and modern technologies through AI. Research has stressed the essence of enlisting AI as a typical tool today to preserve cultural heritage. This, in turn, largely contributes to the development of dramatic genres such as opera. It serves as a rather remarkable point of interface between the cutting-edge realms of technology and cultural legacy, illustrating how AI can virtually reshape the artistic world. (shrink)

Background Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are being increasingly introduced into various domains of health care for diagnostic, prognostic, therapeutic and other purposes. A significant part of the discourse on ethically appropriate conditions relate to the levels of understanding and explicability needed for ensuring responsible clinical decision-making when using AI-CDSS. Empirical evidence on stakeholders’ viewpoints on these issues is scarce so far. The present study complements the empirical-ethical body of research by, on the one hand, investigating the (...) requirements for understanding and explicability in depth with regard to the rationale behind them. On the other hand, it surveys medical students at the end of their studies as stakeholders, of whom little data is available so far, but for whom AI-CDSS will be an important part of their medical practice. Methods Fifteen semi-structured qualitative interviews (each lasting an average of 56 min) were conducted with German medical students to investigate their perspectives and attitudes on the use of AI-CDSS. The problem-centred interviews draw on two hypothetical case vignettes of AI-CDSS employed in nephrology and surgery. Interviewees’ perceptions and convictions of their own clinical role and responsibilities in dealing with AI-CDSS were elicited as well as viewpoints on explicability as well as the necessary level of understanding and competencies needed on the clinicians’ side. The qualitative data were analysed according to key principles of qualitative content analysis (Kuckartz). Results In response to the central question about the necessary understanding of AI-CDSS tools and the emergence of their outputs as well as the reasons for the requirements placed on them, two types of argumentation could be differentiated inductively from the interviewees’ statements: the first type, the clinician as a systemic trustee (or “the one relying”), highlights that there needs to be empirical evidence and adequate approval processes that guarantee minimised harm and a clinical benefit from the employment of an AI-CDSS. Based on proof of these requirements, the use of an AI-CDSS would be appropriate, as according to “the one relying”, clinicians should choose those measures that statistically cause the least harm. The second type, the clinician as an individual expert (or “the one controlling”), sets higher prerequisites that go beyond ensuring empirical evidence and adequate approval processes. These higher prerequisites relate to the clinician’s necessary level of competence and understanding of how a specific AI-CDSS works and how to use it properly in order to evaluate its outputs and to mitigate potential risks for the individual patient. Both types are unified in their high esteem of evidence-based clinical practice and the need to communicate with the patient on the use of medical AI. However, the interviewees’ different conceptions of the clinician’s role and responsibilities cause them to have different requirements regarding the clinician’s understanding and explicability of an AI-CDSS beyond the proof of benefit. Conclusions The study results highlight two different types among (future) clinicians regarding their view of the necessary levels of understanding and competence. These findings should inform the debate on appropriate training programmes and professional standards (e.g. clinical practice guidelines) that enable the safe and effective clinical employment of AI-CDSS in various clinical fields. While current approaches search for appropriate minimum requirements of the necessary understanding and competence, the differences between (future) clinicians in terms of their information and understanding needs described here can lead to more differentiated approaches to solutions. (shrink)

This article explores the trend of increasing automation in law enforcement and criminal justice settings through three use cases: predictive policing, machine evidence and recidivism algorithms. The focus lies on artificial-intelligence-driven tools and technologies employed, whether at pre-investigation stages or within criminal proceedings, in order to decode human behaviour and facilitate decision-making as to whom to investigate, arrest, prosecute, and eventually punish. In this context, this article first underlines the existence of a persistent dilemma between the goal of increasing (...) the operational efficiency of police and judicial authorities and that of safeguarding fundamental rights of the affected individuals. Subsequently, it shifts the focus onto key principles of criminal procedure and the presumption of innocence in particular. Using Article 6 ECHR and the Directive (EU) 2016/343 as a starting point, it discusses challenges relating to the protective scope of presumption of innocence, the burden of proof rule and the _in dubio pro reo_ principle as core elements of it. Given the transformations law enforcement and criminal proceedings go through in the era of algorithms, big data and artificial intelligence, this article advocates the adoption of specific procedural safeguards that will uphold rule of law requirements, and particularly transparency, fairness and explainability. In doing so, it also takes into account EU legislative initiatives, including the reform of the EU data protection _acquis_, the E-evidence Proposal, and the Proposal for an EU AI Act. Additionally, it argues in favour of revisiting the protective scope of key fundamental rights, considering, _inter alia_, the new dimensions suspicion has acquired. (shrink)

This article explores the potential ways in which artificial intelligence (AI) can support veganism, a lifestyle that aims to promote the protection of animals and also avoids the consumption of animal products for environmental and health reasons. The first part of the article discusses the technical requirements for utilizing AI technologies in the mentioned field. The second part provides an overview of potential use cases, including facilitating consumer change with the help of AI, technologically augmenting undercover investigations in factory farms, (...) raising the efficiency of nongovernment organizations promoting plant-based lifestyles, and so forth. The article acknowledges that the deployment of AI should not happen in a “solutionist” manner, meaning to always consider nontechnical means for achieving desired outcomes. However, it is important for organizations promoting veganism to realize the potential of modern data-driven tools and to merge and share their data to reach common goals. (shrink)

Artificial Intelligence (AI) has permeated every section of human life. Some even suggest that artificial moral advisors (AMAs)—that is, AI-driven artefacts designed to assist human moral growth by providing moral advice—can help humans to lead virtuous lives. With a focus on Thomas Aquinas's theological virtue ethics, this article will argue that AMA projects are cast into question because, theologically speaking, the cultivation of virtues and moral growth are inseparable from hope and patience given through God's grace, which is immeasurable (...) and cannot be reductively mathematised into AI models. Moreover, the tension between AI's efficiency and patient moral life offers further criticism of AMAs. That said, the theology of patience brings forth a conceptual apparatus through which to qualify the virtuous AMA as a tool to bring together human moral advisors as silent hearers and human moral advisees as patient inquirers. (shrink)

Social development organizations increasingly employ artificial intelligence -enabled tools to help team members collaborate effectively and efficiently. These tools are used in various team management tasks and activities. Based on the unified theory of acceptance and use of technology, this study explores various factors influencing employees’ use of AI-enabled tools. The study extends the model in two ways: a) by evaluating the impact of these tools on the employees’ collaboration and b) by exploring the moderating role of AI aversion. Data (...) were collected through an online survey of employees working with AI-enabled tools. The analysis of the research model was conducted using partial least squares, with a two-step model – measurement and structural models of assessment. The results revealed that the antecedent variables, such as effort expectancy, performance expectancy, social influence, and facilitating conditions, are positively associated with using AI-enabled tools, which have a positive relationship with collaboration. It also concluded a significant effect of AI aversion in the relationship between performance expectancy and use of technology. These findings imply that organizations should focus on building an environment to adopt AI-enabled tools while also addressing employees’ concerns about AI. (shrink)

This contribution discusses the development of the Data Ethics Decision Aid (DEDA), a framework for reviewing government data projects that considers their social impact, the embedded values and the government’s responsibilities in times of data-driven public management. Drawing from distinct qualitative research approaches, the DEDA framework was developed in an iterative process (2016–2018) and has since then been applied by various Dutch municipalities, the Association of Dutch Municipalities, and the Ministry of General Affairs (NL). We present the DEDA framework (...) as an effective process to moderate case-deliberation and advance the development of responsible data practices. In addition, by thoroughly documenting the deliberation process, the DEDA framework establishes accountability. First, this paper sheds light on the necessity for data ethical case deliberation. Second, it describes the prototypes, the final design of the framework, and its evaluation. After a comparison with other frameworks, and a discussion of the findings, the paper concludes by arguing that the DEDA framework is a useful process for ethical evaluation of data projects for public management and an effective tool for creating awareness of ethical issues in data practices. (shrink)

This study investigates the intersection of existing computational frameworks for zoonotic disease control within the emerging societal paradigm, Society 5.0. Technologies in human-centric computing can facilitate real-time data collection and analysis, enabling early detection and rapid response to zoonotic disease outbreaks, thereby enhancing surveillance and containment efforts for public health protection. It aims to explore challenges and opportunities within these frameworks and delineate future research directions to serve as a benchmark. Conducting a three-layered analysis, the study identifies high-level technologies, second-layer (...) technologies within those brackets, and minutely analyzes each technology in the second layer. The focus includes a comprehensive examination of the evolving landscape of zoonotic disease control and the influence of computational tools in public health. The findings highlight the potential of leveraging computational frameworks to predict, monitor, and control zoonotic diseases more effectively within Society 5.0. The major identified thrust areas include One Health and governmental integration, digital and data-driven solutions, healthcare and medical technologies, technologies for communication and information dissemination, and socio-cultural and ethical considerations. The integration of large-scale healthcare services offer a robust data source for AI-driven models, enabling early detection and dynamic response to zoonotic threats using data acquired from IoT surveillance and data acquisition infrastructures. However, challenges such as data privacy and equitable healthcare access need addressing. The study emphasizes the opportunities arising from the integration of computational frameworks with Society 5.0 for zoonotic disease control, proposing potential benefits in terms of improved disease surveillance and response. To fully realize this potential, the study suggests exploring specific technological landscapes for further investigation in the context of zoonotic disease control. (shrink)

This study looks at factors influencing, first, trust in artificial intelligence (AI) systems in human resources management, second, perceived usefulness of these tools. Based on a survey experiment provided to 324 private and public Swiss HR professionals’, it first explores how some trust in automation framework’s predictors are related to trust in HR AI tools and, then, how this trust is in return related to UTAUT’s perceived usefulness of these AI-enhanced tools. To do this, the following article is based on (...) a PLS-SEM structural equation model. Its main findings are that reliability, familiarity, intention of developers and propensity to trust are directly positively related to trust in the HR AI tools studied here. Nevertheless, public employees declare more negative feelings toward AI in HRM. Indeed, the latter systematically have less trust in HR AI than private employees. However, public sector employees do not find them any less useful or efficient than private sector employees, except when it comes to the HR AI tools used to assess employee performance and behavior. In addition to this, trust in these tools is systematically positively linked to their perceived usefulness. This influence is partly mediated by the perceived decision fairness of our tools, but not by the absence of privacy concerns associated with them. This said, this article makes a significant contribution to the literature about private and public actors’ perceptions of nascent HR AI-enhanced tools. (shrink)

Urbanization poses significant challenges to healthcare systems, including overcrowded hospitals, inequitable access to care, and rising costs. Artificial Intelligence (AI) and the Internet of Things (IoT) offer transformative solutions for optimizing healthcare delivery in smart cities. This paper explores how AI-driven predictive analytics, combined with IoT-enabled wearable devices and telemedicine platforms, can enhance patient outcomes, streamline resource allocation, and reduce urban health disparities. By analyzing real-time health data and predicting disease outbreaks, this study demonstrates the potential of AI to (...) revolutionize urban healthcare systems. Experimental results highlight improvements in diagnostic accuracy, emergency response times, and operational efficiency, paving the way for equitable and sustainable healthcare in smart cities. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the (...) ‘what’ of AI ethics —rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs. (shrink)

Urban parking systems are a significant contributor to traffic congestion and driver frustration, with studies showing that up to 30% of urban traffic is caused by drivers searching for parking. Traditional parking systems often lack real-time data and adaptability, leading to inefficiencies such as overfilled lots and underutilized spaces. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban parking by enabling real-time parking space detection, demand forecasting, and dynamic pricing. By integrating data from IoT sensors, traffic (...) systems, and mobile applications, cities can reduce congestion, improve parking availability, and enhance the overall urban mobility experience. Experimental results demonstrate significant improvements in parking efficiency, traffic flow, and user satisfaction, offering a sustainable blueprint for smart urban parking systems. (shrink)

Water scarcity and inefficient water management are critical challenges for rapidly growing urban areas. Traditional water distribution systems often suffer from leaks, wastage, and inequitable access, exacerbating resource shortages. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban water management by enabling real-time monitoring, predictive maintenance, and efficient resource allocation. By integrating data from smart meters, pressure sensors, and weather forecasts, cities can reduce water losses, improve distribution efficiency, and ensure equitable access. Experimental results demonstrate significant (...) improvements in leak detection, water conservation, and infrastructure reliability, offering a sustainable blueprint for urban water management in smart cities. (shrink)

Buildings account for a significant portion of global energy consumption and carbon emissions, making energy efficiency a critical focus for urban sustainability. Traditional building management systems often lack the adaptability and precision needed to optimize energy usage dynamically. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance energy efficiency in smart buildings by enabling real-time monitoring, predictive maintenance, and adaptive control systems. By integrating data from smart meters, occupancy sensors, and environmental monitors, cities can reduce energy waste, (...) lower carbon footprints, and improve occupant comfort. Experimental results demonstrate significant improvements in energy savings, operational costs, and environmental impact, offering a sustainable blueprint for smart building management. (shrink)