This paper presents an in-depth literature review on the driving forces and barriers for achieving operational excellence through artificial intelligence. Artificial intelligence is a technological concept spanning operational management, philosophy, humanities, statistics, mathematics, computer sciences, and social sciences. AI refers to machines mimicking human behavior in terms of cognitive functions. The evolution of new technological procedures and advancements in producing intelligence for machines creates a positive impact on decisions, operations, strategies, and management incorporated in the production process (...) of goods and services. Businesses develop various methods and solutions to extract meaningful information, such as big data, automatic production capabilities, and systematization for business improvement. The progress in organizational competitiveness is apparent through improvements in firm’s decisions, resulting in increased operational efficiencies. Innovation with AI has enabled small businesses to reduce operating expenses and increase revenues. The focused literature review reveals the driving forces for achieving operational excellence through AI are improvement in computing abilities of machines, development of data-based AI, advancements in deep learning, cloud computing, data management, and integration of AI in operations. The barriers are mainly cultural constraints, fear of the unknown, lack of employee skills, and strategic planning for adopting AI. The current paper presents an analysis of articles focused on AI adoption in production and operations. We selected articles published between 2015 and 2020. Our study contributes to the literature reviews on operational excellence, artificial intelligence, driving forces for AI, and AI barriers in achieving operational excellence. (shrink)

Explainable Artificial Intelligence (XAI) is increasingly championed for its potential to enhance transparency and accountability in clinical decision support. However, emerging evidence reveals a critical trade-off: these benefits may not come without significant costs. While deep neural networks achieve remarkable accuracy in tasks like diabetic retinopathy diagnosis (AUROCs near 0.91) (Aziz et al. in medRxiv https://doi.org/10.1101/2024.08.10.24311735, 2024), the pursuit of interpretability often necessitates simpler models, leading to a notable performance drop (AUROCs of 0.78–0.82), potentially compromising patient outcomes. Furthermore, post-hoc explanation (...) techniques like SHAP (Lundberg in A unified approach to interpreting model predictions. arXiv:1705.07874, 2017) and LIME (Ribeiro et al. in “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on knowledge discovery and data mining, pp 1135–1144, 2016), while offering insights into complex models, can more than double inference times (Abgrall et al. in Crit Care 28:301, 2024), significantly impeding workflow efficiency, particularly in time-sensitive clinical environments. Adding to these concerns, the integration of XAI can elevate operational costs by 10-15%, potentially contributing to clinician burnout and hindering widespread adoption (Dhiman et al. in Information 14:541, 2023; Hulsen in AI 4:652–666, 2023). While these challenges are significant, they must be weighed against potential long-term benefits in diagnostic accuracy, workflow efficiency, and regulatory compliance, as documented in recent implementation studies (Davenport and Kalakota in Future Healthc J 6:94–98, 2019; Sayres et al. in Ophthalmology 126:552–564, 2019). The multifaceted challenges highlight the inherent tension between transparency, performance, and cost-effectiveness in the application of AI within healthcare. This review synthesizes the evidence for these trade-offs, drawing upon technical findings on performance degradation, clinical data illustrating workflow disruptions, and economic analyses quantifying the financial burdens associated with XAI. We demonstrate that relying solely on post-hoc explanation methods can undermine the predictive power of high-performing models by approximately 3.8% (Amann et al. in BMC Med Inform Decis Mak 20:310, 2020) and add 30–40 s to each patient case. By examining promising emerging solutions, including hybrid models that integrate interpretable components directly into their architectures (Koh et al. in Concept bottleneck models. In: Proceedings of the International Conference on machine learning, PMLR, pp. 5338–5348. https://proceedings.mlr.press/v119/koh20a.html, 2020), and by advocating for clinician-centered evaluation frameworks, we aim to identify viable pathways to maintain diagnostic accuracy without incurring prohibitive costs or unacceptable latency. Ultimately, we argue that aligning AI design with the practical demands of clinical workflows, establishing standardized metrics for interpretability, and embedding transparency directly within model architectures are crucial steps towards realizing trustworthy and cost-efficient decision support. Through this pragmatic lens, balancing inherent interpretability with computational efficiency, and considering both immediate costs and long-term benefits, XAI can transition from a theoretical ideal to a sustainable and truly transformative asset for patient care. (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)

Keywords are essential to the searchability and therefore discoverability of museum and archival collections in the modern world. Without them, the collection management systems (CMS) and online collections these cultural organisations rely on to record, organise, and make their collections accessible, do not operate efficiently. However, generating these keywords manually is time consuming for these already resource strapped organisations. Artificial intelligence (AI), particularly generative AI and Large Language Models (LLMs), could hold the key to generating, even automating, this key data (...) and as such be considered a co-creative add-on. This study contributes to the literature by introducing the use of Meta’s open-source LLM, Llama, to generate keywords from curator/archivist written descriptions of museum and archival collection items. Our findings suggest that these technologies add significant value compared to current manual methods for keyword generation. In particular, we find that through using carefully crafted prompts, successful keyword augmentations could be established making museum and archival collections much more accessible to wider and more diverse audiences. However, the results also showed that generative AI has biases (e.g., hallucinations, over generalisations, outdated language), though the frequency of occurrence was not as high as general perception may insist. Hence, we also discuss mitigation strategies to address these and how cultural institutions can recognise the risks and errors while getting the most from the systems. Finally, we discuss options to achieve structured results which allow easier ingestion of data back into CMS. Ultimately, LLMs hold significant potential to enhance accessibility to museum and archival collections, yet they are not without imperfection as we extensively discuss. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the patient’s outcomes (...) with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (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)

Urban lighting systems are essential for safety, security, and quality of life, but they often consume significant energy and lack adaptability to changing conditions. Traditional lighting systems rely on fixed schedules and manual adjustments, leading to inefficiencies such as over-illumination and energy waste. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban lighting by enabling real-time adjustments, energy savings, and adaptive illumination based on environmental conditions and human activity. By integrating data from motion sensors, weather forecasts, (...) and traffic systems, cities can reduce energy consumption, enhance safety, and improve the quality of life for residents. Experimental results demonstrate significant improvements in energy efficiency, lighting quality, and operational costs, offering a sustainable blueprint for smart urban lighting systems. (shrink)

For companies involved in the supply chain, proper warehousing management is crucial. Warehouse layout arrangement and operation play a critical role in a company’s ability to maintain and improve its competitiveness. Reducing costs and increasing efficiency are two of the most crucial warehousing goals. Deciding on the best warehouse layout is a remarkable optimization problem. This paper uses an optimization method to set bin allocations within an automated warehouse with particular characteristics. The warehouse’s initial layout and the automated platforms (...) limit the search and define the time required to move goods within the warehouse. With the help of historical data and the definition of the time needed to move goods, a mathematical model of warehouse operation was created. An optimization procedure based on the well-known Variable Neighbourhood Search algorithm is defined and applied to the problem. Experimental results demonstrate increments in the efficiency of warehousing operations. (shrink)

L’ordinamento cosmico rappresenta il punto di riferimento costante dell’agire morale della creatura, che deve misurare le sue scelte preferendo sempre gli esseri-beni oggettivamente superiori a quelli inferiori. La virtù della scelta ordinata non è altro che la giustizia, come trasversalmente affermato nelle opere di Agostino d’Ippona. Il presente contributo intende mostrare come, sul piano creaturale, la dinamica della scelta ordinata rimane invariata con la teologia della grazia indebita sviluppata a partire dall’ _ Ad Simplicianum _. In particolare, si intende evidenziare (...) il passaggio da una visione che individua nella creatura stessa, aiutata da Dio, la causa prima della sua volontà morale, presente nelle opere precedenti il 397, a una decisamente più radicale, successiva a questa data, che invece individua tale causa nell’operare efficiente e predestinato di Dio. Quest’ultima posizione mostra il duplice vantaggio di risolvere alcuni paradossi impliciti nella riflessione precedente, senza smentire la forma (ordinata) della giustizia, sebbene non possa evitare di ridimensionare il ruolo della creatura in relazione ai suoi meriti. _ The cosmic order represents the focal point of reference for moral agents, who must evaluate their choices based on the degree of perfection rather than settling for inferiour objects or beings. According to Augustine of Hippo, justice is the virtue of ordered choice. The present contribution aims to show how, from the viewpoint of the creature, the structure of ordered choice does not change even when the theology of grace is developed in Ad Simplicianum. In particular, the essay focuses on the shift from an earlier view, developed before 397, that places the original cause of good will in the creature itself, along with God’s assistance, to a later and more radical perspective that sees God’s role as the exclusive source of good will through efficient and predestined intervention. The latter viewpoint has the dual advantage of unfolding some paradoxes of the previous perspective, while still maintaining the (ordered) form of justice, although it inevitably limits the role of creatures in relation to their merits. _. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Who Wrote This?: How AI and the Lure of Efficiency Threaten Human Writing by Naomi BaronLuke MunnBaron, Naomi. Who Wrote This?: How AI and the Lure of Efficiency Threaten Human Writing. Stanford University Press, 2023. 344pp.Who Wrote This? is Naomi Baron’s latest book exploring the emergence of AI language models and their potential implications for writing. A linguist, educator, and emeritus professor at American University, Baron (...) should be well placed to tease out the nuances of language, showcase the creativity of human literary canon across history, and deliver a set of findings and recommendations informed by deep experience. Indeed, since the release of ChatGPT in November 2022, there is a hunger for this kind of expertise and guidance both inside and outside academia, as the deluge of debates, panels, and forums in the past year have attested. This is the perfect storm, one that should be harnessed by a linguist with two decades of work on digital writing and reading into a powerful book. Instead, the reader must wade through a morass of truisms, historical anecdotes, and wiki-like summaries of research studies, interspersed with surface-level ponderings.The problems begin almost immediately with the structure of the book itself. While the book is ostensibly divided into chapters, each chapter is atomized into dozens of subsections, some no more than a paragraph or two in length. In this whirlwind tour, the reader is swiftly confronted with a jumble of quotes from journalists, jokes about language, historical events, a “laypersons AI roadmap,” and recent news stories, flitting from one to the next with barely a pause. The “AI Applications” section takes this same piecemeal approach, laying out the possible uses of artificial intelligence and then painstakingly stepping through each one. Each subsection features a few paragraphs that regurgitates material you might find, more cohesively and in-depth, in a Wikipedia article. The material on the history of artificial intelligence is handled simplistically and uncritically, skipping across a handful of heroic figures like Yann LeCun, Yoshua Bengio, and Fei-Fei Li.While anecdotes and quotes abound, an argument is much harder to locate. On the one hand, Baron seems to uncritically echo the AI hype that has dominated news headlines in the past year. “We’ve come a long way,” she opines, “we have vacuum cleaning robots, drones, and self-driving [End Page 156] cars” (59). The reader learns that AI can generate poetry, correct grammar, play games, and beat masters (AlphaGo), and even identify cancer (machine learning for mammograms). These use-cases, and the ambitious claims that accompany them, are never really challenged in any sustained theoretical or methodological way. On the other hand, Baron seems to be highly conservative when it comes to the proper domain of “the human” and of writing in particular. “AI isn’t up for self-expression or thinking” (14) she says in passing, dismissing the recent revolutionary improvements in large language models that she’s just hyped up.The possibility of machinic intelligence and creativity, in fact, is a fascinating if messy one. Turing was not so interested in demonstrably “proving” that computers could be intelligent, but instead (and very much in line with his everyday navigation of British sexual norms) in developing something that could “pass” as human. Intelligence was not an acid-test, but a set of gestures that could be performed or pantomimed, emulated until they were “good enough” to deceive. N. Katherine Hayles has continued this trajectory in her work on “non-conscious cognition,” removing the barriers to what qualifies as intelligence and subsequently extending cognition down to unicellular organisms and plants. Within my own work on language models alongside colleagues (Munn et al. 2023; Magee, Munn, and Arora 2023), we highlight how models like GPT 3.5 achieve an approximation of natural speech. What is important is not the authenticity or actual intelligence of this feat, but that this “good enough” ability means that users treat language models as an interlocutor, a listener, a subject. This projection, even if misplaced, opens up this interaction and requires a conceptualization that is at least as attuned to memory, affect, and subjectivity as software interfaces and linear... (shrink)

Ultrasonic excitation has been widely used in the detection of microcracks on metal surfaces, but there are problems such as poor excitation effect of ultrasonic pulse, long time to reach the best excitation, and difficult to find microcracks. In this paper, an adaptive ultrasonic pulse excitation device and infrared thermal imaging technology have been combined, as well as their control method, to solve the problem. The adaptive ultrasonic pulse excitation device adds intelligent modules to realize automatic adjustment of detection parameters, (...) which can quickly obtain reliable excitation; the multidegree-of-freedom base realizes the three-dimensional direction change of the ultrasonic gun to adapt to different excitation occasions. When the appropriate ultrasonic excitation makes microcracks in the resonance state, the microcracks can be frictionated, which produce heat rise with the temperature. Then, the microcrack defect can be detected by the infrared thermal instrument through the different surface temperatures with imaging recognition method. Our detection experiments of the titanium alloy plates and the aluminum alloy profiles of marine engineering show that the method can get reliable detection parameters in a short time and measure the crack length effectively. It can be used in many aspects such as crack detection in mechanical structures or complex equipment operating conditions and industrial production processes. (shrink)

Highly sophisticated capabilities of artificial intelligence (AI) have skyrocketed its popularity across many industry sectors globally. The public sector is one of these. Many cities around the world are trying to position themselves as leaders of urban innovation through the development and deployment of AI systems. Likewise, increasing numbers of local government agencies are attempting to utilise AI technologies in their operations to deliver policy and generate efficiencies in highly uncertain and complex urban environments. While the popularity of AI (...) is on the rise in urban policy circles, there is limited understanding and lack of empirical studies on the city manager perceptions concerning urban AI systems. Bridging this gap is the rationale of this study. The methodological approach adopted in this study is twofold. First, the study collects data through semi-structured interviews with city managers from Australia and the US. Then, the study analyses the data using the summative content analysis technique with two data analysis software. The analysis identifies the following themes and generates insights into local government services: AI adoption areas, cautionary areas, challenges, effects, impacts, knowledge basis, plans, preparedness, roadblocks, technologies, deployment timeframes, and usefulness. The study findings inform city managers in their efforts to deploy AI in their local government operations, and offer directions for prospective research. (shrink)

In efforts to achieve the Sustainable Development Goals, social entrepreneurship has gained popularity as a vehicle for positive change in developing countries. The multiplicity of stakeholders, diverging sociocultural contexts and the hybrid mission complicate the process of legitimacy construction for social entrepreneurs as a basis for the acquisition of scarce resources. This study investigates how social entrepreneurs operating in Sub-Saharan Africa and Asia tackle this challenge of bridging conflicting directions in discursive interaction with their European funders. We conduct a multimodal (...) discourse analysis to uncover discursive strategies for legitimacy construction by combining linguistic data from interviews with visual data from social media accounts. Legitimacy construction, and thus resource acquisition, centers on three aspects which interlink pragmatic, cognitive and moral legitimacy: developing innovative solutions, mobilizing private-sector efficiency, and contributing to local empowerment. Presenting these aspects as mutually reinforcing overcomes contradictions between social and business logics and provides an expanded space for legitimacy construction of social entrepreneurship. Discursively constructing legitimacy around a ‘glorified version of social entrepreneurship’ mobilizes resources but downplays the risk of being an entrepreneur in the Global South, contributing to increasing corporatization of social purpose organizations. (shrink)

This is a proposal for the re-activation of the essentially stillborn automatic car project for which the cart was originally obtained, and presents a process through which this activation could be accomplished painlessly. The project would be financed from the lab's operating grant, and would interact strongly with, while being independent of, any Mars rover research initiated by Lynn Quam. Since I seem to be the only one, apart from John McCarthy, with an active interest in this aspect of (...) things, the following is written entirely from my point of view. (shrink)

In the context of technological advances, the concept of digital justice is emerging, an extra-judicial sphere that uses technologies such as artificial intelligence to address controversial situations through physical assistants or even robots, if the litigant so wishes. Thus, while AI is effective in resolving simple disputes without human intervention, even UNESCO warns against its exclusivity in more complicated cases. From this perspective, through a qualitative approach and literature review, this research focused on examining the benefits and limitations (...) of AI in the administration of justice. The review of the academic literature reveals that this technology facilitates the functions performed by judges and lawyers in controversial situations. The studies, however, point to the importance of using human intelligence in law making and more general judicial processes. In conclusion, AI improves the efficiency of the administration of justice; however, its place in judicial operations and process should only be complementary to the use of human intervention. This action maintains fairness and considers the ethical point at stake, reaffirms the existence of some irreplaceable human capabilities in the judicial process. (shrink)

Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine classifier to control a robot arm in order to help a person to find an object in the (...) environment. The main focus here is to search for the best features that describe efficiently the electroencephalogram data during such imagined gestures by comparing two feature extraction methods, namely the continuous wavelet transform and the empirical modal decomposition, combined with the principal component analysis that were fed through a linear and radial basis function kernel SVM classifier. The experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with an RBF kernel SVM classifier using CWT and PCA compared to 80.25% accuracy obtained with EMD and PCA. The proposed system has been implemented and tested using data collected from five male subjects and it enabled the control of the robot arm in the right and the left direction. (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)

Machine learning algorithms are increasingly used to support decision-making in the exercise of public authority. Here, we argue that an important consideration has been overlooked in previous discussions: whether the use of ML undermines the democratic legitimacy of public institutions. From the perspective of democratic legitimacy, it is not enough that ML contributes to efficiency and accuracy in the exercise of public authority, which has so far been the focus in the scholarly literature engaging with these developments. According to (...) one influential theory, exercises of administrative and judicial authority are democratically legitimate if and only if administrative and judicial decisions serve the ends of the democratic law maker, are based on reasons that align with these ends and are accessible to the public. These requirements are not satisfied by decisions determined through ML since such decisions are determined by statistical operations that are opaque in several respects. However, not all ML-based decision support systems pose the same risk, and we argue that a considered judgment on the democratic legitimacy of ML in exercises of public authority need take the complexity of the issue into account. This paper outlines considerations that help guide the assessment of whether a ML undermines democratic legitimacy when used to support public decisions. We argue that two main considerations are pertinent to such normative assessment. The first is the extent to which ML is practiced as intended and the extent to which it replaces decisions that were previously accessible and based on reasons. The second is that uses of ML in exercises of public authority should be embedded in an institutional infrastructure that secures reason giving and accessibility. (shrink)

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

This article is a contribution to the field of study that Jacques Maritain once described as “metaphysical Axiomatics.” I discuss Aquinas’s use of the metaphysical principle “omne agens agit sibi simile,” focusing on perhaps the most manifest instance of this principle, namely, univocal generation. It is well known that Aquinas holds what could be called a “static” or “formal” view of likeness between God and creatures: creatures are like God because they share in certain exemplar perfections that preexist in God. (...) My focus instead is on an efficient likeness to God, which reflects a foundational truth about reality for Aquinas: all creatures produce something like themselves through their operations, in imitation of God, who does so on a more fundamental level. My discussion will also clarify Aquinas’s derivation of the principle of similitude from a prior metaphysical principle, “every agent acts insofar as it is in act.”. (shrink)

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

This contribution deals with taking up the challenge of sustainable development through human centred systems which aim at the creation and repatriation of global quality in each society, and which are seen to operate as a whole, on a local, regional or even a planetary scale. The paper argues that, particularly in a field such as information, communication, environment, technological processes and innovations, which have structurally revolutionised first of all manufacturing but also education and daily living at the same (...) time. However, producing new pathogenous structures require, by necessity, a political ecology in order to relate these fields to new figures of meditation/mediance and crossbreeding built up by associative networks. PRELUDE'S experience, as an international networking programme of scientists pursuing with other social factors objectives of codevelopments (in response to the failure of bad development in the North and in the South), and its contribution to the theme Global Perspective 2010 of the CE-FAST Programme suggest the actual relevance and, turning to the future, the decisive function of associative networks as a way of approaching more efficiently, because of their flexibility, complex, highly heterogeneous situations to be tackled in a systemic and global fashion. In complementing institutions and established companies these networks give their new performances and efficacity to institute, and so doing displace acquired balances, and increase their capacity to innovate. (shrink)

Considerable interest has been shown in models of inter-organisational collaboration including clusters, networks and recently supply chains. Arguably effective configurations of enterprises can work together to achieve some form of what is termed ‘collective efficiency’ which enables them to cope with the challenges of the current competitive encironment. This paper addresses one aspect of such collective efficiency: the potential acceleration and improvement of the process of knowledge acquisition and capacity building through shared learning. It explores the concept (...) of formal ‘learning networks’ and reports on UK research into the design and operation of such networks. (shrink)

Consider a firm as an organization that needs to efficiently coordinate several specialized departments in an uncertain environment. Decision making involves collective planning sessions and decentralized operational processes. In this setting this paper explores the role of economic modeling through an experimental game. Results support the idea that economic modeling favors higher performance. Economic modeling facilitates the emergence of common knowledge and the decomposition of a group decision problem into individual decision problems that are meaningfully interrelated.

An increasing number of financial services (FS) companies are adopting solutions driven by artificial intelligence (AI) to gain operational efficiencies, derive strategic insights, and improve customer engagement. However, the rate of adoption has been low, in part due to the apprehension around its complexity and self-learning capability, which makes auditability a challenge in a highly regulated industry. There is limited literature on how FS companies can implement the governance and controls specific to AI-driven solutions. AI auditing cannot be performed (...) in a vacuum; the risks are not confined to the algorithm itself, but rather permeates the entire organization. Using the risk of unfairness as an example, this paper will introduce the overarching governance strategy and control framework to address the practical challenges in mitigating risks AI introduces. With regulatory implications and industry use cases, this framework will enable leaders to innovate with confidence. (shrink)

Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic (...) counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted. (shrink)

Cooper (1983) pioneered underspecified scope representation in formal and computational semantics through his introduction of quantifier storage into Montague semantics as an alternative to the syntactic operation of quantifying-in. In this paper we address an important issue in the development of an adequate formal theory of underspecified semantics. The tension between expressive power and computational tractability poses an acute problem for any such theory. Ebert (2005) shows that any reasonable current treatment of underspecified semantic representation either suffers from expressive (...) incompleteness or produces a combinatorial explosion that is equivalent to generating the full set of possible scope readings in the course of disambiguation. In previous work we have presented an account of underspecified scope representations within Property Theory with Curry Typing (PTCT), an intensional first-order theory for natural language semantics. Here we show how filters applied to the underspecified-scope terms of PTCT permit both expressive completeness and the reduction of computational complexity in a significant class of non-worst case scenarios. (shrink)

Healthcare systems are grappling with critical challenges, including chronic diseases in aging populations, unprecedented health care staffing shortages and turnover, scarce resources, unprecedented demands and wait times, escalating healthcare expenditure, and declining health outcomes. As a result, policymakers and healthcare executives are investing in artificial intelligence (AI) solutions to increase operational efficiency, lower health care costs, and improve patient care. However, current level of investment in developing healthcare AI among members of the global digital health partnership does not (...) seem to yield a high return yet. This is mainly due to underinvestment in the supporting infrastructure necessary to enable the successful implementation of AI. If a healthcare-specific AI winter is to be avoided, it is paramount that this disparity in the level of investment in the development of AI itself and in the development of the necessary supporting system components is evened out. (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)

Of all the different types of public buildings, hospitals are the biggest energy consumers. Cooling systems for air conditioning and healthcare uses are particularly energy intensive. Forecasting hospital thermal-cooling demand is a remarkable and innovative method capable of improving the overall energy efficiency of an entire cooling system. Predictive models allow users to forecast the activity of water-cooled generators and adapt power generation to the real demand expected for the day ahead, while avoiding inefficient subcooling. In addition, the maintenance (...) costs related to unnecessary starts and stops and power-generator breakdowns occurring over the long term can be reduced. This study is based on the operations of a real hospital facility and details the steps taken to develop an optimal and efficient model based on a genetic methodology that searches for low-complexity models through feature selection, parameter tuning and parsimonious model selection. The methodology, called GAparsimony, has been tested with neural networks, support vector machines and gradient boosting techniques. Finally, a weighted combination of the three best models was created. The new operational method employed herein can be replicated in similar buildings with similar water-cooled generators. (shrink)

The article presents a socio-philosophical analysis of artificial intelligence (AI) integration into public administration systems. The research focuses on identifying an optimal balance between enhancing administrative efficiency and preserving humanistic values. The author examines diverse perspectives on AI’s role in contemporary society, ranging from techno-optimistic concepts that view AI as a tool for qualitative improvement of human life, to critical theories warning of dehumanization risks and increased social control. The paper conducts a comparative analysis of national AI development strategies (...) among leading global powers, identifying their common features and significant differences shaped by cultural, political, and economic factors. Potential risks and threats associated with the implementation of AI systems in public administration are explored, including issues of personal data protection, information security, and the ethical dimensions of algorithmic decision-making. The concept of a human-centered approach to AI is examined as a potential guiding principle for the development and deployment of these technologies. Various levels of control over AI systems are characterized, encompassing legal regulation, professional and public evaluation. Particular attention is given to the prospects of artificial general intelligence (AGI) development and its potential impact on the transformation of state institutions and social relations. The study argues that AGI architecture, enabling genuine system agency, must incorporate a level responsible for actualization functions (strategic goal-setting, ethics, knowledge, and self-identification). Special emphasis is placed on the system’s awareness of its finite existence as a necessary condition for developing meaningful operational strategies and ethical principles. The article concludes that as AI technologies advance, the importance of ethical norms, value systems, and responsibility principles increases since these core societal factors cannot be fully replaced even by the most sophisticated regulation. The author highlights the growing significance of mutual trust between state and society in an environment where AI systems provide unprecedented opportunities for social control. (shrink)

Complex systems are difficult to manage, operate and maintain. This is why we see teams of highly specialised engineers in industries such as aerospace, nuclear and subsurface. Condition based monitoring is also employed to maximise the efficiency of extensive maintenance programmes instead of using periodic maintenance. A level of automation is often required in such complex engineering platforms in order to effectively and safely manage them. Advances in Artificial Intelligence related technologies have offered greater levels of automation but this (...) potentially pivots the weight of decision making away from the operator to the machine. Implementing AI or complex algorithms into a platform can mean that the Operators’ control over the system is diminished or removed altogether. For example, in the Boeing 737 Air Max Disaster, AI had been added to a platform and removed the operators’ control of the system. This meant that the operator could not then move outside the extremely reserved, algorithm defined, “envelope” of operation. This paper analyses the challenges of AI driven condition based monitoring where there is a potential to see similar consequences to those seen in control engineering. As the future of society becomes more about algorithm driven technology, it is prudent to ask, not only whether we should implement AI into complex systems, but how this can be achieved ethically and safely in order to reduce risk to life. (shrink)

Through the lens of feminist speech act theory, this paper argues that artificial intelligence romance systems objectify and subordinate nonvirtual women. AI romance systems treat their users as consumers, offering them relational invulnerability and control over their (usually feminized) digital romantic partner. This paper argues that, though the output of AI chatbots may not generally constitute speech, the framework offered by an AI romance system communicates an unjust perspective on intimate relationships. Through normalizing controlling one’s intimate partner, these (...) systems operate as speakers that rank women as non-player characters and license their oppression, which is unjust even in the absence of empirical harms. Understanding AI romance systems as speakers has implications for policymakers. First, this account helps close the so-called responsibility gap in the operation of an AI system. Second, it places these systems under the purview of institutions’ policies regarding hate speech, suggesting one avenue for arguing that these systems may violate extant policies. Finally, it provides support for the pursuit of empirical research into AI romance systems’ effects on users. (shrink)

Algorithmic vision, the computational process of making meaning from digital images or visual information, has changed the relationship between the image and the human subject. In this paper, I explicate on the role of algorithmic vision as a technique of algorithmic governance, the organization of a population by algorithmic means. With its roots in the United States post-war cybernetic sciences, the ontological status of the computational image undergoes a shift, giving way to the hegemonic use of automated facial recognition technologies (...) towards predatory policing and profiling practices. By way of example, I argue that algorithmic vision reconfigures the philosophical links between vision, image, and truth, paradigmatically changing the way a human subject is represented through imagistic data. With algorithmic vision, the relationship between subject and representation challenges the humanistic discourse around images, calling for a critical displacement of the human subject from the center of an analysis of how computational images make meaning. I will explore the relationship between the operative image, the image that acts but is not seen by human eyes, and what Louise Amoore calls an “emergent subject,” a subject that is made visible through algorithmic techniques. Algorithmic vision reveals subjects to power in a mode that requires a new approach towards analyzing the entanglement and invisiblization of the human in automated decision-making systems. (shrink)

Machine learning, and more broadly artificial intelligence (AI), is a fascinating technology and can be considered as the closest approximation to the Cartesian “thinking thing” that humans have ever created. Just as the industrial revolution required a new ethos, the age of intelligent machines will create its own, challenging the established moral, economic, and political presuppositions. This paper discusses the relationship between AI and society; it presents several thought experiments to explore the complexity of the relationship and highlights the insufficiency (...) of the current normative paradigm in addressing technological expansion. I argue that many of the externalities, such as security risks, loss of privacy, and economic instability will result from trying to fit the emerging technologies into the existing frame of efficiency and utility, by redefining the notions of human value, identity, autonomy, purity, and truth, among others. The age of the intelligent machine is elevating alienation to new levels, treating the individual as mere patterns in data—its primary commodity. I further argue that while the possibility of unintended consequences, due to the potential misuse of AI is ever present, the intelligent machine per se is unlikely to engage in a zero-sum game for power on its own initiative. I question whether singularity is at all attainable and argue that technology will forever remain a proxy for human interests. I conclude by posing questions for charting the path forward. Through this analysis, I aim to provide a more nuanced understanding of the complex relationship between the AI and humans. (shrink)

This study examines the interplay between text summarization techniques and embeddings from Language Models (LMs) in constructing expert systems dedicated to the retrieval of legal precedents, with an emphasis on achieving cost-efficiency. Grounded in the growing domain of Artificial Intelligence (AI) in law, our research confronts the perennial challenges of computational resource optimization and the reliability of precedent identification. Through Named Entity Recognition (NER) and part-of-speech (POS) tagging, we juxtapose various summarization methods to distill legal documents into a (...) convenient form that retains their essence. We investigate the effectiveness of these methods in conjunction with state-of-the-art embeddings based on Large Language Models (LLMs), particularly ADA from OpenAI, which is trained on a wide range of general-purpose texts. Utilizing a dataset from one of Brazil’s administrative courts, we explore the efficacy of embeddings derived from a Transformer model tailored to legal corpora against those from ADA, gauging the impact of parameter size, training corpora, and context window on retrieving legal precedents. Our findings suggest that while the full text embedded with ADA’s extensive context window leads in retrieval performance, a balanced combination of POS-derived summaries and ADA embeddings presents a compelling trade-off between performance and resource expenditure, advocating for an efficient, scalable, intelligent system suitable for broad legal applications. This study contributes to the literature by delineating an optimal approach that harmonizes the dual imperatives of computational frugality and retrieval accuracy, propelling the legal field toward more strategic AI utilization. (shrink)

One of the challenges facing AI governance is the need for multiple scales. Universal human rights require a global scale. If someone asks AI if education is harmful to women, the answer should be “no” regardless of their location. But economic democratization requires local control: if AI’s power over an economy is dictated by corporate giants or authoritarian states, it may degrade democracy’s social and environmental foundations. AI democratization, in other words, needs to operate across multiple scales. Nature allows the (...) multiscale flourishing of biological systems through fractal distributions. In this paper, we show that key elements of the fractal scaling found in nature can be applied to the AI democratization process. We begin by looking at fractal trees in nature and applying similar analytics to tree representations of online conversations. We first examine this application in the context of OpenAI’s “Democratic Inputs” projects for globally acceptable policies. We then look at the advantages of independent AI ownership at local micro-levels, reporting on initial outcomes for experiments with AI and related technologies in community-based systems. Finally, we offer a synthesis of the two, micro and macro, in a multifractal model. Just as nature allows multifractal systems to maximize biodiverse flourishing, we propose a combination of community-owned AI at the micro-level, and globally democratized AI policies at the macro-level, for a more egalitarian and sustainable future. (shrink)

Internet of thing building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT edge applications security and approval. (...) There are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques. (shrink)

To meet the growing demands for yet more efficient and safer traffic, traffic control is deployed in all modes of transportation. In maritime transportation, traffic control is performed by Vessel Traffic Services (VTS). This paper describes research which is focused upon measurement of VTS operator performance. The concept of situation awareness is introduced as a means to describe and quantify VTS operator performance. Situation awareness is tested in a full scale interactive simulator. A scoring system for VTS operator performance accounts (...) for the specific characteristics of VTS operator work, such as accuracy and relevance of information. (shrink)

This paper reframes the issue of appropriation, extraction, and dispossession through AI—an assemblage of machine learning models trained on big data—in terms of enclosure and foreclosure. While enclosures are the product of a well-studied set of operations pertaining to both the constitution of the sovereign State and the primitive accumulation of capital, here, I want to recover an older form of the enclosure operation to then contrast it with foreclosure to better understand the effects of current algorithmic rationality. I (...) argue that the act of enclosing is to be understood as a set of fundamental operations that consist in producing structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or by algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, I show how current AI perpetuates and at the same time expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For, while the outside is essential and constitutive to the logic of enclosure, foreclosure, by contrast, is characterized by the “totalizing desire” to not leave anything out of it. The totalizing desire to move beyond the logic of the enclosure forecloses that the enclosure has just gotten bigger and that the operations of exclusion have been masked and displaced, making them harder to contest. (shrink)

One of the few certainties we have about our collective future is that it will require a massive amount of learning, by just about everybody, everywhere. The time for generating as many creative and collaborative knowledge builders has come. Therefore, improving the efficiency of learning could very well become a key leverage point for successfully meeting the challenges of the twenty-first century. This paper explores the possibilities of using mutual learning as a systemic means to improve learning efficiencies. This (...) is measured through three different metrics: (1) the time required to learn, (2) the quantity of learning that is retained over time, and (3) the leveraging of the cost of scholarships through the use of a complementary currency designed to track and encourage mutual learning. In all three metrics, mutual learning is shown as an important approach to increase the effectiveness of learning and, at the very least, can be an adjunct to the conventional educational methods. Mutual learning could apply not only to learning among peers, but also to social, intergenerational, or intercultural mutual learning. (shrink)

In an era defined by the global surge in the adoption of AI-enabled technologies within public administration, the promises of efficiency and progress are being overshadowed by instances of deepening social inequality, particularly among vulnerable populations. To address this issue, we argue that democratizing AI is a pivotal step toward fostering trust, equity, and fairness within our societies. This article navigates the existing debates surrounding AI democratization but also endeavors to revive and adapt the historical social justice framework, maximum (...) feasible participation, for contemporary participatory applications in deploying AI-enabled technologies in public administration. In our exploration of the multifaceted dimensions of AI’s impact on public administration, we provide a roadmap that can lead beyond rhetoric to practical solutions in the integration of AI in public administration. (shrink)

Can AI solve the ethical, moral, and political dilemmas of warfare? How is artificial intelligence (AI)-enabled warfare changing the way we think about the ethical-political dilemmas and practice of war? This article explores the key elements of the ethical, moral, and political dilemmas of human-machine interactions in modern digitized warfare. It provides a counterpoint to the argument that AI “rational” efficiency can simultaneously offer a viable solution to human psychological and biological fallibility in combat while retaining “meaningful” human control (...) over the war machine. This Panglossian assumption neglects the psychological features of human-machine interactions, the pace at which future AI-enabled conflict will be fought, and the complex and chaotic nature of modern war. The article expounds key psychological insights of human-machine interactions to elucidate how AI shapes our capacity to think about future warfare's political and ethical dilemmas. It argues that through the psychological process of human-machine integration, AI will not merely force-multiply existing advanced weaponry but will become de facto strategic actors in warfare – the “AI commander problem.”. (shrink)

Generative AI has seen significant advances, particularly in text-to-image, with the potential to revolutionize industries, especially in creative fields such as art and design. This innovation is especially important in architecture, where idea visualization is critical. Text-to-image tools, a form of generative AI, enable architects and designers to visually bring their concepts to life. The study explores the impact of prompt-based AI generation on architecture, asking whether it is enhancing efficiency, creativity, and sustainability or threatening to replace architects. To (...) address concerns about the role of AI in the profession, the research examines the perceptions of architecture professionals in Egypt. The authors conducted a survey and interviews with industry experts to assess the transformative impacts of AI on architecture. The findings reveal a strong awareness of AI's potential to enhance design quality and project outcomes, although some concerns about job prospects and control over AI outputs persist. Small firms view AI as vital for optimizing operations and attracting clients. Overall, AI shows promise in conceptualization and visualization, enhancing creativity and efficiency, with architects needing to adapt to AI as a tool for innovation rather than a competitor. Finally, the study proposes a roadmap for improving the use of AI in architecture. (shrink)

Urban transportation systems face significant challenges due to increasing congestion, inefficient routes, and fluctuating passenger demand. Traditional public transportation networks often struggle to adapt dynamically to these challenges, leading to delays, overcrowding, and environmental inefficiencies. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban mobility by enabling real-time route optimization, demand forecasting, and passenger flow management. By integrating data from GPS trackers, fare collection systems, and environmental sensors, cities can reduce travel times, enhance commuter satisfaction, and (...) promote sustainable transportation. Experimental results demonstrate improvements in route efficiency, passenger load balancing, and operational costs, offering a blueprint for AI-driven smart urban mobility. (shrink)

Urban wastewater management is a critical component of sustainable water cycles, but traditional systems often struggle with inefficiencies such as high operational costs, resource wastage, and environmental pollution. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban wastewater management by enabling real-time monitoring, predictive maintenance, and resource recovery. By integrating data from IoT sensors, water quality monitors, and treatment plants, cities can improve water quality, reduce operational costs, and recover valuable resources such as energy (...) and nutrients. Experimental results demonstrate significant improvements in treatment efficiency, resource recovery rates, and environmental impact, offering a sustainable blueprint for smart urban wastewater systems. (shrink)

Through the analysis of practices of a primary school teacher-in-training during her practicum, the author aims to better understand professional and personal experience. A concrete case issued from a clinical, longitudinal and comparative study led between 2008 and 2009 in comparison with two teachers-in-training reports the interiorization process and the capitalized experience. The comparative study is issued of an innovative practicum project in operation during three years from 2007 to 2009 in the University of Montpellier, and called ‘moderated writing (...) workshop’. The exploratory study leans on notions like experience and professional culture suited to the classroom. The methodology is that of a clinical analysis of one case, followed by the analysis of the verbal and behavioral elements included in the interviews in order to set stabilized long-running categories. The skills and knowledge undertaken by the teacher-in-training examining her own practice are carefully analyzed all along, thus creating an innovative system. The results show that an important evolution occurs concerning practical rules, which seems relevant for the professionals. Those rules give to formative people information about professionalism. Results are discussed before concluding. (shrink)

The technical literature actively discusses ideas for creating digital twins to solve a wide range of problems that arise for enterprises of various kinds. Advances in the areas of information technology and the development of the necessary software really make it possible to obtain important new results through the creation of digital twins. This paper proposes the concept of building a network of digital twins, used to solve a number of actual problems in complex telecommunication systems. Two applications are (...) considered examples of such tasks. The first application is the organization of information feedback for enterprises involved in the life cycle of a telecommunication network: “elaboration of modernization principles, development of equipment, design, construction, and operation.” The second application is monitoring traffic, including its atypical behavior, in order to avoid the recurrence of erroneous actions in case of emergency situations. Another important feature of the digital twin network is that it will become an effective tool for conducting interdisciplinary research. (shrink)

A major challenge with the increasing use of Artificial Intelligence applications is to manage the long-term societal impacts of this technology. Two central concerns that have emerged in this respect are that the optimized goals behind the data processing of AI applications usually remain opaque and the energy footprint of their data processing is growing quickly. This study thus explores how much people value the transparency and environmental sustainability of AI using the example of personal AI assistants. The results from (...) a choice-based conjoint analysis with a sample of more than 1.000 respondents from Germany indicate that people hardly care about the energy efficiency of AI; and while they do value transparency through explainable AI, this added value of an application is offset by minor costs. The findings shed light on what kinds of AI people are likely to demand and have important implications for policy and regulation. (shrink)

This paper reframes the issue of appropriation, extraction, and dispossession through AI—an assemblage of machine learning models trained on big data—in terms of enclosure and foreclosure. While enclosures are the product of a well-studied set of operations pertaining to both the constitution of the sovereign State and the primitive accumulation of capital, here, I want to recover an older form of the enclosure operation to then contrast it with foreclosure to better understand the effects of current algorithmic rationality. I (...) argue that the act of enclosing is to be understood as a set of fundamental operations that consist in producing structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or by algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, I show how current AI perpetuates and at the same time expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For, while the outside is essential and constitutive to the logic of enclosure, foreclosure, by contrast, is characterized by the “totalizing desire” to not leave anything out of it. The totalizing desire to move beyond the logic of the enclosure forecloses that the enclosure has just gotten bigger and that the operations of exclusion have been masked and displaced, making them harder to contest. (shrink)