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

Value co-creation goes through the stage of co-production, customer experience, service-dominant logic, and service ecosystem. The integration of science and technology has become a key factor to the process of VCC. The rise and application of artificial intelligence technology has added a new driving force to VCC and began to affect its original practical logic. Based on the consumer perspective, this study uses Q-methodology to measure consumer cognitive attitude toward the use of AI technology in VCC, aiming to (...) explore the key factors that affect VCC. The study found that content quality, information security, efficiency concern, and degree of manipulation have become the important concerns of consumers for VCC under AI integration. Moreover, their different statements have their specific focus and direction. The study demonstrates and analyzes the importance of the four factors and proposes the combination of human and non-human actors to shape the new model of VCC in the future, which is worth further deepening and exploring for academia and enterprises. (shrink)

We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to (...) target a model-agnostic distillation approach exemplified with these two frameworks, secondly, to study how these two frameworks interact on a theoretical level, and, thirdly, to investigate use-cases in ML and AI in a comparative manner. Specifically, we envision that user-studies will help determine human understandability of explanations generated using these two frameworks. (shrink)

This experimental study provides further support for a theory of meaning first put forward by Bar-Hillel and Carnap in 1953 and foreshadowed by Asimov in 1951. The theory is the Popperian notion that the meaningfulness of a proposition is its a priori falsity. We tested this theory in the first part of this paper by translating to logical form a long, tightly written, published text and computed the meaningfulness of each proposition using the a priori falsity measure. We then selected (...) the top propositions—by a priori falsity—and strung them together to form ad hoc abstracts and compared these abstracts with the published summary. The results were startling: translation to logical form, followed by application of the Asimovian idea and Bar-Hillel/Carnap mathematics as elaborated into an AI/NLP proposal in Fulda (1986, 1988), produced excellent abstracts, thereby providing a proof-of-concept that merely by knowing the logical form of long text passages, one can produce reasonable abstracts of them—without actually understanding the text. We here report on a second experiment analyzing, in the exact same manner, the correspondence that followed the published text of the first experiment. While the results of this confirming experiment are less startling, they nevertheless provide additional confidence in the promise of the technique. In other words, were the results of these two experiments to generalize, that would show that logical form captures much more semantics than has heretofore been considered likely. Far from (as is commonly supposed) being merely the syntactical rewrite of text into formal notation, translation to logical form, even when undertaken with almost no knowledge about the particular predicates, individual constants, or other objects referred to in that form, might capture the core of the meaning in some important sense. -/- Note: There is a key difference in the /style/ of the texts analyzed in I and II in this long paper, divided into two only for practical reasons—space; the first text analyzed is written in a tight mathematical style, while the second is written in the discursive style of philosophy. -/- Finally, the unusual (for me) length of this two-part paper is justified by its being data-driven. (shrink)

The year 2012 has witnessed worldwide celebrations of Alan Turing’s 100th birthday. A great number of conferences and workshops were organized by logicians, computer scientists and researchers in AI, showing the continued flourishing of computer science, and the fruitful interfaces between logic and computer science. Logic is no longer just the concept that Frege had about one hundred years ago, let alone that of Aristotle twenty centuries before. One of the prominent features of contemporary logic is its (...) interdisciplinary character, connecting across mathematics, philosophy, modern computer science, and even the cognitive and social sciences. -/- China is no exception. This special issue explores interfaces of logic with AI and other disciplines, and it attempts to give a bird’s eye view of the developing landscape of logic and AI in China. Our contributors come from computer science, AI, philosophy and linguistics, united by their interest in using formal logical tools and notions to deal with substantial problems in their fields. We are aware that AI is a vast area, and that the limitations of space in a volume like this may provide a somewhat incomplete or biased view. Still we hope that the six papers selected here illustrate the concerns and curiosity driving Chinese researchers, on topics shared with our colleagues around the world. -/- One of the active research areas in AI is planning under uncertainty, with incomplete knowledge, where a well-established logical framework is the action language of Son and Baral (Son and Baral 2001). This language can express the notions of sensing actions and making conditional plans. However, its current semantics is not yet satisfactory, since according to the semantics an agent would not have knowledge of what happens after a plan is executed, which can be a serious problem in a safety-critical environment. In their paper Proof Systems for Planning under Cautious Semantics (Shen and Zhao 2013), Yuping Shen and Xishun Zhao address this problem and propose a cautious and weakly cautious semantics for the action language. This new semantics enables the agent to know exactly what happens after executing an action. Hoare style proof systems are then proposed and proved to be sound and complete. What is more, the proposed new system reduces the reasoning complexity of the action language. -/- In Preferential Semantics for Plausible Subsumption in Possibility Theory (Qi and Zhang 2013), Guilin Qi and Zhizheng Zhang investigate another important issue in AI: how to handle exceptions in a knowledge-based system. This has become a hot topic in recent years, especially in ontological design, where description logics are widely used. The question then arises whether we can get a nonmonotonic extension of description logic. To answer this, the authors first propose three preferential semantics for plausible subsumption in the framework of possibility theory (Dubois and Prade 1985) and study their properties and their relationships. Then they introduce preferential subsumptions to a description logic-based knowledge system, and define an entailment relation of plausible subsumption. Moreover, they study reductions of the new semantics to standard semantics for an expressive description logic. -/- Counterfactual reasoning has been explored extensively by philosophers, cf. Lewis (1973), Stalnaker (1968). But in the last two decades, causal models have been proposed for modeling counterfactual reasoning by researchers in AI. In A Lewisian Logic of Causal Counterfactuals (Zhang 2013), Jiji Zhang studies classes of causal models and provides formal characterizations. In particular, a new logic system is proposed and proved complete for the class of ‘Lewisian models’ satisfying all the principles of David Lewis’s logic of counterfactuals. Moreover, Zhang compares his new framework with those of Lewis and Stalnaker, and also establishes formal connections with more recent approaches by Galles and Pearl, and by Halpern. -/- Consistency is a fundamental notion in logic, but it also plays a key role in many techniques for solving constraint satisfaction problems (Mackworth 1977). In Variable-Centered Consistency in Model RB (Li et al. 2013), Liang Li, Tian Liu and Ke Xu start with a discussion of i-consistency in ‘Model RB’ and ‘Model RD’, two models of random constraint satisfaction problems with growing domain size, then compare it with the notion of t-consistency and strong t-consistency in well-known earlier work by Freuder. After that, the authors define a new kind of variable-centered consistency, and show an upper bound on a suitably chosen parameter in Model RB and Model RD, such that up to this bound, all typical instances of Model RB are variable-centered consistent. -/- Language understanding is a cognitive process that human beings carry out every day. Metaphor is a pervasive phenomenon in natural languages, and its cognitive role calls for interpretation. There is a growing body of literature on cognitive mechanisms underlying metaphor. The paper An Ontology-Based Approach to Metaphor Cognitive Computation (Huang et al. 2013) by Xiaoxi Huang, Huaxin Huang, Beishui Liao, and Cihua Xu proposes a theoretical framework for metaphor understanding based on recent results from cognitive science (Shutova 2010). They then introduce ontology as a knowledge representation method, and present a new ontological model to formalize metaphor-based knowledge. An algorithmic description is provided using a quantitative measure of integrated degree, and its effectiveness is demonstrated. -/- Puzzles and their solution mechanisms are a recurrent theme in many fields. Dynamic epistemic logic (van Ditmarsch et al. 2007); (van Benthem 2011) is a recent framework that can handle information dynamics in knowledge puzzles. In Reasoning About Agent Types and the Hardest Logic Puzzle Ever (Liu and Wang 2013), Fenrong Liu and Yanjing Wang propose a new dynamic-epistemic logic that can specify different types of agents and analyse agents’ knowledge, communication and reasoning. To demonstrate its power, Smullyan’s well-known Knights and Knaves puzzles are formalized, as well as the Hardest Logic Puzzle Ever (HLPE) of Boolos (Boolos 1996). Moreover, a spectrum of new puzzles is found involving knowledge-based agent types, and their solution complexity is shown to be harder than that for existing logic puzzles. It is also shown that a version of HLPE in which the agents do not know the others’ types does not have a solution at all. -/- Finally, we would like to thank the editor of Minds and Machines, Gregory Wheeler, for his efficient assistance and kind support. We also thank Ties Nijssen at Springer for his encouragement to start this project at the first place. And of course, our appreciation goes to our authors, as well as all the colleagues who have helped us review the papers in this collection. (shrink)

This paper establishes several algebraic embedding theorems, each of which asserts that a certain kind of residuated structure can be embedded into a richer one. In almost all cases, the original structure has a compatible involution, which must be preserved by the embedding. The results, in conjunction with previous findings, yield separative axiomatizations of the deducibility relations of various substructural formal systems having double negation and contraposition axioms. The separation theorems go somewhat further than earlier ones in the literature, which (...) either treated fewer subsignatures or focussed on the conservation of theorems only. (shrink)

Epistemic logic has grown from its philosophical beginnings to find diverse applications in computer science, and as a means of reasoning about the knowledge and belief of agents. This book provides a broad introduction to the subject, along with many exercises and their solutions. The authors begin by presenting the necessary apparatus from mathematics and logic, including Kripke semantics and the well-known modal logics K, T, S4 and S5. Then they turn to applications in the context of distributed (...) systems and artificial intelligence. These include the notions of common knowledge, distributed knowledge, explicit and implicit belief, the interplays between knowledge and time, and knowledge and action, as well as a graded (or numerical) variant of the epistemic operators. The authors also discuss extensively the problem of logical omniscience. They cover Halpern & Moses' theory of honest formulas, and they make a digression into the realm of nonmonotonic reasoning and preferential entailment. They discuss Moore's autoepistemic logic, together with Levesque's related logic of "all I know". Furthermore, they show how one can base default and counterfactual reasoning on epistemic logic. Graduate students in philosophy or in computer science, especially those with an interest in AI, will find this book useful. (shrink)

It is proved that the variety of relevant disjunction lattices has the finite embeddability property. It follows that Avron's relevance logic RMImin has a strong form of the finite model property, so it has a solvable deducibility problem. This strengthens Avron's result that RMImin is decidable.

This comprehensive volume features the proceedings of the Third International Workshop on Logics for New-Generation Artificial Intelligence and the International Workshop on Logic, AI and Law, held in Hangzhou, China on September 8-9 and 11-12, 2023. The collection offers a diverse range of papers that explore the intersection of logic, artificial intelligence, and law. With contributions from some of the leading experts in the field, this volume provides insights into the latest research and developments in the applications of (...) logic in these areas. It is an essential resource for researchers, practitioners, and students interested in the latest advancements in logic and its applications to artificial intelligence and law. (shrink)

The European Workshop on Logics in Artificial Intelligence was held at the Centre for Mathematics and Computer Science in Amsterdam, September 10-14, 1990. This volume includes the 29 papers selected and presented at the workshop together with 7 invited papers. The main themes are: - Logic programming and automated theorem proving, - Computational semantics for natural language, - Applications of non-classical logics, - Partial and dynamic logics.

This volume contains the proceedings of JELIA '92, les Journ es Europ ennes sur la Logique en Intelligence Artificielle, or the Third European Workshop on Logics in Artificial Intelligence. The volume contains 2 invited addresses and 21 selected papers covering such topics as: - Logical foundations of logic programming and knowledge-based systems, - Automated theorem proving, - Partial and dynamic logics, - Systems of nonmonotonic reasoning, - Temporal and epistemic logics, - Belief revision. One invited paper, by D. Vakarelov, (...) is on arrow logics, i.e., modal logics for representing graph information. The other, by L.M. Pereira,J.J. Alferes, and J.N. Apar cio, is on default theory for well founded semantics with explicit negation. (shrink)

Recent developments in AI-research suggest that an AI-driven science might not be that far off. The research of for Melnikov et al. (2018) and that of Evans et al. (2018) show that automated systems can already have a distinctive role in the design of experiments and in directing future research. Common practice in many of the papers devoted to the automation of basic research is to refer to these automated systems as ‘agents’. What is this attribution of agency based (...) on and to what extent is this an important notion in the broader context of an AI-driven science? In an attempt to answer these questions, this paper proposes a new methodological framework, introduced as the Four-Fold Framework, that can be used to conceptualize artificial agency in basic research. It consists of four modeling strategies, three of which were already identified and used by Sarkia (2021) to conceptualize ‘intentional agency’. The novelty of the framework is the inclusion of a fourth strategy, introduced as conceptual modeling, that adds a semantic dimension to the overall conceptualization. The strategy connects to the other strategies by modeling both the actual use of ‘artificial agency’ in basic research as well as what is meant by it in each of the other three strategies. This enables researchers to bridge the gap between theory and practice by comparing the meaning of artificial agency in both an academic as well as in a practical context. (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)

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

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)

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)

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)

The growing volume of urban waste poses significant environmental and economic challenges for cities worldwide. Traditional waste management systems often rely on inefficient collection routes, inadequate recycling processes, and excessive landfill usage. This paper explores how Artificial Intelligence (AI) and IoT technologies can revolutionize waste management in smart cities by enabling real-time monitoring, automated sorting, and optimized collection routes. By integrating data from smart bins, robotic sorting systems, and predictive analytics, cities can achieve zero-waste goals and promote circular economy practices. (...) Experimental results demonstrate significant improvements in waste segregation accuracy, collection efficiency, and recycling rates, offering a sustainable blueprint for urban waste management. (shrink)

Air pollution is a critical challenge for urban areas, contributing to public health crises and environmental degradation. Traditional air quality monitoring systems often lack the granularity and adaptability needed to address dynamic pollution sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance air quality management in smart cities by enabling real-time monitoring, pollution source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, satellite imagery, and traffic systems, cities can reduce pollution levels, (...) improve public health outcomes, and promote sustainable urban environments. Experimental results demonstrate significant improvements in pollution detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban air quality management. (shrink)

Noise pollution is a growing concern in urban areas, contributing to public health issues such as stress, sleep disturbances, and hearing loss. Traditional noise monitoring systems often lack the granularity and adaptability needed to address dynamic noise sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance noise pollution management in smart cities by enabling real-time monitoring, source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, traffic systems, and urban infrastructure, cities can (...) reduce noise levels, improve public health outcomes, and enhance the quality of life for residents. Experimental results demonstrate significant improvements in noise detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban noise pollution management. (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)

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)

Four instances of how science fiction contributes to the elucidation of novelty in the context of discovery are considered by extending existing discussions on temporal and use-novelty. In the first instance, science fiction takes an already well-known theory and produces its own re-interpretation; in the second instance, the scientific account is usually straightforward and whatever novelty that may occur would be more along the lines of how the science is deployed to extra-scientific matters; in the third instance, science fiction takes (...) an idea that appears impossible within the delimitations of reality and produces a universe where such a possibility becomes feasible; and in the fourth instance, science fiction extends from an idea already known at the time of the work’s production to simulate a possibility that could emerge should the extension be experimentally viable. However, the article will not end with a mere evaluation of these instances, but also proposes instances on how science fiction could contribute to new ways of experiencing, discerning, and working with scientific knowledge. (shrink)

Urban resilience is critical for ensuring the sustainability and adaptability of cities in the face of growing challenges such as climate change, population growth, and infrastructure degradation. Predictive analytics, powered by Artificial Intelligence (AI) and the Internet of Things (IoT), offers a transformative approach to enhancing urban resilience. This paper explores how AI-driven predictive analytics can optimize disaster preparedness, infrastructure maintenance, and resource allocation in smart cities. By integrating real-time data from IoT sensors with advanced machine learning models, cities (...) can proactively address vulnerabilities and improve decision-making. This study highlights key applications, challenges, and future directions for leveraging predictive analytics to build resilient urban ecosystems. (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)

Socializing students to Academic Integrity in the face of great cultural, linguistic and socioeconomic diversity in the student population in higher education calls for innovative strategies that are aligned with equity, diversity and inclusion principles. Through a mixed method of quantitative analysis of learner engagement data from the Learning Management System and analysis of anonymous evaluation survey, along with thematic analysis of students’ open-ended responses in the evaluation survey, the authors explored how students responded to AI Socialization during a 4-week (...) non-credit, online co-curricular program called ‘Reading and Writing Excellence’. Nine groups of undergraduate students from 34 disciplines in different global locations during the COVID-19 pandemic were introduced to a curated set of AI online resources. Through a learner-driven, instructor-facilitated approach the AI Socialization also engaged students in language development and empowered them to communicate about their disciplinary course topics through written journal entries, receiving instructor feedback that increased their cultural and linguistic capital for further academic writing. This approach led to a high volume of written output. Nonparametric ANOVA was used to establish that low-proficiency students were able to produce as much written output as their more proficient peers. Survey results for various aspects important to academic integrity show students’ self-perception of readiness for academic writing: paraphrasing and summarizing ; organization of ideas ; critical thinking ; logic/argument. Insights gained about educative engagement, language development and learner empowerment that can help students from diverse backgrounds to avoid Academic Integrity Violations and gain transformative access and success in higher education are incorporated into a set of recommendations that are applicable to a wide range of teaching contexts. (shrink)

The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data - driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions (...) by putting forward fuzzy rules as a Janus-faced tool that may represent knowledge, as well as approximate non-linear functions representing data. This paper lays bare logical foundations of data - driven reasoning whereby a set of formulas is understood as a set of observed facts rather than a set of beliefs. Several representation frameworks are considered from this point of view: classical logic, possibility theory, belief functions, epistemic logic, fuzzy rule-based systems. Mamdani's fuzzy rules are recovered as belonging to the data - driven view. In possibility theory a third set-function, different from possibility and necessity plays a key role in the data - driven view, and corresponds to a particular modality in epistemic logic. A bi-modal logic system is presented which handles both beliefs and observations, and for which a completeness theorem is given. Lastly, our results may shed new light in deontic logic and allow for a distinction between explicit and implicit permission that standard deontic modal logics do not often emphasize. (shrink)

Background Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are increasingly being integrated into healthcare for various purposes, including resource allocation. While these systems promise improved efficiency and decision-making, they also raise significant ethical concerns. This study aims to explore healthcare professionals’ perspectives on the ethical implications of using AI-CDSS for healthcare resource allocation. Methods We conducted semi-structured qualitative interviews with 23 healthcare professionals, including physicians, nurses, administrators, and medical ethicists in Turkey. Interviews focused on participants’ views regarding the use (...) of AI-CDSS in resource allocation, potential ethical challenges, and recommendations for responsible implementation. Data were analyzed using thematic analysis. Results Participant responses are clustered around five pre-determined thematic areas: (1) balancing efficiency and equity in resource allocation, (2) the importance of transparency and explicability in AI-CDSS, (3) shifting roles and responsibilities in clinical decision-making, (4) ethical considerations in data usage and algorithm development, and (5) balancing cost-effectiveness and patient-centered care. Participants acknowledged the potential of AI-CDSS to optimize resource allocation but expressed concerns about exacerbating healthcare disparities, the need for interpretable AI models, changing professional roles, data privacy, and maintaining individualized care. Conclusions The integration of AI-CDSS into healthcare resource allocation presents both opportunities and significant ethical challenges. Our findings underscore the need for robust ethical frameworks, enhanced AI literacy among healthcare professionals, interdisciplinary collaboration, and rigorous monitoring and evaluation processes. Addressing these challenges proactively is crucial for harnessing the potential of AI-CDSS while preserving the fundamental values of equity, transparency, and patient-centered care in healthcare delivery. (shrink)

Widespread popular discourse, at the time of writing, is centring on the capabilities of AI technologies, among others, in utilising the readily available mass of data to augment claimed educational problems. These positions often elide the unobjective nature of algorithms and the socio-politically infused assemblages of data available, situated within the neoliberalist scientism dominating educational policy discourse. The simplicity with which datafication treats education has led to a global culture of data-driven techno-rationality that affords ultra-rapid forms of free-floating control (...) settled on an ideology of dataism. Dataism rests on the assumption that sociality and subjectification can be reduced to quantifiable data whereby the student rather than being treated as a subject comes to be treated as data doppelganger. The injustices inherent in datafication and its associated epistemes ignore hidden neoliberal inequalities and maintain the insidious coloniality inherent in advanced capitalism, while simultaneously fuelling the rapid and cyclical stripping of purpose from education itself. There is a need to problematise the hidden logics of coloniality that are both maintained and reproduced within the datafication agenda. The current article draws on decolonial theory to animate the logics of datafication through a Deleuzian reading, situated within the learnification of neoliberal education. (shrink)

Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and (...) changing behaviours. This article discusses how these AI-driven systems pose particular ethical challenges with regards to nudging. To confront these issues, the value sensitive design approach is adopted as a principled methodology that designers can adopt to design these systems to avoid harming and contribute to the social good. The AI for Social Good factors are adopted as the norms constraining maleficence. In contrast, higher-order values specific to AI, such as those from the EU High-Level Expert Group on AI and the United Nations Sustainable Development Goals, are adopted as the values to be promoted as much as possible in design. The use case of Amazon Alexa's Healthcare Skills is used to illustrate this design approach. It provides an exemplar of how designers and engineers can begin to orientate their design programs of these technologies towards the social good. (shrink)

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

The purpose of this paper is to explore the epistemological tensions embedded within big data and data-driven technologies to advance a socio-political reconsideration of the public dimension in the assessment of their implementation.,This paper builds upon (and revisits) the European Union’s (EU) normative understanding of artificial intelligence (AI) and data-driven technologies, blending reflections rooted in philosophy of technology with issues of democratic participation in tech-related matters.,This paper proposes the conceptual design of sectorial and/or local-level e-participation platforms to ignite (...) an ongoing discussion – involving experts, private actors, as well as cognizant citizens – over the implementation of data-driven technologies, to avoid siloed, tech-solutionist decisions.,This paper inscribes the EU’s normative approach to AI and data-driven technologies, as well as critical work on the governance of these technologies, into a broader political dimension, suggesting a way to democratically and epistocratically opening up the decisional processes over the development and implementation of these technologies and turn such processes into a systemic civic involvement. (shrink)

As artificial intelligence (AI) systems become more prevalent, ethical inquiry into transparency, trust, and patient autonomy must develop with similar pace. One area where such inquiry required is...

Integrating artificial intelligence (AI) into healthcare can potentially revolutionize how clinical decisions are made. Advancements in AI-driven Clinical Decision Support Systems (AI_CDSS) are enh...

In artificial intelligence (AI), responses generated by machine-learning models (most often large language models) may be unfactual information presented as a fact. For example, a chatbot might state that the Mona Lisa was painted in 1815. Such phenomenon is called AI hallucinations, seeking inspiration from human psychology, with a great difference of AI ones being connected to unjustified beliefs (that is, AI “beliefs”) rather than perceptual failures). -/- AI hallucinations may have their source in the data itself, that is, the (...) source content, or in the training procedure, i.e. the way the knowledge was encoded in the model’s parameters, so that errors in encoding and decoding textual and non-textual representations can cause hallucinations. In this paper, we will observe how such errors come to life and how they might be mitigated. For this purpose, we will analyze the usability of justification logics, to behave as a proof checker for validating the correctness of large language models’ (LLM) responses. Justification logic was developed by S. Artemov, and later on mostly by Artemov and M. Fitting, deriving its main idea from the logic of proofs (LP): knowledge and belief modalities are seen as justification terms, i.e. t:X stands for t is a (proper) justification for X. Justification logic originated from attempts to create semantics for intuitionistic logic where proofs were the most proper justifications, but in further development, justification logic could be applied to different kinds of justifications). With the recent attempts to mitigate incorrect LLM responses, we will analyze various guardrails that are currently used for LLM responses, and see how the logic of justification may provide its benefits as an AI safety layer against false data. (shrink)

There is a class of expressions which are perceived as ‘ungrammatical’ not because they are syntactically ill-formed but because they have interpretations which are informationally trivial. Triviality-driven unacceptability constrains the distribution of determiners, modals, attitude verbs, exhaustifiers, approximatives, among many other classes of logical terms. At the same time, many superficial tautologies and contradictions—pre-theoretically, the clearest examples of trivial expressions—are judged to be perfectly acceptable. This paper discusses two promising yet fundamentally opposed attempts to model triviality-driven unacceptability without (...) over-generating ‘ungrammaticality’ judgments. One approach combines the ‘Logicality’ view that the language system includes a deductive-inferential system (DS) that automatically identifies and filters out expressions with trivial interpretations, with the hypothesis that the DS runs on ‘modulated logical forms’, i.e., structures where all content-based terms and variables are subject to meaning-modulation operations. The other approach downplays Logicality and tries to reduce triviality-driven unacceptability to familiar kinds of pragmatic infelicities. The Logicality-based approach, I argue, is superior to the most sophisticated attempts to treat triviality-driven unacceptability as a species of pragmatic infelicity. This result suggests that our purely linguistic and logical competences are deeply intertwined, and sheds new light on the division of labor between syntax, semantics and pragmatics. (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)

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

Artificial intelligence (AI)-based virtual influencers are now frequently used by brands in various categories to engage customers. However, little is known about who the followers of these AI-based virtual influencers are and more importantly, what drives the followers to use AI-based virtual influencers. The results from a survey support the notion that compensatory mechanisms and the need to belong play important roles in affecting usage intentions of AI-based virtual influencers. Specifically, the study finds that usage intentions are mediated and moderated (...) by compensatory mechanisms that arise from the perception of AI-based virtual influencers’ functional benefits and existential threats to human identity. Furthermore, the need for belonging moderates the effects of the following status (following versus non-following) on perceived personalization benefits of AI-based virtual influencers and behavioral intentions to use AI-based virtual influencers. This study provides important implications for academia delving into the social, cultural, and philosophical implications of AI-based virtual influencers for human societies as well as for brands that plan to use AI-based virtual influencers and gain a better understanding of their customers in AI-driven digital marketing. (shrink)

We introduce and investigate a family of consequence relations with the goal of capturing certain important patterns of data-driven inference. The inspiring idea for our framework is the fact that data may reject, possibly to some degree, and possibly by mistake, any given scientific hypothesis. There is no general agreement in science about how to do this, which motivates putting forward a logical formulation of the problem. We do so by investigating distinct definitions of ‘rejection degrees’ each yielding a (...) consequence relation. Our investigation leads to novel variations on the theme of rational consequence relations, prominent among non-monotonic logics. (shrink)

This paper explores a neglected normative dimension of algorithmic opacity in the workplace and the labor market. It argues that explanations of algorithms and algorithmic decisions are of noninstrumental value. That is because explanations of the structure and function of parts of the social world form the basis for reflective clarification of our practical orientation toward the institutions that play a central role in our life. Using this account of the noninstrumental value of explanations, the paper diagnoses distinctive normative defects (...) in the workplace and economic institutions which a reliance on AI can encourage, and which lead to alienation. (shrink)

The rapid progress of artificial intelligence (AI) has resulted in its integration into various stages of the media process, including information gathering, processing, and distribution. This integration has raised the possibility of AI dominating the media industry, leading to an era of “autonomous driving” within AI-driven media systems. Similar to the ethical dilemma known as the “trolley problem” (TP) in autonomous driving, a comparable problem arises in AI automated media. This study examines the emergence of the new TP in (...) fully automated AI-driven media (FAAIM), recognizing the complexity of this problem given the nature of media content and its societal impact. To address this complexity, we propose the adoption of the theory of “meaningful human control,” originally developed to address responsibility in the governance of lethal autonomous weapons systems, as a framework for governing FAAIM. By ensuring that humans can be held responsible for the operations of FAAIM, this paper aims to proactively confront the ethical challenges arising from FAAIM, identify potential solutions, and guide future research in media ethics. (shrink)

Social media has witnessed different eating practices, including food mukbang. Food mukbang is a type of video presentation where hosts consume large quantities of food while interacting with viewers. This study is situated on the social eating theory, which explains how people connect their individual interests with society. Though this practice has been on social media platforms for a while now, little is known about its health impact on a wide range of audiences. Unhealthy eating practices are associated with obesity (...) and other chronic diseases, which are the leading causes of death in the world today. Given that there are unhealthy eating practices in food mukbang videos on social media, we collected thirty (30) food mukbang creative common license YouTube videos, and we extracted 2297 comments from these videos to study the trends. In view of this, we found that there is a collection of more unhealthy foods eaten to entertain viewers, different unhealthy eating behaviors such as eating quickly, talking while eating, and putting too much food in the mouth. In addition, we found that the viewers’ sentiments were positive, and they loved watching these videos. Hence, in its best practice, we propose an AI-driven persuasive intervention because AI technologies are capable of detecting unhealthy eating patterns and persuasive interventions can promote a change in behavior (healthy eating behavior). Based on our findings, we present three design recommendations for YouTube and other social media designers to consider in future: (1) using artificial intelligence-related technologies for predicting videos, (2) using natural language processing to prompt feedback, and (3) auto-generating disclaimers at the beginning and end of the video. (shrink)