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)

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)

Explanation is a foundational goal in the exact sciences. Besides the contemporary considerations on ‘description’, ‘classification’, and ‘prediction’, we often see these terms in thriving applications of artificial intelligence (AI) in chemistry hypothesis generation. Going beyond describing ‘things in the world’, these applications can make accurate numerical property calculations from theoretical or topological descriptors. This association makes an interesting case for a logic of discovery in chemistry: are these induction-led ventures showing a shift in how chemists can problematize research (...) questions? In this article, I present a fresh perspective on the current context of discovery in chemistry. I argue how data-driven statistical predictions in chemistry can be explained as a quasi-logical process for generating chemical theories, beyond the classic examples of organic and theoretical chemistry. Through my position on formal models of scientific explanation, I demonstrate how the dawn of AI can provide novel insights into the explanatory power of scientific endeavors. (shrink)

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)

Abstract: Artificial Intelligence (AI) is revolutionizing organizational dynamics by reshaping both structures and processes. This paper explores how AI-driven innovations are transforming organizational frameworks, from hierarchical adjustments to decentralized decision-making models. It examines the impact of AI on various processes, including workflow automation, data analysis, and enhanced decision support systems. Through case studies and empirical research, the paper highlights the benefits of AI in improving efficiency, driving innovation, and fostering agility within organizations. Additionally, it addresses the challenges associated with (...) AI integration, such as resistance to change, ethical concerns, and data security issues. The paper concludes by offering insights into future trends and implications for organizations navigating the evolving landscape of AI-driven change. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. (...) Additionally, it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

This argument explores the transformative potential of AI-driven legislative simulations for creating inclusive, equitable, and globally adaptable laws. By using predictive modeling and adaptive frameworks, these simulations can account for diverse cultural, social, and economic contexts. The argument emphasizes the need for universal ethical safeguards, trust-building measures, and phased implementation strategies. Case studies of successful applications in governance and conflict resolution demonstrate the feasibility and efficacy of this approach. The conclusion highlights AI’s role in democratizing governance and ensuring laws (...) are more equitable and culturally sensitive. (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have revolutionized traditional educational methods (...) and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

By embracing AI-driven HR analytics, organizations can anticipate market shifts, prepare their workforce for future challenges, and stay ahead of the competition. This study outlines the essential components of AI-driven HR analytics, demonstrates its impact on workforce agility, and concludes with potential future enhancements to further optimize HR functions. Key words: Predictive Workforce Analytics, Talent Optimization, Machine Learning in.

In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make proactive decisions (...) in hiring, skill development, performance management, and retention. (shrink)

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze (...) normative ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (shrink)

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)

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)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that provide (...) personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. We also explore the ethical implications and challenges associated with deploying such systems, particularly regarding privacy concerns and the potential for misuse. Through extensive experiments, our model achieved an average accuracy rate of 92%, highlighting its robustness across different environments and user demographics. This research not only contributes to the growing field of affective computing but also lays the groundwork for future developments in emotionally intelligent systems. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework (...) achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like Mean (...) Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. (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)

Ikigai is a Japanese concept, which, in brief, refers to the “reason or purpose to live”. I-risks have been identified as a category of risks complementing x- risks, i.e., existential risks, and s-risks, i.e., suffering risks, which describes undesirable future scenarios in which humans are deprived of the pursuit of their individual ikigai. While some developments in AI increase i-risks, there are also AI-driven virtual opportunities, which reduce i-risks by increasing the space of potential ikigais, largely due to developments (...) in generative AI, virtual worlds as well as AI-driven hyper-personalization. The purpose of this paper is to present a first attempt to map the potential AI-driven virtual hyper-personalised ikigai universe. Moreover, challenges and further ideas are presented. (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)

Concerns about threats to human autonomy feature prominently in the field of AI ethics. One aspect of this concern relates to the use of AI systems for problematically manipulative influence. In response to this, the European Union’s draft AI Act (AIA) includes a prohibition on AI systems deploying subliminal techniques that alter people’s behavior in ways that are reasonably likely to cause harm (Article 5(1)(a)). Critics have argued that the term ‘subliminal techniques’ is too narrow to capture the target cases (...) of AI-based manipulation. We propose a definition of ‘subliminal techniques’ that (a) is grounded on a plausible interpretation of the legal text; (b) addresses all or most of the underlying ethical concerns motivating the prohibition; (c) is defensible from a scientific and philosophical perspective; and (d) does not over-reach in ways that impose excessive administrative and regulatory burdens. The definition is meant to provide guidance for design teams seeking to pursue responsible and ethically aligned AI innovation. (shrink)

Today, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures.

The text “The Ontology of Knowledge, Logic, Arithmetic, Set Theory, and Geometry” by Jean-Louis Boucon explores a deeply philosophical interpretation of knowledge, its logical structure, and the foundational elements of mathematical and scientific reasoning. -/- Here’s an overview condensed by an AI of the key themes and ideas, summarized into a quite general conceptual structure. These two pages are instructive on their own, but their main purpose is to facilitate the reading of the entire article, allowing the reader to (...) situate in the coherence of the whole both the meaning and the usefulness of each proposed idea. (shrink)

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

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

It is often said that ‘every logical truth is obvious’ (Quine 1970: 82), that the ‘axioms and rules of logic are true in an obvious way’ (Murawski 2014: 87), or that ‘logic is a theory of the obvious’ (Sher 1999: 207). In this chapter, I set out to test empirically how the idea that logic is obvious is reflected in the scholarly work of logicians and philosophers of logic. My approach is data-driven. That is to (...) say, I propose that systematically searching for patterns of usage in databases of scholarly works, such as JSTOR, can provide new insights into the ways in which the idea that logic is obvious is reflected in logical and philosophical practice, i.e., in the arguments that logicians and philosophers of logic actually make in their published work. (shrink)

This paper offers the first careful analysis of the possibility that AI and humanity will go to war. The paper focuses on the case of artificial general intelligence, AI with broadly human capabilities. The paper uses a bargaining model of war to apply standard causes of war to the special case of AI/human conflict. The paper argues that information failures and commitment problems are especially likely in AI/human conflict. Information failures would be driven by the difficulty of measuring AI (...) capabilities, by the uninterpretability of AI systems, and by differences in how AIs and humans analyze information. Commitment problems would make it difficult for AIs and humans to strike credible bargains. Commitment problems could arise from power shifts, rapid and discontinuous increases in AI capabilities. Commitment problems could also arise from missing focal points, where AIs and humans fail to effectively coordinate on policies to limit war. In the face of this heightened chance of war, the paper proposes several interventions. War can be made less likely by improving the measurement of AI capabilities, capping improvements in AI capabilities, designing AI systems to be similar to humans, and by allowing AI systems to participate in democratic political institutions. (shrink)

The alignment problem in artificial intelligence (AI) is a critical challenge that extends beyond the need to align future superintelligent systems with human values. This paper argues that even "merely intelligent" AI systems, built on current-gen technologies, pose existential risks due to their competence-without-comprehension nature. Current AI models, despite their advanced capabilities, lack intrinsic moral reasoning and are prone to catastrophic misalignment when faced with ethical dilemmas, as illustrated by recent controversies. Solutions such as hard-coded censorship and rule-based restrictions prove (...) inadequate, as they fail to imbue AI with a true understanding of moral complexities, making these systems vulnerable to manipulation. To address these risks, the paper explores a paradigm shift from competence to comprehension, emphasizing the importance of developing AI systems with an intrinsic value framework capable of moral reasoning. By adopting intrinsic teleology, AI could self-reflect, simulate other minds, and develop empathy, which would enable it to navigate ethical dilemmas in ways that are currently impossible. Using theories from philosophy, ethics, and AI research, the paper proposes the development of AI systems that can reason through moral dilemmas autonomously, suggesting that such systems would be safer and more alignable than current generation competence-driven AI. (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)

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

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

Cho and Martinez-Martin provide a wide-ranging analysis of what they label “digital simulacra”—which are in essence data-driven AI-based simulation models such as digital twins or models used for i...

Implicit stochastic models, including both ‘deep neural networks’ (dNNs) and the more recent unsupervised foundational models, cannot be explained. That is, it cannot be determined how they work, because the interactions of the millions or billions of terms that are contained in their equations cannot be captured in the form of a causal model. Because users of stochastic AI systems would like to understand how they operate in order to be able to use them safely and reliably, there has emerged (...) a new field called ‘explainable AI’ (XAI). When we examine the XAI literature, however, it becomes apparent that its protagonists have redefined the term ‘explanation’ to mean something else, namely: ‘interpretation’. Interpretations are indeed sometimes possible, but we show that they give at best only a subjective understanding of how a model works. We propose an alternative to XAI, namely certified AI (CAI), and describe how an AI can be specified, realized, and tested in order to become certified. The resulting approach combines ontologies and formal logic with statistical learning to obtain reliable AI systems which can be safely used in technical applications. (shrink)

Artificial Intelligence (AI) is rapidly transforming Human Resource Management (HRM) by enhancing the efficiency and effectiveness of key functions such as recruitment, performance management, and employee engagement. This paper explores the integration of AI technologies in HRM, focusing on their potential to revolutionize these critical areas. In recruitment, AI-driven tools streamline candidate sourcing, screening, and selection processes, leading to more accurate and unbiased hiring decisions. Performance management is similarly transformed, with AI enabling continuous, data-driven feedback and personalized development (...) plans that align with organizational goals. Furthermore, AI facilitates more engaging and tailored employee experiences by leveraging predictive analytics to anticipate and address employee needs. Despite these advancements, the paper also addresses the ethical considerations and challenges associated with AI in HRM, including data privacy concerns and the risk of over-reliance on automated systems. Through a comprehensive analysis, this paper aims to provide insights into how AI can be strategically leveraged to optimize HRM practices, while highlighting the need for a balanced approach that integrates human judgment with technological innovation. (shrink)

This paper explores the application of AI-driven HR analytics in shaping workforce agility, focusing on how real-time data collection, analysis, and modeling foster an adaptable workforce. It highlights the role of predictive analytics in forecasting workforce needs, identifying skill gaps, and optimizing talent deployment. Additionally, the paper discusses how AI enhances strategic decision-making by providing precise metrics and insights into employee behavior, productivity, and satisfaction. The integration of AI into HR systems ultimately shifts HR from a traditionally reactive to (...) a highly proactive, data-driven function. (shrink)

Throughout history, humans have sought spiritual experiences to find meaning, solace, or answers to life’s complexities. In religious settings, particularly in charismatic gatherings, phenomena such as people falling to the ground, rolling over, or convulsing after being touched or waved at by religious figures have been reported across many traditions. While believers may interpret these occurrences as signs of divine intervention, the underlying reasons behind these phenomena could be deeply rooted in psychological, neurological, and social factors.

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is rapidly transforming organizational dynamics and decision-making processes. This paper explores the ways in which AI enhances leadership effectiveness by providing data- driven insights, optimizing decision-making, and automating routine tasks. Additionally, it examines the challenges leaders face when adopting AI, including ethical considerations, potential biases in AI systems, and the need for upskilling. By analyzing current applications of AI in leadership and discussing future trends, this study aims to offer (...) a comprehensive overview of the opportunities AI presents for effective leadership, as well as the strategies required to address its associated challenges. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the challenges (...) businesses face when adopting AI, such as data privacy concerns, integration difficulties, and the need for skilled professionals. The paper concludes by offering recommendations for successfully implementing AI solutions to maximize benefits while mitigating potential challenges. (shrink)

Philosophers debate about which logical system, if any, is the One True Logic. This involves a disagreement concerning the sufficient conditions that may single out the correct logic among various candidates. This paper discusses whether there are necessary conditions for the correct logic; that is, I discuss whether there are features such that if a logic is correct, then it has those features, although having them might not be sufficient to single out the correct logic. (...) Traditional rationalist arguments suggest that the necessary conditions of thought are necessary and sufficient conditions singling out the correct logical and mathematical theories. In the contemporary debate, Chalmers advocates a view along this line. Jago, analogously, suggests that the necessary conditions for thought—or, as he calls them, our basic epistemic expectations—single out a family of logical and mathematical theories. Warren and Williamson, on the other hand, argue that there are no necessary conditions of thought. I argue that there are necessary conditions for thought, and these are necessary but not sufficient conditions to be the correct logic; indeed, these are features that all logics—correct or incorrect—share. No view we can understand is ruled out by the necessary conditions for thought, but we cannot understand quite any view. Human linguistic and conceptual abilities are genetically constrained, and these constraints are our best guide to the boundaries of logic. Arguing for this, I tackle two dogmas of modern rationalism: namely, the view that the biological constraints of human cognition have no bearing on the boundaries of the epistemic space, and the view that the boundaries of thought coincide with the boundaries of language. (shrink)

Tae Wan Kim, John Hooker, and Thomas Donaldson make an attempt, in recent articles, to solve the alignment problem. As they define the alignment problem, it is the issue of how to give AI systems moral intelligence. They contend that one might program machines with a version of Kantian ethics cast in deontic modal logic. On their view, machines can be aligned with human values if such machines obey principles of universalization and autonomy, as well as a deontic utilitarian (...) principle. Programming machines to do so might be useful, in their view, for applications such as future autonomous vehicles. Their proposal draws both on traditional logic-based and contemporary connectionist approaches, to fuse factual information with normative principles. I will argue that this approach makes demands of machines that go beyond what is currently feasible, and may extend past the limits of the possible for AI. I also argue that a deontological ethics for machines should place greater stress on the formula of humanity of the Kantian categorical imperative. On this principle, one ought never treat a person as a mere means. Recognition of what makes a person a person requires ethical insight. Similar insight is needed to tell treatment as a means from treatment as a mere means. The resources in Kim, Hooker, and Donaldson’s approach is insufficient for this reason. Hesitation regarding deployment of autonomous machines is warranted in light of these alignment concerns. (shrink)

Abstract: In an era of rapid technological advancements, artificial intelligence (AI) has transformed surveillance systems, enhancing security capabilities across the globe. However, the deployment of AI-driven surveillance raises significant ethical concerns, particularly in balancing the need for security with the protection of individual privacy. This paper explores the ethical challenges posed by AI surveillance, focusing on issues such as data privacy, consent, algorithmic bias, and the potential for mass surveillance. Through a critical analysis of the tension between security and (...) privacy, this paper examines the implications of AI technologies on civil liberties and human rights. It also highlights the importance of establishing clear regulatory frameworks and ethical guidelines to ensure that AI surveillance systems operate within boundaries that respect individual freedoms. In navigating this delicate balance, we propose solutions that prioritize transparency, accountability, and the protection of privacy in the digital age. (shrink)

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could (...) significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI. (shrink)

The pervasiveness of AI-empowered technologies across multiple sectors has led to drastic changes concerning traditional social practices and how we relate to one another. Moreover, market-driven Big Tech corporations are now entering public domains, and concerns have been raised that they may even influence public agenda and research. Therefore, this chapter focuses on assessing and evaluating what kind of business model is desirable to incentivise the AI for Social Good (AI4SG) factors. In particular, the chapter explores the implications of (...) this discourse for SDG #17 (global partnership) and how this goal may encourage Big Tech corporations to strengthen multi-stakeholder partnerships that promote effective public-private and civil society partnerships and the meaningful co-presence of non-market and market values. In doing so, the chapter proposes an analysis of the sociological notion of "social license to operate" (SLO) elaborated in the mining and extractive industry literature and introduces it into the discourse on sustainable digital business models and responsible management of risks in the digital age. This serves to explore how such a social license can be adopted as a practice by digital business models to foster trust, collaboration and coordination among different actors - AI researchers and initiatives, institutions and civil society at large - for the support of SDGs interrelated targets and goals. (shrink)

Abstract: Climate change presents a critical challenge that demands advanced analytical tools to predict and mitigate its impacts. This paper explores the role of artificial intelligence (AI) in enhancing climate modeling, emphasizing how AI-driven methods are revolutionizing our understanding and response to climate change. By integrating machine learning algorithms with diverse data sources such as satellite imagery, historical climate records, and real-time sensor data, AI improves the accuracy, efficiency, and granularity of climate predictions. The paper reviews key AI techniques, (...) including neural networks and ensemble models, and their applications in simulating extreme weather events and forecasting long-term climate trends. Additionally, it examines the impact of AI-enhanced climate models on policy-making and strategic planning. Despite significant advancements, challenges such as data quality, computational demands, and model interpretability remain. This study underscores the potential of AI to transform climate modeling and highlights areas for future research and development to harness these technologies for a sustainable future. (shrink)

This paper critically examines Japan's approach toward the adoption of Generative AI such as ChatGPT in education via studying media discourse and guidelines at both the national as well as local levels. It highlights the lack of consideration for socio-cultural characteristics inherent in the Japanese educational systems, such as the notion of self, teachers’ work ethics, community-centric activities for the successful adoption of the technology. We reveal ChatGPT’s infusion is likely to further accelerate the shift away from traditional notion of (...) self, which has been ongoing during past educational reforms, towards active and self-directed learning. The current discourse on Generative AI’s infusion in education is driven by neoliberal agendas aimed at increasing employability and individualism. Moreover, the introduction of ChatGPT in the context of Japan's educator culture offers potential solutions to address issues of overwork but also necessitates considerations regarding increased responsibilities for teachers and the preservation of their central role in student education. Finally, the irreplicable social growth fostered by community-centric activities in the Japanese educational system highlights the limitations of ChatGPT in replicating dynamic social interactions, emphasizing the importance of holistic approaches to education beyond technological integration. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

One of the primary, if not most critical, difficulties in the design and implementation of autonomous systems is the black-boxed nature of the decision-making structures and logical pathways. How human values are embodied and actualised in situ may ultimately prove to be harmful if not outright recalcitrant. For this reason, the values of stakeholders become of particular significance given the risks posed by opaque structures of intelligent agents (IAs). This paper explores how decision matrix algorithms, via the belief-desire-intention model for (...) autonomous vehicles, can be designed to minimize the risks of opaque architectures. Primarily through an explicit orientation towards designing for the values of explainability and verifiability. In doing so, this research adopts the Value Sensitive Design (VSD) approach as a principled framework for the incorporation of such values within design. VSD is recognized as a potential starting point that offers a systematic way for engineering teams to formally incorporate existing technical solutions within ethical design, while simultaneously remaining pliable to emerging issues and needs. It is concluded that the VSD methodology offers at least a strong enough foundation from which designers can begin to anticipate design needs and formulate salient design flows that can be adapted to the changing ethical landscapes required for utilisation in autonomous vehicles. (shrink)

The landscape of cybersecurity is constantly evolving, with adversaries becoming increasingly sophisticated and persistent. This manuscript explores the utilization of artificial intelligence (AI) to address these evolving threats, focusing on the journey from threat detection to autonomous response. By examining AI-driven detection methodologies, advanced threat analytics, and the implementation of autonomous response systems, this paper provides insights into how organizations can leverage AI to strengthen their cybersecurity posture against modern threats. Key words: Ransomware, Anomaly Detection, Advanced Persistent Threats (APTs), (...) Automated Threat Response and Artificial Intelligence. (shrink)

On 8th August 2019, Secretary of State for Health and Social Care, Matt Hancock, announced the creation of a £250 million NHS AI Lab. This significant investment is justified on the belief that transforming the UK’s National Health Service (NHS) into a more informationally mature and heterogeneous organisation, reliant on data-based and algorithmically-driven interactions, will offer significant benefit to patients, clinicians, and the overall system. These opportunities are realistic and should not be wasted. However, they may be missed (one (...) may recall the troubled Care.data programme) if the ethical challenges posed by this transformation are not carefully considered from the start, and then addressed thoroughly, systematically, and in a socially participatory way. To deal with this serious risk, the NHS AI Lab should create an Ethics Advisory Board and monitor, analyse, and address the normative and overarching ethical issues that arise at the individual, interpersonal, group, institutional and societal levels in AI for healthcare. (shrink)