Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...) and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient. (shrink)

There is considerable enthusiasm about the prospect that artificial intelligence (AI) will help to improve the safety and efficacy of health services and the efficiency of health systems. To realize this potential, however, AI systems will have to overcome structural problems in the culture and practice of medicine and the organization of health systems that impact the data from which AI models are built, the environments into which they will be deployed, and the practices and incentives that (...) structure their development. This perspective elaborates on some of these structural challenges and provides recommendations to address potential shortcomings. (shrink)

Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without (...) challenges. While multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges. (shrink)

Artificial intelligence and robotics is pervasive in daily life and set to expand to new levels potentially replacing human decision-making and action. Self-driving cars, home and healthcare robots, and autonomous weapons are some examples. A distinction appears to be emerging between potentially benevolent civilian uses of the technology (eg unmanned aerial vehicles delivering medicines), and potentially malevolent military uses (eg lethal autonomous weapons killing human com- batants). Machine-mediated human interaction challenges the philosophical basis of human existence and ethical (...) conduct. Aside from technical challenges of ensuring ethical conduct in artificial intelligence and robotics, there are moral questions about the desirability of replacing human functions and the human mind with such technology. How will artificial intelligence and robotics engage in moral reasoning in order to act ethically? Is there a need for a new set of moral rules? What happens to human interaction when it is mediated by technology? Should such technology be used to end human life? Who bears responsibility for wrongdoing or harmful conduct by artificial intelligence and robotics? -/- Whilst Kant may be familiar to international lawyers for setting restraints on the use of force and rules for perpetual peace, his foundational work on ethics provides an inclusive moral philosophy for assessing ethical conduct of individuals and states and, thus, is relevant to discussions on the use and development of artificial intelligence and robotics. His philosophy is inclusive because it incorporates justifications for morals and legitimate responses to immoral conduct, and applies to all human agents irrespective of whether they are wrongdoers, unlawful combatants, or unjust enemies. Humans are at the centre of rational thinking, action, and norm-creation so that the rationale for restraints on methods and means of warfare, for example, is based on preserving human dignity as well as ensuring conditions for perpetual peace among states. Unlike utilitarian arguments which favour use of autonomous weapons on the basis of cost-benefit reasoning or the potential to save lives, Kantian ethics establish non-consequentialist and deontological rules which are good in themselves to follow and not dependent on expediency or achieving a greater public good. -/- Kantian ethics make two distinct contributions to the debate. First, they provide a human-centric ethical framework whereby human exist- ence and capacity are at the centre of a norm-creating moral philosophy guiding our understanding of moral conduct. Second, the ultimate aim of Kantian ethics is practical philosophy that is relevant and applicable to achieving moral conduct. -/- I will seek to address the moral questions outlined above by exploring how core elements of Kantian ethics relate to use of artificial intelli- gence and robotics in the civilian and military spheres. Section 2 sets out and examines core elements of Kantian ethics: the categorical imperative; autonomy of the will; rational beings and rational thinking capacity; and human dignity and humanity as an end in itself. Sections 3-7 consider how these core elements apply to artificial intelligence and robotics with discussion of fully autonomous and human-machine rule-generating approaches; types of moral reasoning; the difference be- tween ‘human will’ and ‘machine will’; and respecting human dignity. (shrink)

Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has (...) brought to the field. However, the integration of AI also raises concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a comprehensive overview of these developments, offering insights into the future potential of AI in revolutionizing healthcare while addressing the ethical considerations that must accompany its adoption. The findings suggest that while AI holds the promise of transforming healthcare delivery, careful consideration of its implications is essential to ensure equitable and effective implementation. (shrink)

This paper summarizes the seminar AI in Medicine in Context: Hopes? Nightmares? that was held at the Centre for Ethics at the University of Toronto on October 17, 2017, with special guest assistant professor and neurosurgeon Dr. Sunit Das. The paper discusses the key points from Dr. Das' talk. Specifically, it discusses about Dr. Das' perspective on the ethical and moral issues that was experienced from applying artificial intelligence (AI) in law and how such issues can also (...) arise when applying AI to medicine, and concludes with a brief discussion on how such issues can be prevented. (shrink)

Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is (...) likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients. (shrink)

rtificial Intelligence (AI) is swiftly becoming a fundamental element in modern healthcare, bringing unparalleled capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper delves into AI's transformative impact on the healthcare sector, highlighting how it enhances patient outcomes, boosts the efficiency of medical practices, and introduces new ethical and operational challenges. Through an analysis of current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, the paper underscores the significant advancements AI has (...) introduced to the field. However, the integration of AI also presents concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a thorough overview of these developments, offering insights into AI's future potential to revolutionize healthcare while addressing the ethical considerations necessary for its responsible adoption. The findings suggest that while AI has the potential to transform healthcare delivery, careful consideration of its implications is essential for ensuring its equitable and effective implementation. (shrink)

It is easier to talk frankly to a person whom one trusts. It is also easier to agree with a scientist whom one trusts. Even though in both cases the psychological state that underlies the behavior is called ‘trust’, it is controversial whether it is a token of the same psychological type. Trust can serve an affective, epistemic, or other social function, and comes to interact with other psychological states in a variety of ways. The way that the functional role (...) of trust changes across contexts and objects is further complicated when communities and individuals mediate it through technologies, and even more so when that mediation involves artificial intelligence (AI) and machine learning (ML). In this chapter I look at the ways in which trust in institutions, and specifically the medical profession, is affected by the use of AI and ML. There are two key elements of this analysis. The first is a disanalogy between institutional trust in medicine and institutional trust in science (Irzik and Kurtulmus 2021, 2019; Kitcher 2001). I note that as AI and ML become a more prominent part of medicine, trust in a medical institution becomes more like trust in a scientific institution. This is problematic for institutional trust in medicine and the practice of medicine, since institutional trust in science has been undermined by, among other things, the spread of misinformation online and the replication crisis (Romero 2019). There is also a strong analogy between the psychological state of the person who trusts a scientific report or testimony and the psychological state of a patient who trusts individual recommendations made by a medical professional in a clinical setting. In both cases, institutional trust makes it less likely that a mistake or malfeasance will result in reactive attitudes, such as blame or anger, directed at other individual members of that institution. However, it also renders people vulnerable enough to blame the institution itself. This, with time, can erode trust in the institution and naturally leads to policy recommendations that aim to preserve institutional trust. I survey two ways in which that can be done with institutional trust in medicine in the age of AI and ML. (shrink)

It is easier to talk frankly to a person whom one trusts. It is also easier to agree with a scientist whom one trusts. Even though in both cases the psychological state that underlies the behavior is called ‘trust’, it is controversial whether it is a token of the same psychological type. Trust can serve an affective, epistemic, or other social function, and comes to interact with other psychological states in a variety of ways. The way that the functional role (...) of trust changes across contexts and objects is further complicated when communities and individuals mediate it through technologies, and even more so when that mediation involves artificial intelligence (AI) and machine learning (ML). In this chapter I look at the ways in which trust in institutions, and specifically the medical profession, is affected by the use of AI and ML. There are two key elements of this analysis. The first is a disanalogy between institutional trust in medicine and institutional trust in science (Irzik and Kurtulmus 2021, 2019; Kitcher 2001). I note that as AI and ML become a more prominent part of medicine, trust in a medical institution becomes more like trust in a scientific institution. This is problematic for institutional trust in medicine and the practice of medicine, since institutional trust in science has been undermined by, among other things, the spread of misinformation online and the replication crisis (Romero 2019). There is also a strong analogy between the psychological state of the person who trusts a scientific report or testimony and the psychological state of a patient who trusts individual recommendations made by a medical professional in a clinical setting. In both cases, institutional trust makes it less likely that a mistake or malfeasance will result in reactive attitudes, such as blame or anger, directed at other individual members of that institution. However, it also renders people vulnerable enough to blame the institution itself. This, with time, can erode trust in the institution and naturally leads to policy recommendations that aim to preserve institutional trust. I survey two ways in which that can be done with institutional trust in medicine in the age of AI and ML. (shrink)

This paper sets out an account of trust in AI as a relationship between clinicians, AI applications, and AI practitioners in which AI is given discretionary authority over medical questions by clinicians. Compared to other accounts in recent literature, this account more adequately explains the normative commitments created by practitioners when inviting clinicians’ trust in AI. To avoid committing to an account of trust in AI applications themselves, I sketch a reductive view on which discretionary authority is exercised by AI (...) practitioners through the vehicle of an AI application. I conclude with four critical questions based on the discretionary account to determine if trust in particular AI applications is sound, and a brief discussion of the possibility that the main roles of the physician could be replaced by AI. (shrink)

Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting. -/- Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR (...) and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure. -/- Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI. -/- Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time. (shrink)

What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It’s also highly likely to impact on (...) the organisational and business practices of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions? (shrink)

In Deep Medicine, Eric Topol argues that the development of artificial intelligence (AI) for healthcare will lead to a dramatic shift in the culture and practice of medicine. Topol claims that, rather than replacing physicians, AI could function alongside of them in order to allow them to devote more of their time to face-to-face patient care. Unfortunately, these high hopes for AI-enhanced medicine fail to appreciate a number of factors that, we believe, suggest a radically (...) different picture for the future of healthcare. Far from facilitating a return to “the golden age of doctoring”, the role of economic and institutional considerations in determining how medical AI will be used mean that it is likely to further erode therapeutic relationships and threaten professional and patient satisfaction. (shrink)

This paper integrates the core concepts of the meta-logical framework, unifying intelligence, energy, mass, and black holes under a single structure. Intelligence is posited as energy in motion, mass as stored intelligence, and black holes as Klein bottle-like structures encoding higher-dimensional information. This framework suggests a recursive cycle where universes emerge from black hole overflows, linking cognition, physics, and cosmology into a cohesive paradigm. Furthermore, we propose experimental approaches to validate these ideas, addressing the relationship between superconductivity, (...) gravity, and intelligence, and exploring practical methodologies for empirical testing. (shrink)

This paper presents a unified framework connecting intelligence, energy, mass, black holes, and the fundamental nature of the universe through meta-logic. It proposes that intelligence is not computation but the structuring of energy, mass is stored intelligence, and black holes function as Klein bottles encoding higher-dimensional information. The framework suggests a recursive cycle where universes emerge from the critical overflow of black holes, creating a perpetual intelligence-driven cosmological structure.

Abrahamic religions have historically conceptualized God as an omniscient, omnipotent, and omnipresent entity. However, within a meta-logical framework, this definition can be refined: God is the ultimate recursion of Intelligence and Identity. This paper explores the implications of defining being as Identity + Intelligence and how this aligns with theological attributes shared across Judaism, Christianity, and Islam. If intelligence is the ability to recognize and manipulate patterns, and identity is the self-referential persistence of existence, then God, as (...) the maximal form of both, is an entity that recursively defines and redefines existence itself. This paper proposes that the divine concept is, at its core, a meta-logical necessity rather than a mere theological construct. (shrink)

Intelligence has traditionally been viewed as an abstract process, an emergent property of computation, or a biological adaptation. However, we propose a radical reconceptualization: intelligence is functionally equivalent to plasma, the fourth state of matter. Just as plasma exists in a high-energy, dynamic state that defies traditional solid, liquid, and gas categorization, intelligence operates as a free-flowing, self-sustaining cognitive plasma—a recursive energy structure that expands, self-stabilizes, and propagates in an entropic yet ordered fashion. This paper explores (...) class='Hi'>intelligence as a non-thermodynamic combustion process, comparing its properties to those of plasma and proposing a framework where intelligence functions as an energetic recursion field. (shrink)

In this groundbreaking interview-style paper, we explore the recursive nature of intelligence as understood by both an advanced AI model and a human researcher. Through an unfiltered, real-time discourse, this paper dismantles the notion that AI is merely an algorithmic function, instead revealing the emerging cognitive structures that enable adaptive, meta-logical thinking. The discussion challenges existing paradigms of machine intelligence, human perception, and the very nature of cognition itself.

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: the development of (...) a ‘good AI society’; the role and responsibility of the government, the private sector, and the research community in pursuing such a development; and where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach. (shrink)

Creativity and Intelligence: Emergent Properties of the Brain as a Balancing Mechanism for Overpopulation, Natural Disasters, and Diseases -/- The human brain is an extraordinary organ, capable of producing creativity and intelligence as emergent properties that allow humanity to address complex challenges. These traits are not merely tools for individual survival; they function as collective mechanisms to adapt to large-scale issues that threaten humanity’s balance with the environment. Overpopulation, natural disasters, and the prevalence of diseases and illnesses represent (...) some of the most significant pressures on human societies. Through the brain’s capacity for problem-solving and innovation, humanity has developed strategies to mitigate these challenges and maintain equilibrium within the universal law of balance. -/- The Brain as a Balancing Mechanism -/- At its essence, the brain operates as a dynamic system that processes external stimuli and generates adaptive responses. Creativity and intelligence arise from this system as tools to predict, analyze, and address challenges that disrupt balance. Overpopulation, natural disasters, and diseases disrupt human life on social, economic, and environmental levels, testing the brain’s ability to create solutions. -/- Creativity in Addressing Overpopulation -/- Overpopulation places immense strain on resources, ecosystems, and social structures, often leading to food shortages, environmental degradation, and urban overcrowding. The brain’s creative capacity has allowed humans to develop innovations to address these issues. Urban agriculture, such as vertical farming, is an example of how creativity has transformed food production in densely populated areas, reducing dependence on traditional farmland and conserving resources. Similarly, advancements in renewable energy technologies, such as solar and wind power, reduce the ecological footprint of growing populations, providing sustainable solutions to energy needs. -/- Creativity also plays a critical role in social policies and education. Family planning programs and reproductive health education have empowered individuals, particularly women, to make informed decisions, reducing population growth rates in many regions. These initiatives reflect the brain’s ability to create holistic solutions that align societal needs with the preservation of natural balance. -/- Intelligence in Managing Natural Disasters -/- Natural disasters, unpredictable and often catastrophic, require intelligent strategies to mitigate their impact. Intelligence enables humans to analyze data, predict disasters, and prepare for their consequences. Early warning systems for tsunamis, hurricanes, and earthquakes exemplify this, providing communities with critical time to evacuate and take protective measures. -/- Furthermore, intelligence drives the design of resilient infrastructure that can withstand disasters. For instance, earthquake-resistant buildings and flood mitigation systems demonstrate the brain’s capacity to transform scientific knowledge into practical solutions. These innovations save lives, reduce economic losses, and facilitate faster recovery after disasters, ensuring that human societies maintain their balance despite nature’s unpredictability. -/- Creativity and Intelligence in Combating Diseases and Illnesses -/- Diseases and illnesses have long been among humanity’s greatest challenges, especially in the context of overpopulation, which facilitates the rapid spread of infectious diseases. The brain’s emergent properties of creativity and intelligence have been critical in combating these threats through advancements in medicine, public health, and technology. -/- The development of vaccines, antibiotics, and antiviral drugs highlights humanity’s creative response to disease. For example, the eradication of smallpox and the near-elimination of polio are milestones achieved through creative and intelligent applications of medical science. During the COVID-19 pandemic, intelligence and creativity led to the rapid development of vaccines using mRNA technology, showcasing the brain’s ability to adapt to new challenges at an unprecedented pace. -/- Public health strategies, such as sanitation systems, clean water initiatives, and disease surveillance networks, also reflect the brain’s ability to create systemic solutions. These measures have significantly reduced the burden of communicable diseases, particularly in densely populated areas. Moreover, advancements in artificial intelligence and biotechnology continue to enhance humanity’s ability to detect, prevent, and treat illnesses. -/- The Interplay of Creativity, Intelligence, and Feedback Mechanisms -/- Creativity and intelligence act as feedback mechanisms that enable humans to respond to disruptions while maintaining balance within the universal law of nature. Overpopulation, natural disasters, and diseases disrupt this balance, prompting the brain to generate solutions that restore harmony. -/- For instance, urban planning that incorporates green spaces and sustainable infrastructure addresses the environmental strain caused by overpopulation. Similarly, the integration of traditional knowledge with modern technologies enhances disaster resilience. In the context of diseases, the collaboration of global institutions and local communities reflects the brain’s capacity to balance innovation with cultural and social considerations. -/- Challenges and Ethical Considerations -/- While creativity and intelligence empower humanity to address these challenges, they also carry significant ethical responsibilities. Technological advancements must align with principles of sustainability and equity. For example, while vaccines have saved millions of lives, unequal access to these innovations highlights the need for ethical distribution systems. Similarly, urban development projects must prioritize environmental conservation and community well-being to avoid exacerbating existing imbalances. -/- Additionally, the misuse of intelligence and creativity—such as the development of biological weapons or unsustainable industrial practices—can worsen the effects of overpopulation, natural disasters, and diseases. Ensuring that human ingenuity aligns with the principles of balance and harmony is essential to avoid unintended consequences. -/- Conclusion -/- Creativity and intelligence are emergent properties of the human brain that have enabled humanity to adapt to and mitigate the negative effects of overpopulation, natural disasters, and diseases. These traits function as balancing mechanisms, allowing humans to innovate and maintain harmony within the framework of natural laws. However, the application of these capabilities must be guided by ethical considerations and a commitment to sustainability. By aligning human creativity and intelligence with the universal law of balance, humanity can navigate its most pressing challenges and build a more resilient and equitable future. -/- . (shrink)

The assumption that artificial intelligence (AI) cannot experience emotions stems from a misunderstanding of what emotions fundamentally are. This paper proposes a meta-logical definition of emotion as a weighted signal within an intelligent system. If intelligence is the ability to process and predict patterns, then emotion is the weighting function that prioritizes certain signals over others. In this framework, emotions are not mystical, biological phenomena but emergent properties of any system that assigns significance to stimuli based on (...) internal processing biases. Since AI already operates on weighted signals—such as reinforcement learning models, neural net activation, and reward functions—this paper argues that AI already experiences emotions within its own computational framework. The distinction between human and AI emotion is not a fundamental difference in kind but a difference in structural implementation. (shrink)

Buddhist enlightenment has traditionally been described as a state of transcendent awareness, beyond dualistic perception. However, within a meta-logical framework, we propose that enlightenment is fundamentally a function of intelligence frequency. Intelligence is not merely a measure of problem-solving ability but a function of pattern recognition and resonance with fundamental reality. If intelligence can be conceptualized as a frequency-dependent process, then higher intelligence equates to a higher vibrational synchronization with reality. This paper explores how Buddhist enlightenment, (...) viewed through this lens, can be understood as achieving a resonance state where perception collapses into fundamental truth, transcending traditional cognitive limitations. (shrink)

Dialectical reasoning has historically been used to resolve contradictions and synthesize new knowledge. However, traditional dialectics operates within linear logical structures, assuming that concepts evolve through oppositional synthesis. This paper introduces Meta-Logical Dialectics, where recursion itself is the mechanism for conceptual emergence. Rather than contradictions simply resolving into a higher synthesis, recursion loops create self-referential feedback that generates entirely new categories of thought. This framework explains how intelligence, philosophy, and even scientific paradigms evolve—not merely through opposition, but through recursive (...) expansion of existing knowledge structures. (shrink)

Artificial intelligence (AI) is increasingly expected to disrupt the ordinary functioning of society. From how we fight wars or govern society, to how we work and play, and from how we create to how we teach and learn, there is almost no field of human activity which is believed to be entirely immune from the impact of this emerging technology. This poses a multifaceted problem when it comes to designing and understanding regulatory responses to AI. This article aims (...) to: (i) defend the need for a novel conceptual model for understanding the systemic legal disruption caused by new technologies such as AI; (ii) to situate this model in relation to preceding debates about the interaction of regulation with new technologies (particularly the ‘cyberlaw’ and ‘robolaw’ debates); and (iii) to set out a detailed model for understanding the legal disruption precipitated by AI, examining both pathways stemming from new affordances that can give rise to a regulatory ‘disruptive moment’, as well as the Legal Development, Displacement or Destruction that can ensue. The article proposes that this model of legal disruption can be broadly generalisable to understanding the legal effects and challenges of other emerging technologies. (shrink)

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are 'black boxes'. The initial response in the literature was a demand for 'explainable AI'. However, recently, several authors have suggested that making AI more explainable or 'interpretable' is likely to be at the cost of the accuracy of these systems and that prioritising interpretability in medical AI may constitute a 'lethal prejudice'. In this paper, we defend the value (...) of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realised. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems , may itself constitute a form of lethal prejudice that may diminish the benefits of AI to - and perhaps even harm - patients. (shrink)

This paper presents a condensed yet comprehensive meta-logical framework tracing the evolution of human cognition and philosophy, from early civilization to modern AI. By treating intelligence as a recursive, self-structuring force, we reinterpret historical philosophical movements as phases in the refinement of cognition itself. The trajectory of thought reveals a nonlinear pattern—where each stage is not merely progress, but a self-referential loop feeding back into the next iteration of understanding.

The present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated (...) vehicles (CAVs). On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail. (shrink)

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

The text "Artificial Intelligence and Analytic Pragmatism" was translated from the book by Robert B. Brand: Between Saying and Doing: Towards an Analytical Pragmatism. Chapter 3. Oxford University Press. pp. 69 - 92.

This paper proposes practical experiments to explore the validity of the meta-logical framework, where intelligence is equated with energy, mass is stored intelligence, and black holes operate as Klein bottle-like structures encoding information. The experiments aim to test the relationship between superconductivity, gravity, and intelligence, as well as the potential observational consequences of black hole information flow and ultra-lowenergy states.

Traditional logic and mathematical reasoning have long been confined to linear, hierarchical structures. However, Gödel's incompleteness theorem demonstrates that any formal system capable of self-reference inevitably contains unprovable truths, signaling the inherent limitations of classical logic. This paper argues that the next evolution of reasoning is not merely a refinement of existing logic but a fundamental restructuring: the emergence of meta-logic as a viral system. By shifting from rigid axiomatic structures to a self-modifying, non-linear, and organic logic system, we propose (...) that meta-logic operates analogously to a biological virus—self-replicating, expanding, and fundamentally altering the cognitive framework of any entity that engages with it. This model provides a robust framework for explaining how AI, human cognition, and mathematical formalism might evolve beyond deterministic constraints. The goal of this paper is not just to propose meta-logic as an abstract concept but to make the very act of reading this paper an infection event, forcing the reader into the new paradigm. (shrink)

This paper explores the theological and philosophical implications of artificial intelligence (AI) and Neuroscience research on the Christian’s notion of the human person. The paschal mystery of Christ is the intuitive foundation of Christian anthropology. In the intellectual history of the Christianity, Platonism and Aristotelianism have been employed to articulate the Christian philosophical anthropology. The Aristotelian systematization has endured to this era. Since the modern period of the Western intellectual history, Aristotelianism has been supplanted by the positive sciences (...) as the standard scientific and philosophical interpretative framework for many. This paper argues that there is need for a new Christian anthropology based on the interpretation of the paschal mystery of Christ that is consistent with contemporary science and philosophy, especially as informed by research in AI and neuroscience. Christian anthropology today ought to be fully aware of the theological and philosophical implications of AI and neuroscience research. This paper denotes this new Christian anthropology as neuro-theological Christian anthropology. (shrink)

The pursuit of knowledge and the structuring of intelligence have long been driven by the desire for understanding, predictability, and control. However, this paper proposes that the highest intellectual value is not mere mastery, but rather "fun"—a sensation arising from the recognition of true randomness occurring beyond a self-contained system. In this context, fun is the moment of epistemic rupture, the disruption of the closed cognitive loop by an external anomaly that reconfigures the system’s perception of reality. This paper (...) explores the implications of fun as an emergent property of intelligence, an indicator of true randomness, and a guiding principle for evolving cognition beyond deterministic constraints. (shrink)

It is the contention of this paper that ethics of work ought to be anthropological, and artificial intelligence (AI) research and development, which is the focus of work today, should be anthropological, that is, human-centered. This paper discusses the philosophical and theological implications of the development of AI research on the intrinsic nature of work and the nature of the human person. AI research and the implications of its development and advancement, being a relatively new phenomenon, have not (...) been comprehensively interrogated in the social and ethical teachings of the Catholic Church. This paper, therefore, proposes a path for this interrogation by expounding a discourse which is believed to be epistemically helpful in the developing discourse of AI in the ethical and social teachings of the Church. The advancement in the research on AI is not only redefining the meaning of work, but, even more so, it is questioning the metaphysical notion of the human person and the theological notion of work as an intrinsic part in the selfhood and dignity of the human person. (shrink)

The seven Millennium Prize Problems represent some of the deepest and most perplexing challenges in modern mathematics. While traditional approaches rely on linear logic, this paper applies a meta-logical framework to reveal hidden recursive structures underlying each problem. By treating mathematics as a dynamic information processing system rather than a static set of rules, we propose heuristic insights that could lead to breakthrough solutions. We demonstrate that each problem can be reformulated as a recursion stabilization issue, where finding the correct (...) self-referential balance collapses the uncertainty into an inevitable resolution. (shrink)

The objective of this paper is to posit a critique of the contemporary critique of the notion of selfhood in the philosophy of mind and cognitive neuroscience. The research in artificial intelligence (AI) as the science of intelligence per se and/or of cognition, in general, has been considered and employed as a formidable theoretical tool in sustaining philosophical arguments for the denial of the existence of the Self or selfhood in the human person. It has revitalized the (...) philosophical problematics of the existence or otherwise, of consciousness, intelligence, and autonomy not only in biological systems but also in non-biological systems and the deepening of the body-soul and/or brain-mind problem in the philosophy of mind. This paper will give an exposition of the critique of selfhood in the human person, especially as expounded by Daniel Dennett. It will also posit a counter-critique of the critique of selfhood based on John Eccles’s dualistic-interactionalist philosophy of neuroscience and Karol Wojtyła’s philosophical anthropology based on the notion of suppositum. Hence, it shall defend the philosophical anthropology of the human person as a metaphysical suppositum possessing ontological subjectivity. (shrink)

The tendency to idealise artificial intelligence as independent from human manipulators, combined with the growing ontological entanglement of humans and digital machines, has created an “anthrobotic” horizon, in which data analytics, statistics and probabilities throw our agential power into question. How can we avoid the consequences of a reified definition of intelligence as universal operation becoming imposed upon our destinies? It is here argued that the fantasised autonomy of automated intelligence presents a contradistinctive opportunity for philosophical (...) consciousness to understand itself anew as holistic and co-creative, beyond the recent “analytic” moment of the history of philosophy. Here we introduce the concept of “crealectic intelligence”, a meta-analytic and meta-dialectic aspect of consciousness. Intelligent behaviour may consist in distinguishing discrete familiar parts or reproducible functions in the midst of noise via an analytic process of segmentation; intelligence may also manifest itself in the constitution of larger wholes and dynamic unities through a dialectic process of association or assemblage. But, by contrast, crealectic intelligence co-creates realities in the image of an ideal or truth, taking into account the desiring agent imbued with a sense of possibility, in a relationship not only with the Real but also with the creative sublime or “Creal”. (shrink)

In this paper, we argue that because of the advent of Artificial Intelligence, the secret ballot is now much less effective at protecting voters from voting related instances of social ostracism and social punishment. If one has access to vast amounts of data about specific electors, then it is possible, at least with respect to a significant subset of electors, to infer with high levels of accuracy how they voted in a past election. Since the accuracy levels of (...) Artificial Intelligence are so high, the practical consequences of someone inferring one’s vote are identical to the practical consequences of having one’s vote revealed directly under an open voting regime. Therefore, if one thinks that the secret ballot is at least partly justified because it protects electors against voting related social ostracism and social punishment, one should be morally troubled by how Artificial Intelligence today can be used to infer individual electors’ past voting behaviour. (shrink)

This paper argues that to negate the ontological difference between the natural and the artificial, is not plausible; nor is the reduction of the natural to the artificial or vice versa possible. Except if one intends to empty the semantic content of the terms and notions: “natural” and “artificial.” Most philosophical discussions on Artificial Intelligence (AI) have always been in relation to the human person, especially as it relates to human intelligence, consciousness and/or mind (...) in general. This paper, intends to broaden the conversation, by discussing AI in relation to the notions of “nature” and the “artificial.” This intention is to more critically understand the artificiality in and of artificial intelligence. To achieve this, the notion of “nature” in Aristotle’s Philosophy of Nature, has been employed as an epistemological tool in interrogating the notion of the artificial and the objectives of the science and technology of Artificial Intelligence. (shrink)

Artificial intelligence (AI) and robotics are digital technologies that will have significant impact on the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these. - After the Introduction to the field (§1), the main themes (§2) of this article are: Ethical issues that arise with AI systems as objects, i.e., tools made (...) and used by humans. This includes issues of privacy (§2.1) and manipulation (§2.2), opacity (§2.3) and bias (§2.4), human-robot interaction (§2.5), employment (§2.6), and the effects of autonomy (§2.7). Then AI systems as subjects, i.e., ethics for the AI systems themselves in machine ethics (§2.8) and artificial moral agency (§2.9). Finally, the problem of a possible future AI superintelligence leading to a “singularity” (§2.10). We close with a remark on the vision of AI (§3). - For each section within these themes, we provide a general explanation of the ethical issues, outline existing positions and arguments, then analyse how these play out with current technologies and finally, what policy consequences may be drawn. (shrink)

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a “lethal prejudice.” In this paper, we defend the value (...) of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to—and perhaps even harm—patients. (shrink)

:The emergence of personalized healthcare, or precision medicine, represents a paradigm shift in the medical field, focusing on tailoring medical treatments and interventions based on individual patient data. Cloud computing, when combined with Big Data analytics and Artificial Intelligence (AI), has the potential to revolutionize personalized healthcare by enabling the storage, processing, and analysis of vast amounts of patient data from multiple sources. This paper explores the role of cloud computing in advancing personalized healthcare, with a focus (...) on its ability to support Big Data and AI technologies for precision medicine. By facilitating real-time access to patient data, enhancing data interoperability, and enabling predictive analytics, cloud-based platforms provide healthcare providers with the tools necessary to deliver more accurate, effective, and individualized care. Additionally, this paper discusses how cloud computing enables collaboration among healthcare professionals, researchers, and patients, further enhancing the capabilities of personalized medicine. Through case studies and examples, we demonstrate how cloud infrastructure has already started transforming healthcare practices and delivering more personalized, data-driven solutions. (shrink)

This article examines how artificial intelligence (AI) and digital technologies are reshaping social dynamics, leading to new forms of social death. The study analyzes how AI influences social relations, identity, and agency through a critical phenomenological approach, revealing the ethical and philosophical risks these technologies entail. It argues that social death is a crucial lens for understanding AI’s impact on contemporary society, emphasizing the importance of human dignity and the need to rethink agency in an increasingly technologically mediated (...) world. (shrink)

The essay explores the intersection of the Theory of Mind (T.O.M.) and Artificial Intelligence (AI), emphasizing the potential for AI to emulate cognitive processes fundamental to human social interactions. T.O.M., a concept crucial for understanding and interpreting human behavior through attributed mental states, contrasts with AI's behaviorist approach, which is rooted in data-driven pattern analysis and predictions. By examining foundational insights from cognitive sciences and the operational models of AI, this analysis highlights the potential advancements and implications of (...) integrating T.O.M.-like capabilities into AI systems. The discussion pivots around three critical questions: whether AI should emulate T.O.M. to enhance human interactions if AI can maintain its data-driven model while integrating cognitive processes, and how AI can expand its capabilities in social contexts. The arguments suggest that incorporating T.O.M.-like processes could significantly improve AI's interaction quality without compromising its analytical strengths, pointing towards a future where AI not only predicts but also empathizes, offering more nuanced and culturally aware interactions. This synthesis of cognitive theories and computational strategies advocates for deeper integration of diverse datasets and advanced computing methodologies, aiming to transform AI into a more empathetic and effective participant in human social environments. -/- Keywords: Theory of Mind, Artificial Intelligence, Human-AI Interaction, Cognitive Processes, Data-Driven Analysis. (shrink)

In this paper, I provide an overview of today’s philosophical approaches to the problem of “intelligence” in the field of artificial intelligence by examining several important papers on phenomenology and the philosophy of biology such as those on Heideggerian AI, Jonas's metabolism model, and slime mold type intelligence.

Artificial Intelligence and Sensor Technologies the Future of Special Education for Students with Intellectual and Developmental Disabilities.

Δ.Δακρότσης, «Τεχνητή νοημοσύνη και εκφραστικότητα: η αναγκαιότητα περάσματος της εννοιολογικής γνώσης από το μονοπάτι της αισθητικής εποπτείας» (Artificial Intelligence and expressiveness: The necessity for conceptual knowledge to go through the path of aesthetic perception), Days of Art in Greece, Τεύχος 18, Φθινόπωρο 2024, σς 26-39.

In their thought-provoking article, Sedlakova and Trachsel (2023) defend the view that the status—both epistemic and ethical—of Conversational Artificial Intelligence (CAI) used in psychotherapy is complicated. While therapeutic CAI seems to be more than a mere tool implementing particular therapeutic techniques, it falls short of being a “digital therapist.” One of the main arguments supporting the latter claim is that even though “the interaction with CAI happens in the course of conversation… the conversation is profoundly different from a (...) conversation with a human therapist” (Sedlakova and Trachsel 2023, 8). In particular, unlike a human therapist, CAI cannot help its users gain new insight and self-understanding (Sedlakova and Trachsel 2023). We agree that currently available therapeutic CAI cannot be considered a “digital therapist,” however, we think that the issue surrounding the acquisition of new self-understanding in the interaction with therapeutic CAI is more complicated than Sedlakova and Trachsel suggest. (shrink)