A leading contributor to artificial intelligence offers insight into the numerous ways in which the mind works to demonstrate how emotions and feelings are just ...

Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or (...) a rationale for particular decisions in individual cases, some commentators regard ceding medical decision‐making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious—as it often is in medicine—the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy. (shrink)

The difficulty of explaining the outputs of artificial intelligence (AI) models and what has led to them is a notorious ethical problem wherever these technologies are applied, including in the medical domain, and one that has no obvious solution. This paper examines the proposal, made by Luciano Floridi and colleagues, to include a new ‘principle of explicability’ alongside the traditional four principles of bioethics that make up the theory of ‘principlism’. It specifically responds to a recent set of (...) criticisms that challenge the supposed need for such a principle to perform an enabling role in relation to the traditional four principles and therefore suggest that these four are sufficient without the addition of explicability. The paper challenges the critics’ premise that explicability cannot be an ethical principle like the classic four because it is explicitly subordinate to them. It argues instead that principlism in its original formulation locates the justification for ethical principles in a midlevel position such that they mediate between the most general moral norms and the contextual requirements of medicine. This conception of an ethical principle then provides a mold for an approach to explicability on which it functions as an enabling principle that unifies technical/epistemic demands on AI and the requirements of high-level ethical theories. The paper finishes by anticipating an objection that decision-making by clinicians and AI fall equally, but implausibly, under the principle of explicability’s scope, which it rejects on the grounds that human decisions, unlike AI’s, can be explained by their social environments. (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)

AI and people do not compete on a level-playing field. Self-driving vehicles may be safer than human drivers, but laws often penalize such technology. People may provide superior customer service, but businesses are automating to reduce their taxes. AI may innovate more effectively, but an antiquated legal framework constrains inventive AI. In The Reasonable Robot, Ryan Abbott argues that the law should not discriminate between AI and human behavior and proposes a new legal principle that will ultimately improve human well-being. (...) This work should be read by anyone interested in the rapidly evolving relationship between AI and the law. (shrink)

Nowadays, new directions for the development of artificial intelligence (AI) have emerged, the task has been set to develop artificial general intelligence (AGI), which is able to go beyond the narrow AI, gain a high degree of autonomy, independently solve problems in different environmental conditions and thus have the ability to perform the functions of natural intelligence. In this regard, important philosophical, theoretical, and methodological questions arise concerning the definition and evaluation of the social significance (...) of new AI achievements, especially regarding the correlation of their socio-humanitarian and technological aspects. It is necessary to overcome a purely technocratic approach, which usually ignores the negative consequences of digitalization, the possible risks and threats of AI development. These difficulties are conditioned by the paradigm gap between the system of concepts used to describe technologies and the systems of concepts specific to socio-humanitarian descriptions and explanations. The so-called ontological paradox arises during digital transformation and the introduction of AI into social systems. An ontological paradox is also an epistemological paradox, since any statement of an ontological type presupposes its epistemological justification. To overcome this gap, V.E. Lepskiy proposes the conception of self-developing polysubjective environments – cybernetics of the third order. This conception allows to create a conceptual “bridge” between two systems of definitions that do not have direct logical connections between them. A productive tool for this can be the information approach, which is widely used to solve such interdisciplinary problems. This makes possible to theoretically correctly connect technological and socio-humanitarian descriptions in a single conceptual structure and to analyze main socio-humanitarian criteria for evaluating digital innovations. The article discusses in detail the main provisions of the information approach and its application for the development of AGI systems, the socio-humanitarian assessment of AI autonomous development, and the resolution of security issues. (shrink)

Alan M. Turing, pioneer of computing and WWII codebreaker, is one of the most important and influential thinkers of the twentieth century. In this volume for the first time his key writings are made available to a broad, non-specialist readership. They make fascinating reading both in their own right and for their historic significance: contemporary computational theory, cognitive science, artificial intelligence, and artificial life all spring from this ground-breaking work, which is also rich in philosophical and logical (...) insight. An introduction by leading Turing expert Jack Copeland provides the background and guides the reader through the selection. About Alan Turing Alan Turing FRS OBE, (1912-1954) studied mathematics at King's College, Cambridge. He was elected a Fellow of King's in March 1935, at the age of only 22. In the same year he invented the abstract computing machines - now known simply as Turing machines - on which all subsequent stored-program digital computers are modelled. During 1936-1938 Turing continued his studies, now at Princeton University. He completed a PhD in mathematical logic, analysing the notion of 'intuition' in mathematics and introducing the idea of oracular computation, now fundamental in mathematical recursion theory. An 'oracle' is an abstract device able to solve mathematical problems too difficult for the universal Turing machine. In the summer of 1938 Turing returned to his Fellowship at King's. When WWII started in 1939 he joined the wartime headquarters of the Government Code and Cypher School (GC&CS) at Bletchley Park, Buckinghamshire. Building on earlier work by Polish cryptanalysts, Turing contributed crucially to the design of electro-mechanical machines ('bombes') used to decipher Enigma, the code by means of which the German armed forces sought to protect their radio communications. Turing's work on the version of Enigma used by the German navy was vital to the battle for supremacy in the North Atlantic. He also contributed to the attack on the cyphers known as 'Fish'. Based on binary teleprinter code, Fish was used during the latter part of the war in preference to morse-based Enigma for the encryption of high-level signals, for example messages from Hitler and other members of the German High Command. It is estimated that the work of GC&CS shortened the war in Europe by at least two years. Turing received the Order of the British Empire for the part he played. In 1945, the war over, Turing was recruited to the National Physical Laboratory (NPL) in London, his brief to design and develop an electronic computer - a concrete form of the universal Turing machine. Turing's report setting out his design for the Automatic Computing Engine (ACE) was the first relatively complete specification of an electronic stored-program general-purpose digital computer. Delays beyond Turing's control resulted in NPL's losing the race to build the world's first working electronic stored-program digital computer - an honour that went to the Royal Society Computing Machine Laboratory at Manchester University, in June 1948. Discouraged by the delays at NPL, Turing took up the Deputy Directorship of the Royal Society Computing Machine Laboratory in that year. Turing was a founding father of modern cognitive science and a leading early exponent of the hypothesis that the human brain is in large part a digital computing machine, theorising that the cortex at birth is an 'unorganised machine' which through 'training' becomes organised 'into a universal machine or something like it'. He also pioneered Artificial Intelligence. Turing spent the rest of his short career at Manchester University, being appointed to a specially created Readership in the Theory of Computing in May 1953. He was elected a Fellow of the Royal Society of London in March 1951 (a high honour). (shrink)

Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that user trust is properly calibrated to a tool’s computational capacities and limitations has both practical and ethical implications, given that overtrust or undertrust can influence over-reliance or under-reliance on algorithmic tools, with significant implications for patient safety and health outcomes. It is, thus, important to better understand how variability (...) in trust criteria across stakeholders, settings, tools and use cases may influence approaches to using AI/ML tools in real settings. As part of a 5-year, multi-institutional Agency for Health Care Research and Quality-funded study, we identify trust criteria for a survival prediction algorithm intended to support clinical decision-making for left ventricular assist device therapy, using semistructured interviews (n=40) with patients and physicians, analysed via thematic analysis. Findings suggest that physicians and patients share similar empirical considerations for trust, which were primarilyepistemicin nature, focused on accuracy and validity of AI/ML estimates. Trust evaluations considered the nature, integrity and relevance of training data rather than the computational nature of algorithms themselves, suggesting a need to distinguish ‘source’ from ‘functional’ explainability. To a lesser extent, trust criteria were also relational (endorsement from others) and sometimes based on personal beliefs and experience. We discuss implications for promoting appropriate and responsible trust calibration for clinical decision-making use AI/ML. (shrink)

Intellectual humility (IH) is often conceived as the recognition of, and appropriate response to, your own intellectual limitations. As far as we are aware, only a handful of studies look at interventions to increase IH – e.g. through journalling – and no study so far explores the extent to which having high or low IH can be predicted. This paper uses machine learning and natural language processing techniques to develop a predictive model for IH and identify top terms and features (...) that indicate degrees of IH. We trained our classifier on the dataset from an existing psychological study on IH, where participants were asked to journal their experiences with handling social conflicts over 30 days. We used Logistic Regression (LR) to train a classifier and the Linguistic Inquiry and Word Count (LIWC) dictionaries for feature selection, picking out a range of word categories relevant to interpersonal relationships. Our results show that people who differ on IH do in fact systematically express themselves in different ways, including through expression of emotions (i.e., positive, negative, and specifically anger, anxiety, sadness, as well as the use of swear words), use of pronouns (i.e., first person, second person, and third person) and time orientation (i.e., past, present, and future tenses). We discuss the importance of these findings for IH and the value of using such techniques for similar psychological studies, as well as some ethical concerns and limitations with the use of such semi-automated classifications. (shrink)

The imaginative context in which artificial intelligence is embedded remains a crucial touchstone from which to understand and critique both the histories and prospective futures of an AI-driven world. A recent article from Cave and Dihal sets out a narrative schema of four hopes and four corresponding fears associated with intelligent machines and AI. This article seeks to respond to the work of Cave and Dihal by presenting a gendered reading of this schema of hopes and fears. I (...) offer a brief genealogy of narratives which feature female automata, before turning to examine how gendered technology today—particularly AI assistants like Siri and Alexa—reproduces the historical narratives associated with intelligent machines in new ways. Through a gendered reading of the hopes and fears associated with AI, two key responses arise. First, that the affective reactions to intelligent machines cannot be readily separated where such machines are gendered female. And second, that the gendering of AI technologies today can be understood as an attempt to reconcile the opposing hopes and fears AI produces, and that this reconciliation is based on the association of such technologies with traditional notions of femininity. Critically, a gendered reading enables us to problematize the narratives associated with AI and expose the power asymmetries that lie within, and the technologies which arise out of, such narratives. (shrink)

The effects that artificial intelligence (AI) technologies will have on society in the short- and long-term are inherently uncertain. For this reason, many governments are avoiding strict command and control regulations for this technology and instead rely on softer ethics-based approaches. The Russian approach to regulating AI is characterized by the prevalence of unenforceable ethical principles implemented via industry self-regulation. We analyze the emergence of the regulatory regime for AI in Russia to illustrate the limitations of this approach. (...) The article is based on 50 interviews with policymakers, representatives of AI companies, and academics in the country. The findings show that this regulatory regime was formed under the strong influence of Russian big tech companies, which saw an opportunity to avoid regulatory oversight by washing out concrete regulatory measures from the policy. This approach is part of a broader protectionist sanction-proofing strategy for the local IT sector designed by the government, which can be characterized by lifting regulatory barriers for local companies. Unenforceable ethics-based self-regulation is a _regulatory gift_ from the Russian government to the industry. This _gift_ was intentionally designed because the government thought that prioritizing local innovation over consumer protection would benefit the public. However, the _gift_ can also unintentionally undermine the public interest by providing an opportunity for _ethics washing_. (shrink)

This book offers a first glimpse into contemporary African Philosophical thought, which covers issues related to the mind-body relationships, the problem of consciousness, the ethics of artificial intelligence, the meaning of life and other topics. Taking inspiration from the conversational tradition in African philosophy, this book not only engages with and takes inspiration from traditional African thought, but also engages with philosophical views outside the philosophical tradition in a bid to present a holistic understanding of the problems that (...) are central to the book. The volume is relevant for professional African philosophers, philosophers of mind, philosophers of AI, undergraduate and postgraduate philosophy students, and African Studies scholars. (shrink)

The article presents a new method for obtaining analogues of words, characterized by simplicity and the absence of the need for preliminary training on large data as in existing methods. In the method under study, analogues are determined by their syntactic predicates using methods of distributive semantics. In the study, analogues of adjectives, nouns and verbs were obtained and analyzed. This made it possible to obtain a result that is not inferior to the results obtained using the most popular neural (...) network approach as word2vec when qualitatively comparing analogues. The demonstrated method shows that obtaining analogues is possible using methods of distributive semantics using a more interpretable method, which opens up the possibility of studying semantic analogy. This method also allows you to identify analogues on a specific topic. Based on the experimental results obtained, an original definition of analogues and cognitive schemes is formulated. The article also analyzes and substantiates the possibility of a new approach for creating artificial intelligence systems based on the researched method. According to the authors, this provides significant advantages for the creation of such systems. In particular, the proposed method allows for broader generalizations over orders of magnitude smaller data, as well as learning during use, which is not possible for neural networks. (shrink)

Increasingly, innovation in artificial intelligence technologies portends the re-conceptualization of human existentiality along the paradigm of posthumanism. An exposition of this through a critical culturo-historical methodology uncloaks the Eurocentric genitive basis of the philosophical anthropology that underpins this technological posthumanism, as well as its dystopian possibilities. As a contribution to obviating the latter, an Africanist civilizational humanism proclaimed by Pixley ka Isaka Seme is proffered as a plausible alternative paradigm for humanity’s technological advancement. Seme, a pan-Africanist thinker of (...) the early twentieth century, proclaimed humanistic-spirituality as the indispensable gift African Civilisation-in-its-renaissance is yet to offer global humanity. His postulation is being provided as a contribution to the archive on cross-cultural ethics of artificial intelligence. (shrink)

The study aims to identify the degree of possession of government school principals in the Karbala Governorate Education Directorate of artificial intelligence applications. The descriptive analytical approach was used. To achieve the study objectives, a questionnaire which contained (34) items was developed, divided into five dimensions. The study community was (114) male and female principals. The study sample consisted of (94) male and female managers, and the sample was selected randomly. The results of the study showed that the (...) degree of possession of government school principals in the Karbala Governorate Education Directorate of artificial intelligence applications was average. The results of the study also showed that there were no statistically significant differences at the significance level (α=0.05) attributed to the study variables (gender, academic qualification, experience in artificial intelligence techniques). In light of the study results, the researchers recommended several recommendations, most notably: paying attention to training courses in the field of information and communications technology, especially in the subject of artificial intelligence, by organizing meetings and workshops with the aim of raising awareness among individuals and society about the importance of artificial intelligence, encouraging the Iraqi Ministry of Education to its teaching staff and providing them with incentives to employ artificial intelligence applications in their administrative and educational work, introducing artificial intelligence applications into curricula and courses and paying attention to them, and providing specialized teaching staff in this field. (shrink)

The application of machine learning and artificial intelligence in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five (...) articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle. We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process. A third of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the “gold standard” of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users’ needs and feedback in the design process. (shrink)

The general idea that we have of artificial intelligence (AI) consists of the belief that machines will be able to develop conscious thoughts such as those possessed by human beings, and, as computing advances, such thinking will also advance until intelligence to surpass the human being, with which the advancement of AI represents ethical risks in the future. In reality, such a belief hides a cognitive assumption, which assumes that computational engineering explains human intelligence through the (...) mind-computer metaphor. According to this assumption, technology explains cognition, and philosophy, through ethics, reflects on the impact of said technology. However, in this article, I contradict such an assumption and defend that the philosophy in AI is not reduced to the ethics that is present after the use and impact of AI in the world. I intend to expose that a good ethics of AI is the one that reflects on the appropriate risks facing AI, and for this, philosophy, beforehand, must make a cognitive analysis about the possibilities that computing has to create intelligent machines, namely, whether or not the mindcomputer metaphor makes sense. My thesis consists in defending that the philosophical analysis about AI must be carried out both on a cognitive level and on an ethical level, but that the philosophical priority in the cognitive analysis over the ethical priority, since the ethical risks of AI depend of the possibilities of technology, and only the cognitive approach can account for this. (shrink)

This paper presents a novel methodology for integrating indigenous knowledge systems into AI development to counter cognitive imperialism and foster inclusivity. By critiquing the dominance of Western epistemologies and highlighting the risks of bias, the authors argue for incorporating diverse epistemologies. The proposed framework outlines a participatory approach that includes indigenous perspectives, ensuring AI benefits all. The methodology draws from AI ethics, indigenous studies, and postcolonial theory, emphasizing co-creation with indigenous communities, ethical protocols for indigenous data governance, and adaptation of (...) AI algorithms. Case studies in natural language processing, content moderation, and healthcare demonstrate the methodology’s effectiveness and importance. By offering a concrete methodology for decolonizing AI, this paper contributes significantly to AI ethics and social justice, providing a roadmap for equitable, culturally respectful AI. (shrink)

The article focuses on the inconsistency between the European Commission’s position on excluding military AI from its emerging AI policy, and at the same time EU policy initiatives targeted at supporting military and defence elements of AI on the EU level. It leads to the question, what, then, does the debate on military AI suggest to the EU’s actorness discussed in the light of Europe as a power debate with a particular focus on Normative Power Europe, Market Power Europe, and (...) Military Power Europe. By employing discourse analysis, the article examines the EU’s AI strategic discourse, consisting of selected AI-related policy documents from different EU institutions. As a result, the article proposes the Military Power Europe definition based on four categories proposed from the Europe as power debate: ways of action, self-definition, preferred international engagement, and the role of the military. It argues that alongside normative proposals for military AI governance, there are evident desires for militarization in the context of AI development, and a considerable role for the military in the future directions of the EU’s digital and security policies. Despite the inconsistency among EU institutions, military AI appears to be actively discussed within the selected discourse, and so is a part of the EU’s emerging AI policy. (shrink)

Informal Logic, Argumentation Theory and Artificial Intelligence.

Sensation is the reflection of the brain on the individual attributes of objective things that directly act on the sense organs. Feeling is the most elementary cognitive process and the simplest psychological phenomenon. Vision is a kind of sense, and sense is produced by objective things acting on the sense organs. But at present, it is rare to analyze interior design exhibition from the perspective of visual psychology, an emerging science, as an interdisciplinary attempt, only in interior design research. Therefore, (...) the study of sensory process should start from its external stimuli, in order to first understand how it acts on the sensory organs to produce sensory phenomena. This paper mainly studies the visual performance of psychological factors in interior design under the background of artificial intelligence. This paper proposes a K-means clustering algorithm and a localization algorithm fused with visual and inertial navigation. The distance thresholds corresponding to the SIFT feature descriptors of threshold T1, 128D, 96D, 64D, and 32D are 170, 160, 150, and 90, respectively. This verifies that the candidate image with the highest number of matching points is considered the best matching image. (shrink)

In recent years, artificial intelligence (AI) has garnered tremendous attention across a range of sectors, including health care. AI is a comprehensive term, broadly defined as computational machines making decisions and performing tasks that previously required human intelligence or cognition. There are high expectations of what AI can do; enthusiasts sometimes refer to it as a central pillar of the Fourth Industrial Revolution, with an impact on humanity as profound as that of the steam power engine or (...) electricity. Building on the Third Industrial Revolution (or Digital Revolution) that began in the middle of the twentieth century and was characterized by the spread of automation and digitization through the use of electronics, computers, and communication technologies (e.g., the internet), the Fourth Industrial Revolution is characterized by a fusion of advancing technologies in AI, robotics, the Internet of Things (IoT), genetic engineering, and quantum computing. These technologies blend physical production and operations with smart digital technologies, promising to transform the operation, management, and governance of every industry, including health care. (shrink)

A groundbreaking endeavor that explores human spirituality using the evolving technology of artificial intelligence.

Dragon boat sport is a traditional activity in China. In recent years, dragon boat sport has become more and more popular around the world. In order to face more challenges, it is urgent for athletes to enhance their own strength. Scientific training methods are particularly important for athletes, and accurate training data are the basis to support scientific training. Traditional mathematical statistic methods neither can sample signals accurately nor can they do real-time analysis and feedback the characteristics to each athlete. (...) In this paper, we use the wearable device with a triaxial accelerometer and heart rate sensor builtin to sample the speed signals and heart rate signals of athletes in various stages of men’s 1000m straight race. Based on the complex network theory, we regard the 23 dragon boat athletes in the dragon boat race as 23 nodes so as to establish a network with 23 nodes and reflect the importance of nodes by measuring the impact of node deletion on the results of the race. The neural network multilayer perceptron model is used for training to obtain the optimal combined value with speed and heart rate for each race stage. The optimal value will be used in the simulated race as the target value to verify if it can help to improve the training efficiency. Experimental results show that the optimal value obtained by this method has a positive effect on the results of the dragon boat race which is beneficial to sports training and tactics planning. (shrink)

Cinema and Machine Vision unfolds the aesthetic, epistemic, and ideological dimensions of machine-seeing films and television using computers. With its critical-technical approach, this book presents to the reader key new problems that arise as AI becomes integral to visual culture. The book theorises machine vision through a selection of aesthetics, film theory, and applied machine learning research, dispelling widely held assumptions about computer systems designed to watch and make images on our behalf. -/- At its heart, Cinema and Machine Vision (...) is an invitation for film and media scholars to critically engage with AI at a technical level, a prompt for scientists and engineers working with images and cultural data to critically reflect on where their assumptions about vision come from, and a joint recognition of the fruitful problems of working together to understand the algorithmic governance of the visual. (shrink)

Recently, Mr. Bauer (2020), CEO of BAM, a human resources service provider, reported about the introduction of a continuous change process using artificial intelligence. From this as a starting point, the article defines and discusses change processes, transformation management, and organisational development. The cudgels are taken up on behalf of the human-in-the-loop. It is argued, that the so-called “weak” artificial intelligence, including the human, is superior to the black box approach, hiding the system state as well (...) as its dynamics from the user and deciding itself. Long-sightedness and employee orientation as well as open communication was the key in Industry 1.0 and it still is in Industry 5.0. (shrink)

In this work, we provide a broad overview of the distinct stages of E-Discovery. We portray them as an interconnected, often complex workflow process, while relating them to the general Electronic Discovery Reference Model (EDRM). We start with the definition of E-Discovery. We then describe the very positive role that NIST’s Text REtrieval Conference (TREC) has added to the science of E-Discovery, in terms of the tasks involved and the evaluation of the legal discovery work performed. Given the critical nature (...) that data analysis plays at various stages of the process, we present a pyramid model, which complements the EDRM model: for gathering and hosting; indexing; searching and navigating; and finally consolidating and summarizing E-Discovery findings. Next we discuss where the current areas of need and areas of growth appear to be, using one of the field’s most authoritative surveys of providers and consumers of E-Discovery products and services. We subsequently address some areas of Artificial Intelligence, both Information Retrieval-related and not, which promise to make future contributions to the E-Discovery discipline. Some of these areas include data mining applied to e-mail and social networks, classification and machine learning, and the technologies that will enable next generation E-Discovery. The lesson we convey is that the more IR researchers and others understand the broader context of E-Discovery, including the stages that occur before and after primary search, the greater will be the prospects for broader solutions, creative optimizations and synergies yet to be tapped. (shrink)

The ontological presupposition of artificial intelligence (AI) is the liberal autonomous human subject of Locke and Kant, and the ideology of AI is the automation of this particular conception of intelligence. This is demonstrated in detail in classical AI by the work of Simon, who explicitly connected his work on AI to a wider programme in cognitive science, economics, and politics to perfect capitalism. Although Dreyfus produced a powerful Heideggerian critique of classical AI, work on neural networks (...) in AI was ultimately based on the individual as the locus of intelligence. Yet this conception of AI both fails to grasp the essence of large language models, which are a statistical model of human language on the Web. The training data that enables AI is the surveillance and capture of data, where the data creates a model to approximate the entire world. However, there is a more hidden ideology inherent in AI where the goal is not to perfect a model but to control the world. As prompted by an argument between Mead and Bateson, social change is prevented by the application of cybernetics to society as a whole. The goal of AI is not just to replace human beings, but to manage humans to preserve existing power relations. As the source of intelligence in AI is distributed cognition between humans and machines, the alternative to AI is collective intelligence. As theorized by Licklider and Engelbart at the dawn of the Internet, collective intelligence explains how computers weave together both human and non-human intelligence. Rather than replace human intelligence, this produces ever more complex collective forms of intelligence. Rather than meta-stabilize a society of control, collective intelligence can go outside individualist capitalist ontology by incorporating the open world of the pluriverse, as theorized by Escobar. Collective intelligence then stands as an alternative ontological path for AI which puts intelligence at the service of humanity and the world rather than a technocratic elite. (shrink)

The article examines the problem of ensuring the political stability of a democratic social system with a shortage of a vital commodity (like vaccine against COVID-19). In such a system, members of society citizens assess the authorities. Thus, actions by the authorities to increase the supply of this commodity can contribute to citizens' approval and hence political stability. However, this supply is influenced by random factors, the actions of competitors, etc. Therefore, citizens do not have sufficient information about all the (...) possibilities of supplying, and it is difficult for them to make the right decisions. Such citizen unawareness can be exploited by unscrupulous politicians to achieve personal targets. Therefore, it is necessary to organize public control to motivate politicians to use all available opportunities in supplying. The goal of the paper is to build such a digital mechanism of public control of the politicians by citizens, which would best assess and stimulate the activities of the authorities to improve the supply of a vital commodity. In the age of artificial intelligence, such digital public control in the face of uncertainty can be based on digital machine learning. In addition, it is necessary to take into account and model the activities of politicians associated with the presence of their own targets that do not coincide with public ones. Such politicians can use the learning of citizens for their own targets. The objective of the article is to build an optimal digital mechanism of public control in a two-level model of a democratic social system—a digital society. At its top level, there is the Citizen, who gives an assessment for the Politico located at the lower level. In turn, the Politico can influence the supplying of a vital commodity. Political stability is guaranteed if the Citizen regularly approves of the Politico’s actions to increase this supply. However, the Politico may not use the opportunities available to him to offer a commodity to achieve a personal target. To avoid this, the Politico’s control mechanism is proposed. It includes the procedure for digital learning of the Citizen, as well as a procedure for assessing the Politico activity. Sufficient conditions have been found for the synthesis of such the Politico’s control mechanism, at which stochastic possibilities of increasing the supply of a vital commodity are used. The example of such the Politico’s control mechanism is considered on the case of supply of the COVID-19 vaccine in England. (shrink)

The central thesis of this paper is that human unconscious processes influence the behavior and design of artificial intelligence (AI). This thesis is discussed through the case study of a chatbot called Replika, which intends to provide psychological assistance and friendship but has been accused of inciting murder and suicide. Replika originated from a trauma and a work of mourning lived by its creator. The traces of these unconscious dynamics can be detected in the design of the app (...) and the narratives about it. Therefore, a process of de-psychologization and de-humanization of the unconscious takes place through AI. This psychosocial approach helps criticize and overcome the so-called “standard model of intelligence” shared by most AI researchers. It facilitates a new interpretation of some classic problems in AI, such as control and responsibility. (shrink)

Automation, artificial intelligence, and biotechnology have become topics of increasing interest in both academia as well as in popular media. The goal of this article is to establish which issues are the most pressing, and what are the underlying causes of the rise of robots. I demonstrate that fears of automation are well supported by current trends of automation as well as the inherent tendency within a capitalist system to automate at the expense of workers and working wages. (...) Additionally, I point to what appears to be a potential evolutionary divide between what Yuval Noah Harari calls the “god” class and the “useless” class. Finally, I discuss how the new role of information as the world’s most valued commodity can either exacerbate this coming divide or liberate society. I present the case that in order to diminish the negative effects we must move beyond the present capitalist economic system, towards one in which economic democracy and the free flow of information thrives. (shrink)

본 논문의 목적은 현재의 인공지능(artificial intelligence, 이하 AI) 윤리 논의에서 결여되어 있는 AI 개발자 윤리의 중요성과 필요성을 부각하고, AI 개발자 윤리로 덕성(virtue)기반의 모델이 적합함을 보여 이를 제안하는 것이다. AI는 고정된 과학기술이 아니라 발전노정에 있는 동적이라는 특성과 인간과의 상호작용에서 AI를 만든 인간의 삶에 지대한 영향을 끼치는 재귀적 특성이 있는 과학기술이다. 이러한 AI의 특성으로 인해, AI가 초래할 리스크에 대응하려는 AI 윤리는 발전 과정에 따라 제시되어야 한다는 점에서 체계적이고, 실천을 도모하는 동기부여의 구조를 가져야 한다는 점에서 기존 도덕의 형식적 특징인 1인칭, 2인칭, (...) 3인칭 관점이 통합될 필요가 있다. 그런데 최근에 활발하게 제시된 많은 AI윤리 지침에 대한 메타 분석에 따르면, 현재의 지침으로서의 AI 윤리 논의들은 1인칭 관점의 동기부여가 없는 3인칭 관점에만 한정되어 실질적인 ‘윤리 실현’(ethics realizing)을 도모하기에 취약하다. AI 윤리 지침 자체가 추상적이고 모호할 뿐만 아니라, AI를 개발하는 사람들에게 아무런 영향력을 행사하지 못하는 ‘윤리 세탁’(ethics washing)에 불과하기 때문이다. 이러한 한계를 보완하기 위해서는 AI 개발자들이 AI 윤리를 내면화하여 규범적으로 실천하게 할 수 있게 하는 AI 개발자 윤리가 필요한데, 본 논문은 덕성 기반의 AI 개발자 윤리가 이러한 목적을 위해 효과적이라고 주장한다. 덕성 기반의 AI 개발자 모델은, 과학적 덕성 모델(scientific virtue model)이 그랬던 것처럼 AI 개발의 목적이 무엇인지, AI개발자들의 전문직 덕성(vocational virtue)이 무엇인지, 그리고 이러한 덕성을 함양하기 위한 효과적인 교육 방법은 무엇인지 등의 이론 연구와 AI 개발자들과의 소통을 토대로 하는 경험 연구의 결합을 통해 실천적 차원의 모델로 구체화될 수 있다. 본 논문에서 이론적으로 제안하는 덕성 기반의 AI 개발자 윤리가 추후 한국 사회의 경험 연구를 통해 실천적 차원의 모델로 구체화되어 AI가 초래하는 윤리적 문제들에 효과적으로 대응하면서 AI의 혜택이 풍성히 향유되는데 기여하기를 기대한다. (shrink)

Future developments in artificial intelligence (AI) will likely allow for a greater degree of human‐machine convergence, with machines becoming more humanlike and intelligent machinery becoming more integrated into human brain function. This will pose many ethical challenges, and the necessity for a moral framework for evaluating these challenges will grow. This paper argues that community concern constitutes a central factor in both the evolution of religion and the human brain, and as such it should be used as the (...) organizing principle for moral evaluations of AI technologies. (shrink)

The potential societal impacts of automation using intelligent control and communications technologies have emerged as topics in a number of recent writings and public policy initiatives. Many of these expressions have referenced the writings and research efforts of Herbert Simon (1961), Norbert Wiener (1948), and contemporaries from their early technological and social vantage points concerning the future of technology and society. Constructed entities labeled as “thinking machines” (such as IBM’s Watson as well as intelligent chatbot and robotic systems) have also (...) played significant roles in this discourse. This paper provides an historical sequencing then analyzes a selection of writings produced since the 1940s concerning the economic and social issues involving artificial intelligence (AI) research and applications. The paper explores how overstatements and hyperbolic themes and concepts, often stemming from AI’s early periods, are being employed in characterizations of current AI approaches in apparently opportunistic attempts to provide rhetorical support for various large-scale business and societal initiatives. It also addresses the relative neglect of the consideration of many of AI’s sociotechnical failures and discontinued approaches in recent examinations of automation and social welfare issues. The paper discusses the moral logic of AI researchers and developers providing reasonable and measured narratives in public discourse rather than hyperbole, efforts that can empower decision makers to make sounder judgments concerning the technology’s current and future applications as well as allocate the rewards of the technology more equitably. (shrink)

The paper examines the relationship between artificial intelligence (AI), neurodiversity, and trust. It aims to address the gap in understanding the impact AI has on neurodivergent individuals. First, it provides a general introduction to neurodiversity, highlighting its variations and societal significance. Second, the paper introduces AI, focusing on its potential to support neurodivergent individuals in overcoming challenges related to communication, executive functioning, sensory processing, education and socialization. The paper proceeds by exploring the complexities of trust from a philosophical (...) perspective. It stresses that trust in AI could be seen as "delegated trust," which is directed towards the developers, regulators, and institutions behind AI systems rather than the AI itself. However, main focus on trust regarding AI is from the end-user perspective. Ultimately, the paper advocates for carefully crafted regulations that balance the benefits of AI for neurodivergent individuals with the risks of over-reliance and the need for strong privacy protections. (shrink)

Conversational artificial intelligence (CAI) presents many opportunities in the psychotherapeutic landscape—such as therapeutic support for people with mental health problems and without access to care. The adoption of CAI poses many risks that need in-depth ethical scrutiny. The objective of this paper is to complement current research on the ethics of AI for mental health by proposing a holistic, ethical, and epistemic analysis of CAI adoption. First, we focus on the question of whether CAI is rather a tool (...) or an agent. This question serves as a framework for the subsequent ethical analysis of CAI focusing on topics of (self-) knowledge, (self-)understanding, and relationships. Second, we propose further conceptual and ethical analysis regarding human-AI interaction and argue that CAI cannot be considered as an equal partner in a conversation as is the case with a human therapist. Instead, CAI’s role in a conversation should be restricted to specific functions. (shrink)

1. WHAT IS ARTIFICIAL INTELLIGENCE? One of the fascinating aspects of the field of artificial intelligence (AI) is that the precise nature of its subject ..

The increasing use of Artificial Intelligence for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily (...) focused on how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring. (shrink)