Impact of Artificial Intelligence

Smart wearable technologies have seen an explosive growth over recent years, with some research indicating that the wearable technology industry is expected to grow from USD 24 billion today to over USD 70 billion in 2025. This proliferation has extended across disparate domains, ranging from medical applications and fitness and social technologies to military, industrial, and manufacturing applications. As with any emergent technology, these wearables present opportunities for our moral benefit as well as moral challenges to be addressed. A crucial (...) dimension of this discussion is the ethical evaluation of the impact of smart wearables on the autonomy of human decision-making. Nowhere is this a more pertinent concern than when dealing with persons uniquely vulnerable to autonomy infringement. This contribution, undertaken from an explicitly normative and ethical perspective, investigates the potential impact of smart wearables on various dimensions of the decisional autonomy of vulnerable persons. (shrink)

Acceleration ethics addresses the tension between innovation and safety in artificial intelligence. The acceleration argument is that risks raised by innovation should be answered with still more innovating. This paper summarizes the theoretical position, and then shows how acceleration ethics works in a real case. To begin, the paper summarizes acceleration ethics as composed of five elements: innovation solves innovation problems, innovation is intrinsically valuable, the unknown is encouraging, governance is decentralized, ethics is embedded. Subsequently, the paper illustrates the acceleration (...) framework with a use-case, a generative artificial intelligence language tool developed by the Canadian telecommunications company Telus. While the purity of theoretical positions is blurred by real-world ambiguities, the Telus experience indicates that acceleration AI ethics is a way of maximizing social responsibility through innovation, as opposed to sacrificing social responsibility for innovation, or sacrificing innovation for social responsibility. (shrink)

Rethinking Work, Power, and the Future of Nations// This article examines how economic and social systems adapt to technological disruptions, particularly artificial intelligence and automation. It explores several fundamental questions about systemic evolution in the face of changing scarcity paradigms. How do systems adapt when traditional forms of scarcity diminish? As technology reduces scarcity of physical and intellectual labor, new forms of scarcity emerge—attention, meaning, purpose, and experience become the new commodities. These shifts catalyze the evolution of economic structures, transforming (...) work rather than eliminating it entirely..//What drives the survival mechanisms of societal systems? Systems function like software agents with programmatic rules, continuously evolving to maintain their existence. Though not consciously acting entities, they develop traits ensuring adaptation and persistence. Citizens serve as both carriers and executing agents of these systems, participating in reinforcement mechanisms that perpetuate the system's operation.// How do systems change? Significant transformation typically requires emotional energy exceeding critical thresholds—often generated through crises. While proactive adaptation would be preferable, systems generally resist change until environmental pressures become intense enough to overcome institutional inertia.// Why is system diversity of systems important? Multiple competing systems create "trial-and-error laboratories" that test different approaches to emerging challenges. This diversity enhances humanity's collective adaptive capacity, proving more advantageous than monocultural global systems with reduced adaptability.// What role does information control play? As systems compete, control over information flows becomes crucial for survival. Systems naturally frame comparisons with competitors to maintain their power position. Open communication about relative fitness, however, encourages beneficial cross-adoption of successful elements.// What future emerges from these adaptations? Rather than catastrophic collapse, gradual transformation is more likely. Systems will incorporate new elements while preserving core structures. The evolving landscape may create unprecedented value, enabling human endeavor to pivot toward qualitative improvements in well-being, sustainability, and creativity./// The analysis concludes that human progress continues through evolutionary adaptation rather than revolutionary disruption, as systems expand into new niches while preserving fundamental human values and social cohesion. (shrink)

Three directions for the AI avant-garde are sketched against the background of time. Posthumanism changes what we are, and belongs to the radical future. Transhumanism changes how we are, and corresponds with the radical past. Genhumanism changes who we are, and exists in the radical present. While developing the concepts, this essay intersects in two ways with theoretical debates about humanism in the face of technological advance. First, it describes how temporal divisions may cleanly differentiate post- and transhumanism. Second, the (...) essay introduces generative humanism, which contributes to discussions about AI and society by delineating a novel humanistic response to contemporary technology. Finally, grounds are provided for a practical project, one where philosophers work with AI engineers in the area of genhumanism. Contemporary AI research into serendipity in recommendation engines provides natural support for the shared research. (shrink)

Since Clark and Chalmers advanced “The Extended Mind” in 1998, a persistent dispute evolved on how our tool interactions shape the kind of cognition we have. Extended cognition generally views us as cognitively augmented and enhanced by our tool practices, which shall render our cognitive constitution extended to those tools. Bounded and embedded cognition have primarily criticized this metaphysical claim. However, another contender may arise from considering how we use more intelligent tools. We arguably employ advanced technologies that capture, mimic, (...) and then replace our cognitive skills, which we then no longer need to exercise ourselves. This precedes any metaphysical debate, since such practices might stand in a more fundamental conflict with extended cognition. The counter-hypothesis of extracted cognition states that we primarily tend to use tools that initially attain and eventually displace our cognitive responsibilities and involvements. This paper evaluates extended and extracted cognition by comparing theoretical, practical, and ethical arguments respectively. If extracted cognition describes most convincingly how such tool interactions shape our kind of cognition, then we may also endorse “The Extracted Mind”. (shrink)

Deploying an algorithmically informed policy is a significant intervention in society. Prominent methods for algorithmic fairness focus on the distribution of predictions at the time of training, rather than the distribution of social goods that arises after deploying the algorithm in a specific social context. However, requiring a ‘fair’ distribution of predictions may undermine efforts at establishing a fair distribution of social goods. First, we argue that addressing this problem requires a notion of prospective fairness that anticipates the change in (...) the distribution of social goods after deployment. Second, we provide formal conditions under which this change is identified from pre-deployment data. That requires accounting for different kinds of performative effects. Here, we focus on the way predictions change policy decisions and, consequently, the causally downstream distribution of social goods. Throughout, we are guided by an application from public administration: the use of algorithms to predict who among the recently unemployed will remain unemployed in the long term and to target them with labor market programs. Third, using administrative data from the Swiss public employment service, we simulate how such algorithmically informed policies would affect gender inequalities in long-term unemployment. When risk predictions are required to be ‘fair’ according to statistical parity and equality of opportunity, targeting decisions are less effective, undermining efforts to both lower overall levels of long-term unemployment and to close the gender gap in long-term unemployment. (shrink)

Beauty filters are automated photo editing tools that use artificial intelligence and computer vision to detect facial features and modify them, allegedly improving a face’s physical appearance and attractiveness. Widespread use of these filters has raised concern due to their potentially damaging psychological effects. In this paper, I offer an account that examines the effect that interacting with such filters has on self-perception. I argue that when looking at digitally-beautified versions of themselves, individuals are looking at AI-curated distorted mirrors. This (...) helps identify two potential cognitive effects of this behavior. First, it can elicit affective attitudes that change how individuals feel when looking at their unfiltered self-images. Second, exposure to filtered self-images might cause a perceptual normalization of such images. Finally, I argue that this form of distorted mirror gazing is a novel cultural practice for self-perception, and I highlight some ways in which this practice could be critically evaluated and ultimately changed. (shrink)

En las últimas décadas, la neurocomunicación ha emergido como un campo interdisciplinario que explora cómo los procesos neurológicos influyen en la manera en que las personas se comunican y aprenden. En el ámbito educativo, esta disciplina abre nuevas posibilidades para comprender cómo los estudiantes procesan la información y responden a los estímulos cognitivos y emocionales. Con la creciente influencia de la IA, se han desarrollado herramientas que permiten aplicar principios de neurocomunicación en contextos pedagógicos. Este artículo de revisión se centra (...) en la intersección entre neurocomunicación, educación y las nuevas experiencias que ofrece la IA, explorando las teorías clave y los avances tecnológicos que potencian el aprendizaje personalizado y eficiente. La investigación teórica se enmarca en un análisis de las fuentes más relevantes y recientes sobre cómo la IA está transformando la interacción educativa, proporcionando una nueva perspectiva sobre el impacto neuronal en los procesos de enseñanza-aprendizaje. En este artículo se pretende explorar cómo la neurocomunicación y la IA están transformando la educación desde una perspectiva teórica. La revisión se centrará en las principales teorías de neurocomunicación aplicadas a la enseñanza y cómo la IA ha redefinido estas interacciones en el aula. A través del análisis de fuentes clave, se examinarán los avances en neurotecnología educativa que mejoran la personalización del aprendizaje, adaptándose a los patrones neuronales individuales. Así, se busca ofrecer una perspectiva profunda sobre cómo las experiencias y aplicaciones en IA están configurando un nuevo paradigma en la neurocomunicación educativa, planteando preguntas cruciales sobre el futuro del aprendizaje y su relación con la tecnología. Este estudio de naturaleza teórica apunta a la necesidad de comprender cómo estas tecnologías emergentes influyen en los estudiantes, no solo en términos de rendimiento académico, sino también en su desarrollo cognitivo y emocional. Al identificar las áreas clave en las que la IA y la neurocomunicación convergen, se pretende sentar las bases para futuras investigaciones y aplicaciones prácticas en el ámbito educativo. (shrink)

El libro “Investigación en escenarios formativos y conocimiento abierto en acción” explora, a través de diez capítulos, una amplia gama de temas relevantes en el ámbito de la investigación educativa y la innovación docente, integrando teoría y práctica en diversos contextos educativos. Desde el análisis de herramientas psicométricas hasta el impacto de las tecnologías emergentes, esta obra adopta un enfoque multidisciplinario orientado a mejorar la calidad de la educación. En el Capítulo 1, se examinan las propiedades psicométricas del cuestionario LIDPED, (...) una herramienta diseñada para evaluar el liderazgo educativo en centros de difícil desempeño. Este análisis no solo permite comprender mejor el rol de liderazgo en entornos complejos, sino que también aporta una valiosa herramienta de medición para gestores educativos en contextos desafiantes. El Capítulo 2 introduce la creación y validación de la escala EVAFORPE, enfocada en evaluar la formación profesional desde la perspectiva de los orientadores laborales. Este capítulo destaca la importancia de la retroalimentación de estos profesionales para adaptar y mejorar los programas de formación, asegurando que respondan a las necesidades reales del mercado laboral. En el Capítulo 3, el enfoque se desplaza hacia el ámbito universitario con un análisis de las metodologías activas y el uso de TIC para fomentar un aprendizaje colaborativo. Este capítulo subraya cómo las tecnologías pueden transformar la experiencia educativa, promoviendo una mayor participación y cooperación entre los estudiantes. El Capítulo 4 explora el fenómeno del cohousing y coliving como nuevas formas de vivienda compartida, resaltando cómo estos modelos se adaptan a las demandas de una sociedad cada vez más digitalizada y colaborativa. Este capítulo invita a reflexionar sobre el impacto de estos entornos en el aprendizaje informal y la convivencia. En el siguiente capítulo se adentra en el campo de la neurocomunicación y la inteligencia artificial, analizando cómo estas áreas emergentes pueden enriquecer los procesos educativos. A través de un estudio teórico, se investiga cómo la neurociencia aplicada a la educación contribuye a una comprensión más profunda de los procesos cognitivos y emocionales. El Capítulo 6 aborda la necesidad de integrar la figura del psicólogo sanitario en los centros educativos, destacando el rol clave de la salud mental en el desarrollo integral de los estudiantes. Este capítulo propone que el apoyo psicológico dentro de los centros educativos es fundamental para crear entornos de aprendizaje seguros y resilientes. En el Capítulo 7, la formación en didáctica de la educación plástica se analiza desde la perspectiva de los futuros docentes. Este capítulo pone de manifiesto la importancia de una formación específica que permita a los futuros docentes enseñar de forma creativa y efectiva en áreas artísticas. El Capítulo 8 aporta una perspectiva económica al estudiar la evolución de los modelos de reparto de dividendos y las tecnologías emergentes en este campo. A través de este análisis, se destacan las intersecciones entre economía y educación, particularmente en la adaptación de herramientas financieras en entornos formativos. El Capítulo 9 presenta una herramienta diseñada para mejorar la comprensión lectora en estudiantes con TDAH, comparando su eficacia entre distintos niveles de primaria. Este estudio subraya la relevancia de metodologías adaptadas a las necesidades de estudiantes con dificultades de aprendizaje. Finalmente, el Capítulo 10 explora la innovación educativa mediante la gamificación en la educación superior, mostrando una experiencia de éxito que podría aplicarse en áreas STEM. Este capítulo cierra el libro con una visión de futuro, destacando cómo el juego y la tecnología pueden motivar a los estudiantes y fomentar un aprendizaje activo. Esta indagación temática y metodológica muestra cómo la investigación educativa y el conocimiento abierto no solo permiten una comprensión más profunda de las necesidades y desafíos que enfrenta la educación actual, sino que también se configuran como motores esenciales de innovación y transformación. A través del análisis de herramientas de evaluación, metodologías activas, y el impacto de tecnologías emergentes y modelos alternativos de aprendizaje, esta obra ofrece una visión integradora que fomenta el avance del conocimiento pedagógico en distintos niveles educativos y contextos formativos. En última instancia, la obra subraya la importancia de una formación continua y adaptada a los cambios sociales y tecnológicos, aportando soluciones prácticas y teorías aplicadas que contribuyen al desarrollo de una educación inclusiva, resiliente y orientada a la colaboración. (shrink)

Nous vivons aujourd'hui une période de transformations technologiques d'une ampleur sans précédent, où l'IA s'impose comme un phénomène qui redessine en profondeur les structures de notre société. Le monde académique, socle premier de la production et de la diffusion du savoir, est particulièrement touché par cette révolution. En produisant des contenus de manière autonome, en introduisant de nouvelles dynamiques qui reconfigurent les pratiques académiques, l'IA met à l'épreuve les valeurs sur lesquelles repose la légitimité scientifique. En dix chapitres issus de (...) la conférence internationale de Coïmbra de 2024, les auteurs rappellent les principes de l'intégrité de la recherche, soulignent les dangers actuels et à venir. -/- Dans ce troisième ouvrage de l’IRAFPA (Institut International de Recherche et d’Action sur la Fraude et le Plagiat Académiques) publié chez EMS dans la collection « Questions de société », les auteurs nous invitent à considérer les transformations technologiques d’une ampleur sans précédent, où l’intelligence artificielle (IA) s’impose comme un phénomène qui redessine en profondeur les structures mêmes de notre société. Le monde académique, socle premier de la production et de la diffusion du savoir, est particulièrement affecté. -/- Au fil des chapitres, le livre Réinventer l’intégrité académique à l’ère de l’intelligence artificielle jette les bases d’une réflexion approfondie sur la manière dont l’intelligence artificielle, et plus particulièrement l’IA générative, bouleverse les paradigmes de la recherche, de l’enseignement et de la gestion des institutions académiques. Cette technologie a fait des avancées spectaculaires en très peu de temps. Elle offre aujourd’hui des opportunités sans précédent pour accélérer la production et la diffusion du savoir : elle permet de traiter des volumes de données massifs, d’automatiser des tâches complexes et d’optimiser la gestion des ressources humaines. Toutefois, ces succès indéniables s’accompagnent de défis inédits, notamment sur les plans de l’éthique, de l’intégrité académique et de l’organisation institutionnelle. -/- Les auteurs de cet ouvrage nous invitent à explorer les implications à long terme de l’IA à partir de leurs différentes perspectives disciplinaires : chirurgie, droit, éducation, gestion, informatique, médecine, philologie, philosophie, psychologie, psychanalyse et sociologie. Un modèle de production académique « à la chaîne », dans lequel la créativité humaine et l’intuition intellectuelle seraient partiellement confiées à des machines, bouleverserait les fondements mêmes de l’autorité et de la légitimité scientifique. En fin de compte, l’IA représente un défi pour les institutions académiques, non seulement en termes d’adaptation technique, mais aussi dans leur rôle social. À nous de nous saisir aujourd’hui de ce défi pour la connaissance. (shrink)

Dans son chapitre, Ignace Haaz analyse la pratique littéraire depuis son poste d’observation d’éditeur. Traditionnellement, la publication scientifique est le fruit d’un processus rigoureux de recherche, de vérification et de validation par les pairs. Cependant, avec la capacité de l’IA à générer de manière autonome des textes cohérents et détaillés, il devient plus difficile de distinguer les travaux véritablement innovants de ceux qui ne sont que des répliques ou des compilations automatisées de travaux existants. Comment garantir que l’utilisation de l’IA (...) ne conduira pas à une érosion de la rigueur linguistique ? Pour l’auteur de ce chapitre, une automatisation de la production scientifique poserait des questions cruciales sur l’authenticité et l’originalité du contenu d’un manuscrit. (shrink)

Advances in Artificial Intelligence (AI) have driven the evolution of reasoning in modern AI models, particularly with the development of Large Language Models (LLMs) and their "Think and Answer" paradigm. This paper explores the influence of human reinforcement on AI reasoning and its potential to enhance decision-making through dynamic human interaction. It analyzes the roots of bias and fairness in AI, arguing that these issues often stem from human data and reflect inherent human biases. The paper is structured as follows: (...) first, it frames reasoning as a mechanism for ethical reflection, grounded in dynamic learning and feedback loops; second, it discusses how scaling laws suggest that reinforcement learning (RL) can mitigate biases and promote fairness; third, it examines how RL allows models to view algorithmic bias and fairness as approximation problems. Through an experimental study, the paper demonstrates how AI models, empowered by RL, have successfully identified biases across various domains, including gender and socio-economic contexts, highlighting how reasoning improves algorithmic fairness. Ultimately, this work emphasizes the role of RL in mitigating algorithmic bias and enhancing fairness in AI systems. (shrink)

We propose a conceptualization and implementation of AI ethics via the capability approach. We aim to show that conceptualizing AI ethics through the capability approach has two main advantages for AI ethics as a discipline. First, it helps clarify the ethical dimension of AI tools. Second, it provides guidance to implementing ethical considerations within the design of AI tools. We illustrate these advantages in the context of AI tools in medicine, by showing how ethics-based auditing of AI tools in medicine (...) can greatly benefit from our capability-based approach. (shrink)

Many social media platforms enable (nearly) anyone to post (nearly) anything. One clear downside of this permissiveness is that many people appear bad at determining who to trust online. Hacks, quacks, climate change deniers, vaccine skeptics, and election deniers have all gained massive followings in these free markets of ideas, and many of their followers seem to genuinely trust them. At the same time, there are many cases in which people seem to reliably determine who to trust online. Consider, for (...) example, Do It Yourself (DIY) content about how to play guitar, bake, fix one’s plumbing, or repair one’s car. For these topics, those who have the largest accounts and the most popular content typically possess significant expertise. That is, social media users seem to reliably pick out DIY experts. We thus have a puzzle: why are social media users seemingly competent at identifying DIY experts, but not climate science or vaccine experts? In what follows, we solve this puzzle. We begin by identifying a novel wisdom of the crowds phenomenon: specifically we argue that the crowd (in combination with social media search and recommendation algorithms) reliably picks out DIY experts and serves as a credentialing institution for DIY content. Next, we argue that (a) there are five epistemic factors that determine whether the crowd can succeed at recognizing experts on social media platforms, and (b) while many of those factors are satisfied to a sufficiently high degree by DIY content, they are mostly lacking for content about climate change or vaccines. (shrink)

Nick Bostrom is perhaps best known for his 2014 book Superintelligence, which explores the existential threat posed to humans by superintelligent AI. In his new book, Deep Utopia, he shifts gears to explore the existentialist threat that superintelligent AI poses. Suppose that humans successfully navigate the technological, moral, and political challenges that come with advanced AI. The result of such success, according to Bostrom, would be a kind of deep utopia. We would not only live in a post-work, and post-scarcity (...) society (things already covered by shallower conceptions of utopia); we would also live in a “post-instrumental” society where almost anything we might consider doing, including pursuing our own personal enrichment, could be better accomplished by AI. Bostrom’s central concern is how we might find meaning and purpose in such a world. If everything you might consider doing can be done better by machine intelligence, what is there to live for? (shrink)

This article draws on Bernard Stiegler and Gilbert Simondon’s work to further interrogate the psychic, social, and political problems raised by the development of Artificial Intelligence. Stiegler’s political philosophy of time-consciousness reveals three concomitants urgencies: human memory is conditioned by industrial supplements that are increasingly disruptive, capitalism has produced an entropic condition where life on earth is threaten by toxic systems, the deployment of technologies of spirits has striped individuals of their psychic and collective individuation. I read media theory along (...) with Simondon’s philosophy to elucidate the question of mediation in today’s reticulated AI-driven society. (shrink)

Elon Musk famously predicted that an artificial intelligence superior to the smartest individual human would arrive by the year 2025. In response, Gary Marcus offered Musk a $1 million bet to the effect that he would be proved wrong. In specifying the conditions of this bet (which Musk did not take) Marcus lists the following ‘tasks that ordinary people can perform’ which, he claimed, AI will not be able to perform by the end of 2025. • Reliably drive a car (...) in a novel location that they haven’t previously encountered, even in the face of unusual circumstances like hand lettered signs, without the assistance of other humans. • Learn to ride a mountain bike off-road through forest trails. • Babysit children in an unfamiliar home and keep them safe. • Tend to the physical and psychological needs of an elderly or infirm person. Each of these tasks involves the application of practical or tacit knowledge, or what is also called ‘knowing how’, which is to say knowledge of a sort that is captured not by means of sentences or propositions or explicit rules, but rather through the expertise demonstrated in human actions. The talk addresses the puzzle that nowhere in the AI literature do we find a discussion of knowing how as a feature of human intelligence. (shrink)

Applications and devices of artificial intelligence are increasingly common in the healthcare field. Robots fulfilling some caregiving functions are not a distant future. In this scenario, we must ask ourselves if it is possible for machines to care to the extent of completely replacing human care and if such replacement, if possible, is desirable. In this paper, I argue that caregiving requires know-how permeated by affectivity that is far from being achieved by currently available machines. I also maintain that the (...) complete substitution of machine caregiving for human caregiving is not desirable because caregiving requires genuine human connection. (shrink)

The value of testimony as a source of knowledge has been a subject of epistemological debates. The "trust theory of testimony" suggests that human testimony is based on an affective relationship supported by social norms. However, the advent of generative artificial intelligence challenges our understanding of genuine testimony. The concept of "quasi-testimony" seeks to characterize utterances produced by non-human entities that mimic testimony but lack certain fundamental attributes. This article analyzes these issues in depth, exploring philosophical perspectives on testimony and (...) the implications of conversational AI technologies on our epistemic practices. (shrink)

The rapid integration of these artificial intelligence (AI) tools into our daily lives is reshaping how we think and make decisions. We propose that data-driven AI systems, by transcending individual artefacts and interfacing with a dynamic, multiartefact ecosystem, constitute a distinct psychological system. We call this ‘system 0’, and position it alongside Kahneman’s system 1 (fast, intuitive thinking) and system 2 (slow, analytical thinking).System 0 represents the outsourcing of certain cognitive tasks to AI, which can process vast amounts of data (...) and perform complex computations beyond human capabilities. It emerges from the interaction between users and AI systems, which creates a dynamic, personalized interface. Authors: Massimo Chiriatti, Marianna Ganapini, Enrico Panai, Mario Ubiali & Giuseppe Riva. (shrink)

This paper argues that the emotional cues exhibited by AI systems designed for social interaction may lead human users to hold misplaced trust in such AI systems, and this poses a substantial problem for human-AI relationships. It begins by discussing the communicative role of certain emotions relevant to perceived trustworthiness. Since displaying such emotions is a reliable indicator of trustworthiness in humans, we use such emotions to assess agents’ trustworthiness according to certain generalizations of folk psychology. Our tendency to engage (...) in anthropomorphism with artificial agents that display these communicative emotions may mislead us into trusting them, even when such trust is misplaced. This phenomenon is referred to as the Emotions-Trustworthiness Gap. This paper demonstrates this general concept with compassion and guilt. In both cases, we may be tempted to judge that AI systems' mere mimicking of relevant emotions constitutes evidence for their trustworthiness. However, most current AI systems are not trustworthy on any major view of trustworthy AI. Thus, the trust elicited from human users will be misplaced. This tendency of human users to hold misplaced trust in their AI companions leaves them vulnerable to serious harm. Several potential solutions to the Emotions-Trustworthiness Gap are discussed. (shrink)

There is an underappreciated respect in which the widespread availability of generative artificial intelligence (AI) models poses a threat to human connection. My central contention is that human creativity is especially capable of helping us connect to others in a valuable way, but the widespread availability of generative AI models reduces our incentives to engage in various sorts of creative work in the arts and sciences. I argue that creative endeavors must be motivated by curiosity, and so they must disclose (...) the creative agent’s inquisitive self. It is through self-disclosure, including the disclosure of the inquisitive self, that we put ourselves in a position to be seen by and connect with others through our creative pursuits. Because relying on AI for certain generative tasks is less self-disclosive than the creative work such technologies supplant, this reliance threatens to weaken our connections to one another. (shrink)

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

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

The use of Artificial Intelligence (AI) in educational gamification marks a significant advancement, transforming traditional learning methods by offering interactive, adaptive, and personalized content. This approach makes historical content more relatable and promotes active learning and exploration. This research presents an innovative approach to heritage education, combining AI and gamification, explicitly targeting the Silk Roads. It represents a significant progression in a series of research, transitioning from basic 2D textual interactions to a 3D environment using photogrammetry, combining historical authenticity and (...) immersive gameplay. It features AI-driven characters developed in Unreal Engine, which provides heritage insights augmented by personalized interactions, highlighting the importance of collaborative tools like interactive quizzes for enriching education and promoting critical thinking and community among learners. In conclusion, this research underlines the transformative impact of AI and gamification in heritage education enhancement, integrating real-time voice conversation and interactive pedagogical methods into immersive 3D environments as a supplementary tool. (shrink)

Background: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight (...) are needed. Here, we introduce "predomics", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements. -/- Results: Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data. -/- Conclusions: Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field. (shrink)

Abstract This article delves into the intricate interplay between copyright laws, Artificial Intelligence (AI) technologies, and the evolving role of authors in the contemporary digital landscape, especially after the irruption of Generative AI systems, as ChatGPT. The paper scrutinizes Japan’s approach to copyright in the realm of AI training, highlighting the delicate balance between safeguarding creators’ rights and fostering competitiveness in the global market. By examining the concept of “the death of the Author” as elucidated by Roland Barthes, the study (...) explores how AI-generated content challenges traditional notions of authorship and creativity. (shrink)

In the rapidly evolving landscape of artificial intelligence (AI), disparities in access and benefits are becoming increasingly apparent, leading to the emergence of an AI divide. This divide not only amplifies existing socio-economic inequalities but also fosters the creation of AI caste systems, where marginalized groups—referred to as AI Dalits—are systematically excluded from AI advancements. This article explores the definitions and contributing factors of the AI divide and delves into the concept of AI caste systems, illustrating how they perpetuate inequality. (...) Furthermore, it proposes comprehensive strategies for bridging this divide through inclusive and equitable approaches. Key strategies include promoting equal access to AI education and resources, enhancing diversity and representation in AI development, implementing fair and transparent AI policies, promoting public awareness and engagement, and empowering AI Dalits through targeted initiatives. By championing an "All in AI" and "AI for All" paradigm, this paper envisions an inclusive future where the benefits of AI are universally shared, ensuring that no group is left behind in the AI-driven world. (shrink)

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise them. (...) For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

In the rapidly evolving landscape of multimodal communication research, this paper explores the transformative role of machine learning (ML), particularly using multimodal large language models, in tracking, augmenting, annotating, and analyzing multimodal data. Building upon the foundations laid in our previous work, we explore the capabilities that have emerged over the past years. The integration of ML allows researchers to gain richer insights from multimodal data, enabling a deeper understanding of human (and non-human) communication across modalities. In particular, augmentation methods (...) have become indispensable because they facilitate the synthesis of multimodal data and further increase the diversity and richness of training datasets. In addition, ML-based tools have accelerated annotation processes, reducing human effort while improving accuracy. Continued advances in ML and the proliferation of more powerful models suggest even more sophisticated analyses of multimodal communication, e.g., through models like ChatGPT, which can now “understand” images. This makes it all the more important to assess what these models can achieve now or in the near future, and what will remain unattainable beyond that. We also acknowledge the ethical and practical challenges associated with these advancements, emphasizing the importance of responsible AI and data privacy. We must be careful to ensure that benefits are shared equitably and that technology respects individual rights. In this paper, we highlight advances in ML-based multimodal research and discuss what the near future holds. Our goal is to provide insights into this research stream for both the multimodal research community, especially in linguistics, and the broader ML community. In this way, we hope to foster collaboration in an area that is likely to shape the future of technologically mediated human communication. (shrink)

Mark Coeckelbergh is a Belgian philosopher who specializes in the philosophy of technology. His work primarily explores the intersection of technology and society, specifically the philosophical implications of emerging technologies such as AI and robotics. He has written on whether machines can be moral agents and how ethical frameworks should be applied to autonomous machines. He has a broad philosophical perspective drawing on classical sources, Eastern philosophy, Marxism, Foucault, phenomenology, and the postmodernists. In this short text, he brings his remarkable (...) insights and erudition to bear on our attempts at self-improvement in the age of AI. (shrink)

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? (...) And what sorts of evidence bear upon this question? -/- In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. (shrink)

International regulation of autonomous weapon systems (AWS) is increasingly conceived as an exercise in risk management. This requires a shared approach for assessing the risks of AWS. This paper presents a structured approach to risk assessment and regulation for AWS, adapting a qualitative framework inspired by the Intergovernmental Panel on Climate Change (IPCC). It examines the interactions among key risk factors—determinants, drivers, and types—to evaluate the risk magnitude of AWS and establish risk tolerance thresholds through a risk matrix informed by (...) background knowledge of event likelihood and severity. Further, it proposes a methodology to assess community risk appetite, emphasizing that such assessments and resulting tolerance levels should be determined through deliberation in a multistakeholder forum. The paper highlights the complexities of applying risk-based regulations to AWS internationally, particularly the challenge of defining a global community for risk assessment and regulatory legitimization. (shrink)

The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to partial data collection, rare updates, and significant resource demands. To address these issues, the article suggests that specific data management and Machine Learning techniques, such as natural language processing and sentiment analysis, can (...) improve the measurement and practice of IPD. (shrink)

Bài viết này giới thiệu năm yếu tố tiền đề đó với mục đích gia tăng nhận thức về quan hệ giữa người và máy trong bối cảnh công nghệ ngày càng thay đổi cuộc sống thường nhật. Năm tiền đề bao gồm: Tiền đề về cấu trúc xã hội, văn hóa, chính trị, và lịch sử; về tính tự chủ và sự tự do của con người; về nền tảng triết học và nhân văn của nhân loại; về hiện (...) tượng tiếp biến văn hóa sinh ra từ tương tác giữa người và máy; về tam giác tương tác giữa con người với máy tính, giữa con người với con người, và giữa con người với thiên nhiên. Trên cơ sở giải thích rõ về năm tiền đề này, bài viết đã đưa ra ba gợi ý cho Việt Nam về việc phát triển, ứng dụng, và quản lý trí tuệ nhân tạo. (shrink)

The present narrative review seeks to unravel where we are now, and where we need to go to delineate the active ingredients in psychotherapy delivered by conversational agents (e.g., chatbots). While psychotherapy delivered by conversational agents has shown promising effectiveness for depression, anxiety, and psychological distress across several randomized controlled trials, little emphasis has been placed on the therapeutic processes in these interventions. The theoretical framework of this narrative review is grounded in prominent perspectives on the active ingredients in psychotherapy. (...) Based on search terms derived from various theoretical perspectives, we conducted a systematic literature search of four scientific databases and identified 17 studies. Across the selected studies, three themes emerged: relationship variables, emotional venting, and cognitive factors. While methodological issues compromise the epistemic value of this evidence base, core questions also remain to be answered. Such questions include, but are not limited to, whether humans can form affectionate bonds to inanimate beings and whether these kind of mental health treatments should be understood as psychotherapy or something else. Researchers should therefore be cautious when applying theories of psychotherapy in the realm of conversational agents. We conclude the paper by introducing recommendations for future research, which we hope will help instigate methodologically sound studies in this field. (shrink)

Machina sapiens - l;algoritmo che ci ha rubato il segreto della conoscenza. -/- Le macchine possono pensare? Questa domanda inquietante, posta da Alan Turing nel 1950, ha forse trovato una risposta: oggi si può conversare con un computer senza poterlo distinguere da un essere umano. I nuovi agenti intelligenti come ChatGPT si sono rivelati capaci di svolgere compiti che vanno molto oltre le intenzioni iniziali dei loro creatori, e ancora non sappiamo perché: se sono stati addestrati per alcune abilità, altre (...) sono emerse spontaneamente mentre leggevano migliaia di libri e milioni di pagine web. È questo il segreto della conoscenza, ed è adesso nelle mani delle nostre creature? Cos'altro può emergere, mentre continuiamo su questa strada? (shrink)

For synergistic interactions between humans and artificial intelligence (AI) systems, AI outputs often need to be explainable to people. Explainable AI (XAI) systems are commonly tested in human user studies. However, whether XAI researchers consider potential cultural differences in human explanatory needs remains unexplored. We highlight psychological research that found significant differences in human explanations between many people from Western, commonly individualist countries and people from non-Western, often collectivist countries. We argue that XAI research currently overlooks these variations and that (...) many popular XAI designs implicitly and problematically assume that Western explanatory needs are shared cross-culturally. Additionally, we systematically reviewed over 200 XAI user studies and found that most studies did not consider relevant cultural variations, sampled only Western populations, but drew conclusions about human-XAI interactions more generally. We also analyzed over 30 literature reviews of XAI studies. Most reviews did not mention cultural differences in explanatory needs or flag overly broad cross-cultural extrapolations of XAI user study results. Combined, our analyses provide evidence of a cultural bias toward Western populations in XAI research, highlighting an important knowledge gap regarding how culturally diverse users may respond to widely used XAI systems that future work can and should address. (shrink)

What does it mean to be human? Philosophers and theologians have been wrestling with this question for centuries. Recent advances in cognition, neuroscience, artificial intelligence and robotics have yielded insights that bring us even closer to an answer. There are now computer programs that can accurately recognize faces, engage in conversation, and even compose music. There are also robots that can walk up a flight of stairs, work cooperatively with each other and express emotion. If machines can do everything we (...) can, does that mean we are machines? -/- This book examines whether an artificial person can be constructed and if so, what that might tell us about our future and ourselves. Different human capacities such as perception, creativity, consciousness, social behavior, and free will are described in separate chapters. Technological advances in these areas are summarized and compared to our own abilities. The book adopts a multi-disciplinary approach, with a naturalistic perspective drawn from biology and psychology matched against a technological perspective based on computer science and robotics. (shrink)

This research explores how a person with whom information has been shared and, importantly, an artificial intelligence (AI) system used to deduce information from the shared data contribute to making the disclosure context private. The study posits that private contexts are constituted by the interactions of individuals in the social context of intersubjectivity based on trust. Hence, to make the context private, the person who is the trustee (i.e., with whom information has been shared) must fulfil trust norms. According to (...) the commitment account of trustworthiness, a person is trustworthy only if they satisfy the norm of competence. It is argued that a person using an AI system to answer a question is competent only if they are ex post justified in believing what has been delivered by the AI system. A person’s belief is justified in the doxastic sense only if the AI system is accurate. This feature of AI’s performance affects a person’s competence and, as a result, trustworthiness. The effect of AI on trust as an essential component of making the context private, and thus on privacy, means an AI system also impacts privacy. Therefore, a private context is constituted when the individual with whom the information is shared fulfils the competence norm and the AI system used for analysing the information is sufficiently accurate to adhere to this norm. The result of this research emphasises the significance of the relationship between individuals involved in information-sharing and how an AI system used for analysing that information impacts the relationship regarding making the context private, as well as how it impacts privacy. The findings of this research have significant implications for improving or ameliorating privacy regulations in light of trust. (shrink)

A collection of philosophical fiction stories about the future, humanity, science, technology, artificial intelligence, and religion.

Deepfakes pose a multi-faceted threat to the acquisition of knowledge. It is widely hoped that technological solutions—in the form of artificially intelligent systems for detecting deepfakes—will help to address this threat. I argue that the prospects for purely technological solutions to the problem of deepfakes are dim. Especially given the evolving nature of the threat, technological solutions cannot be expected to prevent deception at the hands of deepfakes, or to preserve the authority of video footage. Moreover, the success of such (...) technologies depends on institutional trust that is in short supply. Finally, outsourcing the discrimination between the real and the fake to automated, largely opaque systems runs the risk of undermining epistemic autonomy. (shrink)

“Throw away anything that sounds too complicated. Only keep what is simple to grasp...If the information appears fuzzy and causes the brain to implode after two sentences, toss it away and stop listening. Doing so will make the news as orderly and simple to understand as the truth.” - In “GHG emissions,” The Kingfisher Story Collection, (Vuong 2022a).

The simulation hypothesis can reinforce a cynical dismissal of human potential. This attitude can allow online platform designers to rationalize employing manipulative neuromarketing techniques to control user decisions. We point to cognitive boosting techniques at both user and designer levels to build critical reflection and mindfulness.

A series of recent papers raises worries about the manipulative potential of algorithmic transparency (to wit, making visible the factors that influence an algorithm’s output). But while the concern is apt and relevant, it is based on a fraught understanding of manipulation. Therefore, this paper draws attention to the ‘indifference view’ of manipulation, which explains better than the ‘vulnerability view’ why algorithmic transparency has manipulative potential. The paper also raises pertinent research questions for future studies of manipulation in the context (...) of algorithmic transparency. (shrink)

The application of artificial intelligence (AI) to judicial decision-making has already begun in many jurisdictions around the world. While AI seems to promise greater fairness, access to justice, and legal certainty, issues of discrimination and transparency have emerged and put liberal democratic principles under pressure, most notably in the context of bail decisions. Despite this, there has been no systematic analysis of the risks to liberal democratic values from implementing AI into judicial decision-making. This article sets out to fill this (...) void by identifying and engaging with challenges arising from artificial judicial decision-making, focusing on three pillars of liberal democracy, namely equal treatment of citizens, transparency, and judicial independence. Methodologically, the work takes a comparative perspective between human and artificial decision-making, using the former as a normative benchmark to evaluate the latter. -/- The chapter first argues that AI that would improve on equal treatment of citizens has already been developed, but not yet adopted. Second, while the lack of transparency in AI decision-making poses severe risks which ought to be addressed, AI can also increase the transparency of options and trade-offs that policy makers face when considering the consequences of artificial judicial decision-making. Such transparency of options offers tremendous benefits from a democratic perspective. Third, the overall shift of power from human intuition to advanced AI may threaten judicial independence, and with it the separation of powers. While improvements regarding discrimination and transparency are available or on the horizon, it remains unclear how judicial independence can be protected, especially with the potential development of advanced artificial judicial intelligence (AAJI). Working out the political and legal infrastructure to reap the fruits of artificial judicial intelligence in a safe and stable manner should become a priority of future research in this area. (shrink)

Psychoanalysis, particularly as articulated by figures like Freud and Lacan, highlights the inherent division within the human subject—a schism between the conscious and unconscious mind. It could be said that this suggests that such an internal division becomes amplified in the context of generative art, where technology and algorithms are used to generate artistic expressions that are meant to emerge from the depths of the unconscious. Here, we encounter the tension between the conscious artist and the generative process itself, which (...) may yield unexpected, even uncontrollable results. -/- This paper, therefore, seeks to address this division within the modern subject and its relationship to technology, wherein the division within the living body is revealed through the presence of prosthetic elements, which mirrors the division brought about by the incorporation of language as a signifier. I argue that the amplification of this internal schism does not necessarily lead to a more fractured subject. Instead, generative art, bolstered by advancements in AI and machine learning, offers a unique opportunity for individuals to externalize and explore their minds in novel ways. -/- By examining contemporary works such as Hal Foster’s Prosthetic Gods, which stands as a pivotal exploration of the convergence between modernist art and psychoanalytic theory and Isabel Millar’s Psychoanalysis of Artificial Intelligence, this paper elucidates the profound implications of Freud’s vision of modern -/- subjectivity as Prothesengott (Prosthetic God) and address the questions concerning this technological imbrication of the human mind and body through the Lacanian framework. Although for Freud, Man does not become a real God, rather, the potential to transcend one’s limitations ascribes us to God-like qualities by seeking to generate new forms of life that go beyond merely reproducing nature — a transcention of the natural. Millar emphasizes that Freud observes that this is evidenced by the fact that these additional organs remain distinct from the organism and can never assimilate into it. One continually falls short of realizing the fantasy he envisions, opting instead to use his supplementary artificial organs to endlessly revolve around the objects of the drive. -/- This evolving relationship that the drive has with its technological objects, resounds in Lacan’s conception of “lathouse” which allows extimate objects to convert interiority (unconscious) into exteriority (conscious) and exteriority into interiority. The thesis of this paper seeks to employ this underutilized concept to understand the nature of human subjectivity and its bodily and structural relationship to generative art. Therefore, this paper emphasizes what really happens when we enter into this relationship with the lathouse, whereby this artificial object has effects in the "real of jouissance", where these Lathouses create a network, namely the Alethosphere. My goal is to argue that generative art as a technological development, can be seen as an extension to the development of the drive. Conclusively, I make the claim for generative art's potential to externalize the human creative drive by emphasizing the interplay between randomness and structure, and how it offers a means to surpass our inherent limitations by presenting an avenue for self-expression that transcends traditional modes of art. (shrink)