La scorciatoia - Come le macchine sono diventate intelligenti senza pensare in modo umano -/- Le nostre creature sono diverse da noi e talvolta più forti. Per poterci convivere dobbiamo imparare a conoscerle Vagliano curricula, concedono mutui, scelgono le notizie che leggiamo: le macchine intelligenti sono entrate nelle nostre vite, ma non sono come ce le aspettavamo. Fanno molte delle cose che volevamo, e anche qualcuna in più, ma non possiamo capirle o ragionare con loro, perché il loro comportamento è (...) in realtà guidato da relazioni statistiche ricavate da quantità sovrumane di dati. Eppure possono essere in certi casi più potenti di noi: ci osservano continuamente, e prendono decisioni al nostro posto. E allora come incorporarle nella nostra società senza rischi ed effetti collaterali? Questo libro - rigoroso, pungente, originale nell'approccio - ci spiega come siamo arrivati sin qui, e indica il percorso che ci aspetta prima di poterci fidare di questi nuovi agenti «alieni». La tecnologia non basta, occorre un dialogo tra scienze naturali e umane: è il passaggio cruciale per una convivenza sicura con questa nuova forma di intelligenza. (Il Mulino). (shrink)

Riassunto: Sviluppandosi sul piano della “prevenzione del rischio”, dei “livelli di controllo” in fase di programmazione e del possibile inserimento dei cosiddetti “algoritmi etici”, il dibattito sul presente e il futuro dell’Intelligenza Artificiale, ha favorito nel corso degli anni la creazione di una sempre più profonda spaccatura tra discipline “tecniche” e saperi “umanistici”, tra il dominio del “fare” e il dominio del “pensare”. Prendendo atto di questa sterile distanza, occorre mettere in questione i metodi dell’etica e interrogarsi sull’efficacia e (...) l’utilità della teoria e, più in generale, meglio definire la relazione che la filosofia oggi intrattiene – e potrà ancora intrattenere – con le macchine progettate per pensare e con i loro progettisti. Parole chiave: Filosofia; Etica; Intelligenza artificiale; Cibernetica; Controllo Beyond ethics: An anthropodecentric approach to artificial intelligence Riassunto: The ethical and philosophical debate around the present and future of Artificial Intelligence has grown in intensity over the years. Continual development within AI in terms of “prevention of risks”, “levels of control in programming”, and the inclusion of so-called “ethical algorithms”, has encouraged an ever deeper split between “technicians” and “humanists”, between the domains of “making” and “thinking”. Focusing on this unproductive distance, we interrogate ethical approaches methods and the effectiveness and utility of ethical theories. More generally, we attempt to better define the relationship that philosophy has today – and will be able to maintain in the future tomorrow – with machines designed to think and with their designers. Keywords: Philosophy; Ethics; Artificial Intelligence; Cybernetics; Control. (shrink)

The notion of context arises in assorted areas of artificial intelligence (AI), including knowledge representation, natural language processing, intelligent information retrieval, etc. Although the term ‘context’ is frequently employed in descriptions, explanations, and analyses of computer programs in these areas, its meaning is frequently left to the reader’s understanding. -/- My aim in this paper is to offer a swift review of context in AI. I will first identify the role of context in various fields of AI. I will then (...) consider the enterprise of formalising context. The focus of my discussion will be McCarthy's (1993) proposal – a landmark study that inspired almost every logical theory of context since then – and its close variants, studied by his students. -/- (The author is grateful to Prof. Carlo Penco for his invaluable assistance with this work.). (shrink)

As technologies become more and more pervasive in our everyday life new questions arise, for example, about security, accountability, fairness and ethics. These concerns are about all the realities that are involved or committed in designing, implementing, deploying and using the technology. This document addresses such concerns by presenting a set of practical obligations and recommendations for the development of applications and systems based on Artificial Intelligence (AI) techniques. These are derived from a definition of rights resulting from principles and (...) ethical values rooted in the foundational charters of our social organization. (shrink)

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and (...) the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment. (shrink)

Artificial intelligence is man's attempt to use software to emulate the intelligence of human beings. But the complexity of the human neurological system formed in the course of evolution is impossible to replicate: "Human languages and societies are complex systems, indeed complex systems of many complex systems," so much so that their mathematical modeling is impossible. Barry Smith, philosopher and professor at the University at Buffalo. shows no uncertainty about this. His latest book written with Jobst Landgrebe, a mathematician and (...) entrepreneur in the AI field, leaves no doubt – starting with the title: Why machines will never rule the world (Routledge 2023). The book was presented in Turin during an evening organized by the Fondazione CRT and the Bruno Leoni Institute, in which Smith was interviewed based on questions developed by generative Ai. We have accepted the challenge to conduct an interview parallel to the one done with GPT4. (shrink)

The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; (...) class='Hi'>recommender systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI. (shrink)

In this paper, I will identify two problems of trust in an AI-relevant context: a theoretical problem and a practical one. I will identify and address a number of skeptical challenges to an AI-relevant theory of trust. In addition, I will identify what I shall term the ‘scope challenge’, which I take to hold for any AI-relevant theory of trust that purports to be representationally adequate to the multifarious forms of trust and AI. Thereafter, I will suggest how (...) trust-engineering, a position that is intermediate between the modified pure rational-choice account and an account that gives rise to trustworthy AI, might allow us to address the practical problem of trust, before identifying and critically evaluating two candidate trust-engineering approaches. (shrink)

In this essay, we explore the philosophical and theoretical resonances of the artwork Qatipana from the perspective of some key insights of Gilbert Simondon’s information processing system approach. Qatipana (Quechua word that means flow, sequence, transmission) is a hybrid ecosystem of information flow which, even though not the kind of dispositive systems theory was designed to read, offers some valuable empirical insights to test some key aspects of Simondon’s information processing systems. In particular, we are interested in observing how (...) Simondon’s becoming and individuation play out in an algorithmic cycle performed by the cognitive system of an Artificial Intelligence agent: how does mono-technology and computerization of cultural techniques influence the nature of knowing the affection of being with others (people, things, animals)? We contrast Simondon’s contributions with the work of Norbert Wiener and Stafford Beer on information in cybernetics and the cosmotechnics and Technodiversity of Chinese philosopher Yuk Hui. Furthermore, we offer a provisional assessment of the reach, limits, and possibilities this kind of symbiotic crossing of technologies and arts: it is a field filled with challenges and unresolved complexities that should encourage us to recognize the urgency that is implied in the disruptive (and creative) we can see unfolding in social and political life. The already pervasive (and growing) presence of computational systems has become both a tremendous risk and a vertiginous deployment of liberating opportunities. Latin America and elsewhere are desperately calling for an insightful and imaginative engagement. (shrink)

Current methods to capture, analyse and present the audience participation of broadcast events are increasingly carried out using social media. Uptake of such technology tools has so far been poor amongst older adults, and it has the worrying effect of excluding the demographic from participation. Our work explores whether a common desire to interact with debates can be tapped with technology with a very low barrier to entry, to both support better engagement with broadcast debates and encourage greater use of (...) social media. This paper describes experiments where older adults interact with a BBC radio debate programme: The Moral Maze. As a result, we obtained common interaction patterns which then are used to define recommendations for software-supported interaction with debates based on theories of argumentation. Our goal is to combine research on computational models of argument and user-driven research on human-centred computing in a project with the potential for high-profile impact in addressing older adults inclusion in the digital economy. (shrink)

This paper examines the question of whether Large Language Models (LLMs) like ChatGPT possess minds, focusing specifically on whether they have a genuine folk psychology encompassing beliefs, desires, and intentions. We approach this question by investigating two key aspects: internal representations and dispositions to act. First, we survey various philosophical theories of representation, including informational, causal, structural, and teleosemantic accounts, arguing that LLMs satisfy key conditions proposed by each. We draw on recent interpretability research in machine learning to support these (...) claims. Second, we explore whether LLMs exhibit robust dispositions to perform actions, a necessary component of folk psychology. We consider two prominent philosophical traditions, interpretationism and representationalism, to assess LLM action dispositions. While we find evidence suggesting LLMs may satisfy some criteria for having a mind, particularly in game-theoretic environments, we conclude that the data remains inconclusive. Additionally, we reply to several skeptical challenges to LLM folk psychology, including issues of sensory grounding, the "stochastic parrots" argument, and concerns about memorization. Our paper has three main upshots. First, LLMs do have robust internal representations. Second, there is an open question to answer about whether LLMs have robust action dispositions. Third, existing skeptical challenges to LLM representation do not survive philosophical scrutiny. (shrink)

L’articolo esamina la proposta europea di regolamento sull’intelligenza artificiale, AI Act (AIA). In particolare, esamina il modello di analisi e valutazione del rischio dei sistemi di IA. L’articolo identifica tre potenziali problemi di implementazione del regolamento: (1) la predeterminazione dei livelli di rischio, (2) la genericità del giudizio di significatività del rischio e (3) l’indeterminatezza della valutazione sull’impatto dei diritti fondamentali. Il saggio suggeriscealcune soluzioni per affrontare questi tre problemi.

Health Recommender Systems are promising Articial-Intelligence-based tools endowing healthy lifestyles and therapy adherence in healthcare and medicine. Among the most supported areas, it is worth mentioning active aging. However, current HRS supporting AA raise ethical challenges that still need to be properly formalized and explored. This study proposes to rethink HRS for AA through an autonomy-based ethical analysis. In particular, a brief overview of the HRS’ technical aspects allows us to shed light on the ethical risks and challenges they (...) might raise on individuals’ well-being as they age. Moreover, the study proposes a categorization, understanding, and possible preventive/mitigation actions for the elicited risks and challenges through rethinking the AI ethics core principle of autonomy. Finally, elaborating on autonomy-related ethical theories, the paper proposes an autonomy-based ethical framework and how it can foster the development of autonomy-enabling HRS for AA. (shrink)

In 2021, 193 Member States at UNESCO’s General Conference adopted the Recommendation on the Ethics of Artificial Intelligence as the first important step towards a future global standard-setting instrument on the subject. The text reflects an emerging consensus among the international community about the growing ethical concerns with artificial intelligence (AI). Among these concerns are also serious risks and dangers attributed to the manipulative effects of AI, which can be further exacerbated by the creative combination of AI with other innovative (...) technologies or applications, such as brain–computer interfaces (BCIs), functional magnetic resonance imaging (fMRI), robotics, and big data. The risks for individuals and society as a whole caused by manipulation through AI are already well known and have recently also been addressed by the European Union having released a proposal for an Artificial Intelligence Act (AIA). Among multiple risks related to AI, the AIA singles out the specific dangers related to AI systems that deploy “subliminal techniques beyond a person’s consciousness to materially distort a person’s behaviour in a manner that causes or is likely to cause that person or another person physical or psychological harm”. The present article thus aims to highlight the known and potential dangers related to AI systems manipulating human thoughts and behaviour through subliminal and supraliminal means and methods. To this end, it advocates the joint study of law and the senses captured by the concept of legal synaesthesia to correspond to the need for an interdisciplinary debate covering the complexity of the links between AI, related technologies, human perception based on the senses and the mind, as well as the role and instruments of law in the future organization of societies in this world. (shrink)

Generative AI has captured the public imagination as a tool that promises access to expertise beyond the technical jargon and expense that traditionally characterize such infospheres as those of medicine and law. Largely absent from the current literature, however, are interrogations of generative AI’s abilities to deal in culturally responsive communication, or the expertise interwoven with culturally aware, socially responsible, and personally sensitive communication best practices. To interrogate the possibilities of cultural responsiveness in generative AI, we examine the patterns of (...) response that characterize ChatGPT-3.5’s advice for coming out. Specifically, we submitted 100 prompts soliciting coming out advice to GPT-3.5, variegating each of those prompts slightly to account for intersectional identities. From the analysis, we find that, while the responses are largely in-line with best practices, there are also instances that might represent problematics concerning the interpellation of the user or the persons to whom one is coming out. (shrink)

Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) health, and judicial (...) contexts. Data from a scenario-based survey experiment with a national sample (N = 958) show that people are by and large concerned about risks and have mixed opinions about fairness and usefulness of automated decision-making at a societal level, with general attitudes influenced by individual characteristics. Interestingly, decisions taken automatically by AI were often evaluated on par or even better than human experts for specific decisions. Theoretical and societal implications about these findings are discussed. (shrink)

This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial intelligence is viewed as amongst the technological advances that will reshape modern societies and their relations. While the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories (...) use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us, ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all. (shrink)

Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU´s new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation. Taken together, the article explores how the (...) AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasizing how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA. (shrink)

What is Understanding? This is the first of a series of Chats with OpenAI’s ChatGPT (Chat). The main goal is to obtain Chat’s response to a series of questions about the concept of ’understand- ing’. The approach is a conversational approach where the author (labeled as user) asks (prompts) Chat, obtains a response, and then uses the response to formulate followup questions. David Deutsch’s assertion of the primality of the process / capability of understanding is used as the starting point. (...) Understanding is posited as a major step in the Big Picture description of emergence in the Universe. Understanding follows the emergence of Brains and Minds, culminating in Power Over the Universe. My evaluation suggests that Chat does indeed offer utility, but also has limitations manifested by overly general responses that do not focus tightly on the questions asked. Chat does yield some gems that could be easily missed. The approach used here includes creating Metaprompts, wherein Chat is asked to create more efficient prompts based on initial promptsThis approach appears to hold significant promise, and will likely be of benefit to those exploring various facets of Artificial Intelligence (AI), Large Language Models (LLMs) and designers of Advanced General Intelligence (AGI). Other findings include the need for creating new metrics and ways of discussing intelligence, and various adjacent areas and subprocesses associated with intelligence. Discussions of future directions is also included. (shrink)

A simple argument suggests that we can fruitfully model advanced AI systems using expected utility theory. According to this argument, an agent will need to act as if maximising expected utility if they’re to avoid exploitation. Insofar as we should expect advanced AI to avoid exploitation, it follows that we should expected advanced AI to act as if maximising expected utility. I spell out this argument more carefully and demonstrate that it fails, but show that the manner of its (...) failure is instructive: in exploring the argument, we gain insight into how to model advanced AI systems. (shrink)

In our book, Why Machines Will Never Rule the World, Jobst Landgrebe and I argue that we can engineer machines that can emulate the behaviours only of simple systems, which means: only of those systems whose behaviour we can predict mathematically. The human brain is an example of a complex system, and thus its behaviour cannot be emulated by a machine. We use this argument to debunk the claims of those who believe that large language models are poised to achieve (...) a level of intelligence that will equal or even surpass that of the human brain. The essay was published under the title "Why Machines Will Never Rule the World". (shrink)

In the debate on whether to ban LAWS, moral arguments are mainly used. One of these arguments, proposed by Sparrow, is that the use of LAWS goes hand in hand with the responsibility gap. Together with the premise that the ability to hold someone responsible is a necessary condition for the admissibility of an act, Sparrow believes that this leads to the conclusion that LAWS should be prohibited. In this article, it will be shown that Sparrow’s argumentation for both premises (...) is not convincing. If one interprets the thesis that responsibility (first premise) is necessary in a descriptive sense, this assertion clashes with military theory and practice. And even if you focus on the normative interpretation, that claim does not stand. The second premise for Sparrow’s conclusion, namely that you cannot hold anyone responsible for LAWS’ (mis)deeds, is based on the idea that control is a necessary condition for responsibility. It will be shown that this idea too is not correct, which means that Sparrow’s control argument does not do the work it should do. From this, we can conclude that Sparrow’s justification for his claim that LAWS should be banned is insufficient, and neither can we conclude that the thesis of a responsibility gap has in any case been undermined. However, it will also be argued that someone may be responsible for the actions of LAWS, or that it cannot be excluded that one can be held responsible. (shrink)

Artificial intelligence represents a key technology being inbuilt into evermore products. Research investigating attitudes towards artificial intelligence surprisingly is still scarce, although it becomes apparent that artificial intelligence will shape societies around the globe. To better understand individual differences in attitudes towards artificial intelligence, the present study investigated in n = 351 participants associations between the Affective Neuroscience Personality Scales (ANPS) and the Attitudes towards Artificial Intelligence framework (ATAI). It could be observed that in particular higher levels of SADNESS were (...) associated with higher negative attitudes towards AI (fearing AI). The findings are discussed in this work from an evolutionary perspective because primary emotional systems—according to Affective Neuroscience Theory—represent tools for survival, which have been homologously conserved across mammalian species including homo sapiens. (shrink)

Responsibility has become a central concept in AI ethics; however, little research has been conducted into practitioners’ personal understandings of responsibility in the context of AI, including how responsibility should be defined and who is responsible when something goes wrong. In this article, we present findings from a 2020–2021 data set of interviews with AI practitioners and tech workers at a single multinational technology company and interpret them through the lens of feminist political thought. We reimagine responsibility in the context (...) of AI development and deployment as the product of work cultures that enable tech workers to be responsive and answerable for their products over the long and short term. From our interviews, we identify three key pain points in understanding the distribution of responsibility between actors and developing responsible design and deployment practices: (1) unstable business ecosystems and AI lifecycles, which require an approach to responsibility that accounts for the dynamic nature of these systems; (2) the issue of incentivizing engineers to take responsibility for the mundane maintenance practices essential to the functioning of AI systems and (3) the need to overcome individual and structural barriers to taking ownership over AI products and their effects. From these findings, we make three recommendations based on feminist theory: (1) organisations should move from a static model of responsibility to a dynamic and ethically motivated response-ability; (2) companies need to revalue care and maintenance practices; and (3) firms must move away from individualistic ideas of responsibility towards fostering wider cultures of responsibility. (shrink)

This research attempts to construct a second order observation model in understanding the significance of Artificial intelligence (AI) in changing the global power structure. Because of the inevitable ubiquity of AI in the world societies’ near future, it impacts all the sections of society triggering socio-technical iterative developments. Its horizontal impact and states’ race to become leader in the AI world asks for a vivid understanding of its impact on the international system. To understand the latter, Triple Helix (TH) model (...) along with Shannon’s information entropy has been used to operationalize system’s theory. This model uses Shannon’s information theory to calculate the uncertainty generated from the interactions between the sub-systems considered. Data for the latter has been taken from Sanford Artificial Intelligence Laboratory 2019 report. It is found that European countries are the most effected in the AI era, with probability of losing their global influence and thus creating power void. Emerging powers such as India, Canada, South Africa and Brazil have better chances to fill the void and emerge as global influences. (shrink)

Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine’s behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today’s AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make (...) better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call “decision ownership”: they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made. (shrink)

The objective of this study is to examine the effects of generative artificial intelligence (AI) tools, with a specific focus on ChatGPT, on the analytical proficiencies of policy analysts operating in Indonesia. Considering the increasing intricacies of contemporary governance and the emergence of "wicked problems," this study investigates the potential of AI to facilitate the development of inventive, data-centric public policies. Involving postgraduate students in a quasi-experimental design, this study investigated the efficacy of ChatGPT in assisting in the development of (...) policy solutions pertaining to air pollution in Jakarta. Specialized prompt engineering techniques are utilized in this study to enhance the utility of ChatGPT for policy analysis. The results suggest that, although generative AI tools such as ChatGPT provide systematic frameworks for tackling complex policy issues and may enhance analytical skills, they still require additional refinement to increase their empirical support and practical applicability in the Indonesian policy context. In accordance with incrementalism theory, the study concludes that incorporating AI into policy analysis can facilitate the gradual formulation of evidence-based policies, offering a pragmatic approach to the resolution of complex problems in the public sector. (shrink)

La crescita esponenziale delle tecnologie di AI come il machine learning e le reti neurali, nonché l'integrazione tecnica dei sistemi cyber-fisici nella produzione, nei servizi logistici e nei processi industriali, rende il mondo dell'industria 4.0 sempre più vicino ed accessibile nei paesi industrializzati. La repentina transizione odierna dai processi di produzione di massa verso l'economia dell'informazione, che scaturisce da queste innovazioni, crea zone di ombra e penombra sul futuro incerto del mercato del lavoro umano. Come in ogni rivoluzione tecnologica, la (...) capacità umana di rimanere al centro del proprio processo di produzione sembra drammaticamente a rischio. Questo articolo si propone di operare un resoconto specifico dell'impatto che l'informatizzazione eserciterà sul mercato del lavoro. Dapprima, il lettore verrà introdotto ad un'analisi storica della metamorfosi del lavoro umano nella terza rivoluzione industriale, a seguito dell'introduzione di tecnologie di automazione, che permetterà di operare paragoni critici con la situazione odierna. Verrà quindi delineato un quadro saliente sulle opportunità e le complesse problematiche che l'automazione odierna e futura comporterà su un numero significativo di attività lavorative umane, a causa della disoccupazione, e sui prezzi di produzione. L'impatto delle tecnologie dette "disruptive", come l'AI, non si limita a conseguenze economiche. La perdita di una percentuale importante dei posti di lavoro produrrà profonde conseguenze indirette sulle strutture socio-simboliche e sulle pratiche organizzative delle comunità umane. Attorno alla nozione di "lavoro" orbita oggi una parte essenziale dell'universo simbolico delle società, la cui revisione porterà serie conseguenze esistenziali per g​ . (shrink)

This study investigates the relationship between the usage of artificial intelligence (AI), specifically AI-based personalized recommendations (AI-PR), and its impact on consumers’ green product awareness (GPA) during online shopping. Recognizing that AI’s influence alone may not significantly affect green buying behaviours, we introduce emotional intelligence (EI) as a moderator to better understand human impact when using AI. The research framework is built upon the Interactive Recommendation Agents and the SOBC framework. A self-administered survey was conducted with 403 respondents, and the (...) data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that AI and AI-PR positively impact GPA, with partial mediation observed among the variables. Furthermore, emotional intelligence positively moderates the relationship between perceived AI usage and GPA. Specifically, individuals with higher emotional intelligence exhibit a stronger positive association between AI usage and GPA. Conversely, emotional intelligence negatively moderates the relationship between AI-based personalized recommendations and GPA, indicating that higher EI weakens the relationship between AI-PR and GPA, while lower EI strengthens it. The implications of this study are significant for marketers and retailers aiming to utilize AI-based personalized recommendations to encourage green behaviour and contribute to a sustainable future. These insights can help them understand the complex dynamics between AI, emotional intelligence, green product awareness, and behavioural intentions. (shrink)

The foundations of AI design discourse are worth analyzing. Here, attention is paid to the nature of theory languages used in designing new AI technologies because the limits of these languages can clarify some fundamental questions in the development of AI. We discuss three types of theory language used in designing AI products: formal, computational, and natural. Formal languages, such as mathematics, logic, and programming languages, have fixed meanings and no actual-world semantics. They are context- and practically content-free. (...) Computational languages use terms referring to the actual world, i.e., to entities, events, and thoughts. Thus, computational languages have actual-world references and semantics. They are thus no longer context- or content-free. However, computational languages always have fixed meanings and, for this reason, limited domains of reference. Finally, unlike formal and computational languages, natural languages are creative, dynamic, and productive. Consequently, they can refer to an unlimited number of objects and their attributes in an unlimited number of domains. The differences between the three theory languages enable us to reflect on the traditional problems of strong and weak AI. (shrink)

BIG DATA AND ARTIFICIAL INTELLIGENCE: A LOOK INTO THE FUTURE To say or write something innovative on the ongoing revolution in the fields of Big Data and Artificial Intelligence is very difficult. The advent of these two new technologies is in fact among the most relevant events in human history since in a little more than a decade it will likely lead to the creation of the First Artificial Intelligence of the Fourth level: i.e capable to think and create autonomously. (...) This is a strong statement but like it or not is also irrefutable and fully compatible with the current technological trends and with what is happening right now, in front of our eyes. For the time being, however, we are dealing just with a weak Artificial Intelligence deeply rooted in Big Data and Big Data Analytics. Even so, however, the epochal changes which are currently taking place in the social, economic, and political structure of our world, have no historical equivalent. In this essay, after a short introduction (for the nonprofessional) of Machine Learning and of the basics of the weak Artificial Intelligence, I will try to explain why Artificial Intelligence and Big Data inextricably connect to each other. I will also try to summarise some among the many positive and negative aspects of the problem. (shrink)

The field of artificial intelligence (AI) can be characterized as the investigation of computational systems that exhibit intelligent behavior (including algorithms and models used in these systems). The emphasis is not so much on understanding (human) cognitive processes as on producing models, algorithms, and systems that are capable of apparently intelligent behavior by whatever means available. The idea of AI has had a long history that can be traced all the way back to, for example, Leibniz. The idea was furthered (...) through the development, early in this century, of mathematical logic, cybernetics, and information theory, all of which contributed to AI theorizing. Alan Turing's bold claim that machines can think, advanced in his famous 1950 paper, and the “Turing test” he proposed therein as a means to test the intelligence of machines predated and prompted the formation of AI. Six years later, the Dartmouth conference in 1956, the participants of which included Allen Newell, Herbert Simon, Marvin Minsky, and John McCarthy, inaugurated AI as an academic discipline. This contemporary version of AI was facilitated by the invention and rapidly widening use of digital computers. AI embodies an extremely diverse set of ideas that are often mutually conflicting (as in the case of symbolism versus connectionism), and its relationship with other fields studying cognition (such as psychology and linguistics) has been a close but uneasy one. (shrink)

Explainable AI (XAI) provides methods to understand non-interpretable machine learning models. However, we have little knowledge about what legal experts expect from these explanations, including their legal compliance with, and value against European Union legislation. To close this gap, we present the Explanation Dialogues, an expert focus study to uncover the expectations, reasoning, and understanding of legal experts and practitioners towards XAI, with a specific focus on the European General Data Protection Regulation. The study consists of an online questionnaire and (...) follow-up interviews, and is centered around a use-case in the credit domain. We extract both a set of hierarchical and interconnected codes using grounded theory, and present the standpoints of the participating experts towards XAI. We find that the presented explanations are hard to understand and lack information, and discuss issues that can arise from the different interests of the data controller and subject. Finally, we present a set of recommendations for developers of XAI methods, and indications of legal areas of discussion. Among others, recommendations address the presentation, choice, and content of an explanation, technical risks as well as the end-user, while we provide legal pointers to the contestability of explanations, transparency thresholds, intellectual property rights as well as the relationship between involved parties. (shrink)

Although Artificial Intelligence (AI) and Machine Learning (ML) are attracting a lot of scientific and engineering attention nowadays, nothing up to now has been achieved to reach the level of building machines that possess human-like intelligence. Though, the engineering community continuously claims that several engineering problems are solved using AI or ML. Here, it is argued that engineers are not being able to build intelligent machines, implying that the systems claimed to have AI/ML belong to different engineering domains. The base (...) of the syllogism is the existence of four main obstacles on which extensive elucidation is performed. In addition, attempt to clear out the developed confusion, mis-use and ab-use of the phrases “Artificial Intelligence” and “Machine Learning” by scientists and engineers is carried out. Furthermore, mathematical, and philosophical approaches are also mentioned that strengthen the argument against AI implementability as part of the whole syllogism. Finally, an alternative approach (not being unique) is suggested and discussed for performing research on AI and ML by the engineers. It is based on complexity theory and non-linear adaptive systems and provides the benefit of eliminating the before mentioned pragmatic and philosophical obstacles that engineers are facing and ignoring, without creating confusion on this scientific endeavor. (shrink)

The monster is created as a marvel, as something which, like transgression itself, does not belong to the assumed natural order of things. It is a liminal creature which embodies the very boundaries humans have overreached. The latest perspectives on the concept of the monstrous are connected to the new technological experiments and to artificial intelligence. Technology problematizes the real vs the virtual, the human vs the inhuman, the concept itself of legal persona: all this sets itself within a posthuman (...) scenario. The movies Transcendence and Her perfectly epitomize these concepts, because in the former the conscience of the scientist Will is downloaded into a computer, from which he emerges as a new monstrous entity; in the latter a computer program, a voice, so artfully interacts with man as to give the impression of a reasonable and independent human being. (shrink)

This article is a philosophical study that examines power and resistance within the conceptual network of post-phenomenological theories of technological mediation. In the contemporary landscape, the interaction between human beings and technology plays a crucial role in shaping social, cultural, and political dynamics. As human beings increasingly emerge into a world permeated by technological innovations, it becomes essential to understand the complex power relations that arise from this intertwining. In this context, post-phenomenological theories of technological mediation offer a valuable theoretical (...) framework capable of exploring the nuances of these interactions. The study begins by positioning power and resistance within this conceptual network. It then explores the complex interactions of power in technologically mediated relationships, with particular emphasis on the technologies incorporated into social media platforms, i.e., algorithmic recommendation systems. Drawing on the concepts of power as defined by Michel Foucault and Byung-Chul Han, the paper challenges conventional understandings of productive power by examining how these platforms shape and exert influence. Ultimately, it introduces the concept of intelligent power as a means to better understand the power dynamics inherent in algorithmic mediation. (shrink)

Here is a fine semipopular book about the ideas which have motivated the much-talked-about revolution in the theories of information, control and communication. Jagjit Singh is one of those rare science writers who knows how to present intricate technical concepts to the less-than-expert reader without compromising the original sense or significance. The book begins, appropriately enough, with a discussion of the concept of information, culminating in the technical definition which enables us to assign numerical values to its quantity. The following (...) chapters present the conceptual heart of the Shannon-Weaver theory, the theories of automatic computing machines and the intriguing neurological theories of Warren McCulloch and John Von Neumann. The chapter on Turing Machines does not pay enough attention to the purely mathematical applications of these devices for the present reviewer's taste. The closing chapters deal with mathematical theories of intelligence and the brain. The text is accompanied by many good drawings and appropriate mathematical demonstrations. A book of this quality could have been produced only by an author with absolute command of his topic.—H. P. K. (shrink)

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

Large language models such as ChatGPT enable users to automatically produce text but also raise ethical concerns, for example about authorship and deception. This paper analyses and discusses some key philosophical assumptions in these debates, in particular assumptions about authorship and language and—our focus—the use of the appearance/reality distinction. We show that there are alternative views of what goes on with ChatGPT that do not rely on this distinction. For this purpose, we deploy the two phased approach of deconstruction and (...) relate our finds to questions regarding authorship and language in the humanities. We also identify and respond to two common counter-objections in order to show the ethical appeal and practical use of our proposal. (shrink)

We propose four policies of ethical design of human-grade Artificial Intelligence. Two of our policies are precautionary. Given substantial uncertainty both about ethical theory and about the conditions under which AI would have conscious experiences, we should be cautious in our handling of cases where different moral theories or different theories of consciousness would produce very different ethical recommendations. Two of our policies concern respect and freedom. If we design AI that deserves moral consideration equivalent to that of human (...) beings, that AI should be designed with self-respect and with the freedom to explore values other than those we might impose. We are especially concerned about the temptation to create human-grade AI pre-installed with the desire to cheerfully sacrifice itself for its creators’ benefit. (shrink)

Autonomy has been central to moral and political philosophy for millenia, and has been positioned as a critical aspect of both justice and wellbeing. Research in psychology supports this position, providing empirical evidence that autonomy is critical to motivation, personal growth and psychological wellness. Responsible AI will require an understanding of, and ability to effectively design for, human autonomy (rather than just machine autonomy) if it is to genuinely benefit humanity. Yet the effects on human autonomy of digital experiences are (...) neither straightforward nor consistent, and are complicated by commercial interests and tensions around compulsive overuse. This multi-layered reality requires an analysis that is itself multidimensional and that takes into account human experience at various levels of resolution. We borrow from HCI and psychological research to apply a model (“METUX”) that identifies six distinct spheres of technology experience. We demonstrate the value of the model for understanding human autonomy in a technology ethics context at multiple levels by applying it to the real-world case study of an AI-enhanced video recommender system. In the process we argue for the following three claims: 1) There are autonomy-related consequences to algorithms representing the interests of third parties, and they are not impartial and rational extensions of the self, as is often perceived; 2) Designing for autonomy is an ethical imperative critical to the future design of responsible AI; and 3) Autonomy-support must be analysed from at least six spheres of experience in order to approriately capture contradictory and downstream effects. (shrink)

Critics currently argue that applied ethics approaches to artificial intelligence (AI) are too principles-oriented and entail a theory–practice gap. Several applied ethical approaches try to prevent such a gap by conceptually translating ethical theory into practice. In this article, we explore how the currently most prominent approaches of AI ethics translate ethics into practice. Therefore, we examine three approaches to applied AI ethics: the embedded ethics approach, the ethically aligned approach, and the Value Sensitive Design (VSD) approach. We (...) analyze each of these three approaches by asking how they understand and conceptualize theory and practice. We outline the conceptual strengths as well as their shortcomings: an embedded ethics approach is context-oriented but risks being biased by it; ethically aligned approaches are principles-oriented but lack justification theories to deal with trade-offs between competing principles; and the interdisciplinary Value Sensitive Design approach is based on stakeholder values but needs linkage to political, legal, or social governance aspects. Against this background, we develop a meta-framework for applied AI ethics conceptions with three dimensions. Based on critical theory, we suggest these dimensions as starting points to critically reflect on the conceptualization of theory and practice. We claim, first, that the inclusion of the dimension of affects and emotions in the ethical decision-making process stimulates reflections on vulnerabilities, experiences of disregard, and marginalization already within the AI development process. Second, we derive from our analysis that considering the dimension of justifying normative background theories provides both standards and criteria as well as guidance for prioritizing or evaluating competing principles in cases of conflict. Third, we argue that reflecting the governance dimension in ethical decision-making is an important factor to reveal power structures as well as to realize ethical AI and its application because this dimension seeks to combine social, legal, technical, and political concerns. This meta-framework can thus serve as a reflective tool for understanding, mapping, and assessing the theory–practice conceptualizations within AI ethics approaches to address and overcome their blind spots. (shrink)

I aim to illustrate how the recommender systems of digital platforms create a particularly problematic kind of vulnerability in their users. Specifically, through theories of scaffolded cognition and scaffolded affectivity, I argue that a digital platform’s recommender system is a cognitive and affective artifact that fulfills different functions for the platform’s users and its designers. While it acts as a content provider and facilitator of cognitive, affective and decision-making processes for users, it also provides a continuous and detailed (...) amount of information to platform designers regarding users’ cognitive and affective processes. This dynamic, I argue, engenders a kind of vulnerability in platform users, structuring a power imbalance between designers and users. This occurs because the recommender system can not only gather data on users’ cognitive and affective processes, but also affects them in an unprecedentedly economic and capillary manner. By examining one instance of ethically problematic practice from Facebook, I specifically argue that rather than being a tool for manipulating or exploiting people, digital platforms, especially by their underlying recommender systems, can single out and tamper with specific cognitive and affective processes as a tool specifically designed for mind invasion. I conclude by reflecting how the understanding of such AI systems as tools for mind invasion highlights some merits and shortcomings of the AI Act with regards to the protection of vulnerable people. (shrink)

AI for social good is a thriving research topic and a frequently declared goal of AI strategies and regulation. This article investigates the requirements necessary in order for AI to actually serve a public interest, and hence be socially good. The authors propose shifting the focus of the discourse towards democratic governance processes when developing and deploying AI systems. The article draws from the rich history of public interest theory in political philosophy and law, and develops a framework for (...) ‘public interest AI’. The framework consists of (1) public justification for the AI system, (2) an emphasis on equality, (3) deliberation/ co-design process, (4) technical safeguards, and (5) openness to validation. This framework is then applied to two case studies, namely SyRI, the Dutch welfare fraud detection project, and UNICEF’s Project Connect, that maps schools worldwide. Through the analysis of these cases, the authors conclude that public interest is a helpful and practical guide for the development and governance of AI for the people. (shrink)

The societal and ethical implications of artificial intelligence (AI) have sparked discussions among academics, policymakers and the public around the world. What has gone unnoticed so far are the likewise vibrant discussions in China. We analyzed a large sample of discussions about AI ethics on two Chinese social media platforms. Findings suggest that participants were diverse, and included scholars, IT industry actors, journalists, and members of the general public. They addressed a broad range of concerns associated with the application of (...) AI in various fields. Some even gave recommendations on how to tackle these issues. We argue that these discussions are a valuable source for understanding the future trajectory of AI development in China as well as implications for global dialogue on AI governance. (shrink)

Nel 1956, in piena guerra fredda, una conferenza di scienziati al Dartmouth College negli Stati Uniti annunciò il lancio di un audace progetto scientifico, l’Intelligenza Artificiale (I.A.). Dopo l’iniziale fallimento degli sforzi della “Hard AI” di produrre un’intelligenza simile a quella umana, alla fine del XX secolo è emerso il movimento della “Soft AI”. Invece di essere orientato a imitare il comportamento umano in relazione a compiti specifici, ha preferito cercare modi alternativi di eseguire i compiti basati sulle (...) particolari funzioni e strutture della macchina. Il fattore decisivo è stata la combinazione della tecnologia di apprendimento automatico con le comunicazioni digitali online, l’incontro tra l’IA e Internet. Le applicazioni dell’IA “soft” sono quelle che vediamo nel mondo dei dispositivi automatici “intelligenti” che informano la nostra vita sociale digitale. Nel seguente articolo mi concentrerò su una particolare applicazione dell’IA per le discussioni online con utenti umani, ChatGPT, per mostrare le implicazioni della tecnologia digitale online nelle modalità storico-sociali dell’esistenza umana. ChatGPT è un’applicazione che combina l’IA con Internet, le possibilità della corrispondenza tra macchine e le potenzialità della tele-comunicazione umana. Il mio approccio si concentra sulle implicazioni fenomenologiche e storico-sociali della proliferazione della tecnologia AI per quanto riguarda la coscienza e la socialità umana. -/- Abstract (english) -/- In 1956, amidst the Cold War, a conference of scientists at Dartmouth College in the United States announced the launch of a bold, scientific project, Artificial Intelligence (A.I.). After the initial failure of “Hard AI” efforts to produce a human-like intelligence, the “Soft AI” movement emerged in the late 20th century. Instead of being oriented towards imitating human behaviour concerning specific tasks, it preferred to seek alternative ways of performing tasks based on the particular functions and structures of the machine. The decisive factor was the combination of Machine Learning technology with Digital Online Communications, the meeting between AI and the Internet. The applications of “Soft” AI are those we see in the world of “smart”, “intelligent” automatic devices that inform our digital social life. In the following article, I will focus on a particular application of AI for online discussions with human users, ChatGPT, in order to show the implications of online digital technology in the social-historical modalities of human existence. ChatGPT is an application that combines AI with the Internet, the possibilities of Machine correspondence with the potentialities of human telecommunications. My approach focuses on the phenomenological and social-historical implications of the proliferation of AI technology as regards human consciousness and sociality. (shrink)

Artificially intelligent systems will be used to make increasingly important decisions about us. Many of these decisions will have to be made without universal agreement about the relevant moral facts. For other kinds of disagreement, it is at least usually obvious what kind of solution is called for. What makes moral disagreement especially challenging is that there are three different ways of handling it. _Moral solutions_ apply a moral theory or related principles and largely ignore the details of the (...) disagreement. _Compromise solutions_ apply a method of finding a compromise and taking information about the disagreement as input. _Epistemic solutions_ apply an evidential rule that treats the details of the disagreement as evidence of moral truth. Proposals for all three kinds of solutions can be found in the AI ethics and value alignment literature, but little has been said to justify choosing one over the other. I argue that the choice is best framed in terms of _moral risk_. (shrink)

The justification and rationality of this paper is to present some fundamental principles, theories, and concepts that we believe moulds the nucleus of a good artificial intelligence (AI) society. The morally accepted significance and utilitarian concerns that stems from the inception and realisation of an AI’s structural foundation are displayed in this study. This paper scrutinises the structural foundation, fundamentals, and cardinal righteous remonstrations, as well as the gaps in mechanisms towards novel prospects and perils in determining resilient fundamentals, accountability, (...) and AI’s convoluted and responsible implications. We outline a number of salient and practical benefits, in which to place moral norms within the mise en scène of AI, to delineate the rudimentary ethical dilemmas and decorous directions within the realms of AI. (shrink)