This article explores the challenges surrounding generative artificial intelligence (GenAI) in public administrations and its impact on human‒machine interactions within the public sector. First, it aims to deconstruct the reasons for distrust in GenAI in public administrations. The risks currently linked to GenAI in the public sector are often similar to those of conventional AI. However, while some risks remain pertinent, others are less so because GenAI has limited explainability, which, in return, limits its uses in public administrations. Confidentiality, marking (...) of GenAI outputs and errors are specific matters for which responses should be technical as well as cultural, as they are pushing the boundaries of our instrumental conceptions of machines. Second, this article proposes some paradigm shifts in the perspective of using GenAI in public administrations due to the radical change caused by its language-based nature. GenAI represents a profound break from the “numerical” nature of AI systems implemented in public administrations to date. The transformative impact of GenAI on the intellectual production of the state raises fears of the replacement, or rather enslavement, of civil servants to machines. The article argues for the development of critical thinking as a specific skill for civil servants who have become highly specialized and will have to think with a machine that is eclectic by nature. It anticipates a transformation in the political nature of public administrations, which should lead to more considerations for the strategic stake related to training corpus and for our conceptualization of the neutrality of AI. (shrink)

Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized (...) as an ability to perform certain tasks. We extend this notion of scientific understanding by considering a set of questions that gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances. We suggest building a Scientific Understanding Benchmark (SUB), formed by a set of these tests, allowing for the evaluation and comparison of scientific understanding. Benchmarking plays a crucial role in establishing trust, ensuring quality control, and providing a basis for performance evaluation. By aligning machine and human scientific understanding we can improve their utility, ultimately advancing scientific understanding and helping to discover new insights within machines. (shrink)

AI-based technologies are increasingly pervasive in a number of contexts. Our affective and emotional life makes no exception. In this article, we analyze one way in which AI-based technologies can affect them. In particular, our investigation will focus on affective artificial agents, namely AI-powered software or robotic agents designed to interact with us in affectively salient ways. We build upon the existing literature on affective artifacts with the aim of providing an original analysis of affective artificial agents and their distinctive (...) features. We argue that, unlike comparatively low-tech affective artifacts, affective artificial agents display a specific form of agency, which prevents them from being perceived by their users as extensions of their selves. In addition to this, we claim that their functioning crucially depends on the simulation of human-like emotion-driven behavior and requires a distinctive form of transparency—we call it emotional transparency—that might give rise to ethical and normative tensions. (shrink)

Conversational Artificial Intelligence (CAI) systems (also known as AI “chatbots”) are among the most promising examples of the use of technology in mental health care. With already millions of users worldwide, CAI is likely to change the landscape of psychological help. Most researchers agree that existing CAIs are not “digital therapists” and using them is not a substitute for psychotherapy delivered by a human. But if they are not therapists, what are they, and what role can they play in mental (...) health care? To answer these questions, we appeal to two well-established and widely discussed concepts: cognitive and affective artifacts. Cognitive artifacts are artificial devices contributing functionally to the performance of a cognitive task. Affective artifacts are objects which have the capacity to alter subjects’ affective state. We argue that therapeutic CAIs are a kind of cognitive-affective artifacts which contribute to positive therapeutic change by (i) simulating a (quasi-)therapeutic interaction, (ii) supporting the performance of cognitive tasks, and (iii) altering the affective condition of their users. This sheds new light on why virtually all existing mental health CAIs implement principles and techniques of Cognitive Behavioral Therapy — a therapeutic orientation according to which affective change and, ultimately, positive therapeutic change is mediated by cognitive change. Simultaneously, it allows us to conceptualize better the potential and limitations of applying these technologies in therapy. (shrink)

In this paper, we explore a new perspective on digital humanism, emphasizing the centrality of multi-stakeholder dialogues and a bottom-up approach to surfacing stakeholder values. This approach starkly contrasts with existing frameworks, such as the Vienna Manifesto's top-down digital humanism, which hinges on pre-established first principles. Our approach provides a more flexible, inclusive framework that captures a broader spectrum of ethical considerations, particularly those pertinent to the digital realm. We apply our model to two case studies, comparing the insights generated (...) with those derived from a utilitarian perspective and the Vienna Manifesto's approach. The findings underscore the enhanced effectiveness of our approach in revealing additional, often overlooked stakeholder values, not typically encapsulated by traditional top-down methodologies. Furthermore, this paper positions our digital humanism approach as a powerful tool for framing ethics-by-design, by promoting a narrative that empowers and centralizes stakeholders. As a result, it paves the way for more nuanced, comprehensive ethical considerations in the design and implementation of digital technologies, thereby enriching the existing literature on digital ethics and setting a promising agenda for future research. (shrink)

Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may lead (...) to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors, cases of epistemic injustice, and unwarranted trust. To address these issues, we propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users. This addition aims to bridge the gap between LLM capabilities and user perceptions, promoting the ethically responsible development and use of LLM-based technology. (shrink)

This article sets in with the question whether current or foreseeable transformer-based large language models (LLMs), such as the ones powering OpenAI’s ChatGPT, could be language users in a way comparable to humans. It answers the question negatively, presenting the following argument. Apart from niche uses, to use language means to act. But LLMs are unable to act because they lack intentions. This, in turn, is because they are the wrong kind of being: agents with intentions need to be autonomous (...) organisms while LLMs are heteronomous mechanisms. To conclude, the article argues, based on structural aspects of transformer-based LLMs, that these LLMs have taken a first step away from mechanistic artificiality to autonomous self-constitution, which means that these models are (slowly) moving into a direction that someday might result in non-human, but equally non-artificial agents, thus subverting the time-honored Kantian distinction between organism and mechanism. (shrink)

Generative AI enables automated, effective manipulation at scale. Despite the growing general ethical discussion around generative AI, the specific manipulation risks remain inadequately investigated. This article outlines essential inquiries encompassing conceptual, empirical, and design dimensions of manipulation, pivotal for comprehending and curbing manipulation risks. By highlighting these questions, the article underscores the necessity of an appropriate conceptualisation of manipulation to ensure the responsible development of Generative AI technologies.

The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact can (...) give testimony. In section 3, I identify three assumptions underlying the view that rejects conversational AIs – AI-based technologies that converse, as testifiers: conversational AIs (1) lack intentions, (2) cannot be normatively assessed, and (3) cannot constitute an object in trust relations, while humans can. In section 4, I propose the concept ‘AI-testimony’ for analyzing outputs of conversational AIs, suggesting three conditions for technologies to deliver AI-testimony: (1) content is propositional, (2) generated and delivered with no other human directly involved, (3) the output is perceived as phenomenologically similar to that of a human. I conclude that this concept overcomes the limitations of the anthropocentric concept of testimony, opening future directions of research without associating conversational AIs with human-like agency. (shrink)

Can nonhuman animals and artificial intelligence (AI) entities be attributed moral agency? The general assumption in the philosophical literature is that moral agency applies exclusively to humans since they alone possess free will or capacities required for deliberate reflection. Consequently, only humans have been taken to be eligible for ascriptions of moral responsibility in terms of, for instance, blame or praise, moral criticism, or attributions of vice and virtue. Animals and machines may cause harm, but they cannot be appropriately ascribed (...) moral responsibility for their behavior. This thesis challenges the conventional paradigm by proposing an alternative approach where moral agency is conceived as the competence to participate in moral responsibility practices. By shifting focus from intra-individual to contextual and socially situated features, this practice-focused approach appears to make the attribution of moral agency to nonhuman animals and AI entities more plausible than commonly assumed. Moreover, considering the current and potential future prevalence of nonhuman animals and AI entities in everyday settings and social contexts, a potential extension of moral agency to such entities could very well transform our social, moral, and legal practices. Hence, this thesis proposes that the attribution or withholding of moral agency to different entities should be carefully evaluated, considering the potential normative implications. (shrink)

This paper takes a step back from considering expertise as a social phenomenon. One should investigate how people become knowers before assigning expertise to a person’s actions. Using a temporal-sensitive systemic ethnography, a case study shows how undergraduate students form a social system out of necessity as they fabricate knowledge around an empty wording like ‘conscious living’. Tracing the engagement with students and tutor to recursive moments of coaction, I argue that, through the subtleties of bodily movements, people incorporate the (...) actions of others as they become knowers. Knowing for a person solidifies as they imbue concepts, terms, facts, etc. with their own understanding. While coaction refers to the interlocking of actions in a specific moment, the fabrication of knowledge resides in temporally distributed moments of coaction where students deliberately incorporate and build on past occurrences in a present moment. In so doing, people cannot be separated from their systemic embedding. Linking coaction with systemic cognition, people fabricate knowledge within wider systemic structures. Within these boundaries, knowers come to fabricate knowledge for themselves and a wider system. Thus, knowing must be seen as an active, embodied, dialogical and multiscalar activity. (shrink)

Is ChatGPT an author? Given its capacity to generate something that reads like human-written text in response to prompts, it might seem natural to ascribe authorship to ChatGPT. However, we argue that ChatGPT is not an author. ChatGPT fails to meet the criteria of authorship because it lacks the ability to perform illocutionary speech acts such as promising or asserting, lacks the fitting mental states like knowledge, belief, or intention, and cannot take responsibility for the texts it produces. Three perspectives (...) are compared: liberalism (which ascribes authorship to ChatGPT), conservatism (which denies ChatGPT's authorship for normative and metaphysical reasons), and moderatism (which treats ChatGPT as if it possesses authorship without committing to the existence of mental states like knowledge, belief, or intention). We conclude that conservatism provides a more nuanced understanding of authorship in AI than liberalism and moderatism, without denying the significant potential, influence, or utility of AI technologies such as ChatGPT. (shrink)

This comprehensive analysis dives deep into the intricate interplay between artificial intelligence (AI) and human agency, examining the remarkable capabilities and inherent limitations of large language models (LLMs) such as GPT-3 and ChatGPT. The paper traces the complex trajectory of AI's evolution, highlighting its operation based on statistical pattern recognition, devoid of self-consciousness or innate comprehension. As AI permeates multiple spheres of human life, it raises substantial ethical, legal, and societal concerns that demand immediate attention and deliberation. The metaphorical illustration (...) of humans as “shepherds” of AI, coupled with a thought-provoking examination of “agency without intelligence,” engenders deep reflections on the future of human-AI interplay and the associated philosophical, ethical, and practical ramifications. The paper further elucidates critical challenges associated with AI, such as ethical dilemmas, “agency without intelligence,” the role of human oversight, and the environmental impact of AI technologies. In the pursuit of solutions, the paper maps prospective directions, emphasizing the need for robust AI policies, improved AI literacy, sustainable AI practices, and a continued dialogue on the multifaceted issues surrounding AI. Through a holistic exploration of AI, this paper underlines the necessity for a multidisciplinary, collaborative approach to chart an ethical, beneficial, and sustainable AI future, enabling society to harness the potential of AI responsibly while mitigating the associated risks. (shrink)

Michael A PetersBeijing Normal UniversityChatGPT is an AI chatbot released by OpenAI on November 30, 2022 and a ‘stable release’ on February 13, 2023. It belongs to OpenAI’s GPT-3 family (generativ...

Large Language Models (LLMs) “assign probabilities to sequences of text. When given some initial text, they use these probabilities to generate new text. Large language models are language models u...

This paper discusses two opposing views about the relation between artificial intelligence (AI) and human intelligence: on the one hand, a worry that heavy reliance on AI technologies might make people less intelligent and, on the other, a hope that AI technologies might serve as a form of cognitive enhancement. The worry relates to the notion that if we hand over too many intelligence-requiring tasks to AI technologies, we might end up with fewer opportunities to train our own intelligence. Concerning (...) AI as a potential form of cognitive enhancement, the paper explores two possibilities: (1) AI as extending—and thereby enhancing—people’s minds, and (2) AI as enabling people to behave in artificially intelligent ways. That is, using AI technologies might enable people to behave as if they have been cognitively enhanced. The paper considers such enhancements both on the level of individuals and on the level of groups. (shrink)

Conceptual Engineering (CE) is thought to be generally aimed at ameliorating deficient concepts. In this paper, we challenge this assumption: we argue that CE is frequently undertaken with the orthogonal aim of _conceptual adaptation_. We develop this thesis with reference to the interplay between technology and concepts. Emerging technologies can exert significant pressure on conceptual systems and spark ‘conceptual disruption’. For example, advances in Artificial Intelligence raise the question of whether AIs are agents or mere objects, which can be construed (...) as a CE question regarding the concepts AGENT and OBJECT. We distinguish between three types of conceptual disruption (conceptual gaps, conceptual overlaps, and conceptual misalignments) and argue that when CE occurs to address these disruptions, its primary aim is not to improve concepts, but to retain their functional quality, or to prevent them from degrading. This is the characteristic aim of CE when undertaken in philosophy of technology: to _preserve_ the functional role of a concept or conceptual scheme, rather than _improving_ how a concept fulfills its respective function. (shrink)

Turing made strong statements about the future of machines in society. This article asks how they can be interpreted to advance our understanding of Turing’s philosophy. His irony has been largely caricatured or minimized by historians, philosophers, scientists, and others. Turing is often portrayed as an irresponsible scientist, or associated with childlike manners and polite humor. While these representations of Turing have been widely disseminated, another image suggested by one of his contemporaries, that of a nonconformist, utopian, and radically progressive (...) thinker reminiscent of the English Romantic poet Percy B. Shelley, has remained largely underexplored. Following this image, I will reconstruct the argument underlying what Turing called (but denied being guilty of) his “Promethean irreverence” (1947–1951) as a utopian satire directed against chauvinists of all kinds, especially intellectuals who might sacrifice independent thought to maintain their power. These, Turing hoped, would eventually be rivaled and surpassed by intelligent machines and transformed into ordinary people, as work once considered “intellectual” would be transformed into non-intellectual, “mechanical” work. I study Turing’s irony in its historical context and follow the internal logic of his arguments to their limit. I suggest that Turing genuinely believed that the possibilities of the machines he envisioned were not utopian dreams, and yet he conceived them from a utopian frame of mind, aspiring to a different society. His ever-learning child machines, whose intelligence would grow out of their own individual experiences, would help distribute power. (shrink)

Large language models (LLMs) have now entered the realm of medical ethics. In a recent study, Balaset alexamined the performance of GPT-4, a commercially available LLM, assessing its performance in generating responses to diverse medical ethics cases. Their findings reveal that GPT-4 demonstrates an ability to identify and articulate complex medical ethical issues, although its proficiency in encoding the depth of real-world ethical dilemmas remains an avenue for improvement. Investigating the integration of LLMs into medical ethics decision-making appears to be (...) an interesting avenue of research. However, despite the promising trajectory of LLM technology in medicine, it is crucial to exercise caution and refrain from attributing their expertise to medical ethics. Our thesis follows an examination of the nature of expertise and the epistemic limitations that affect LLM technology. As a result, we propose two more fitting applications of LLMs in medical ethics: first, as tools for mining electronic health records or scientific literature, thereby supplementing evidence for resolving medical ethics cases, and second, as educational platforms to foster ethical reflection and critical thinking skills among students and residents. The integration of LLMs in medical ethics, while promising, requires careful consideration of their epistemic limitations. Consequently, a well-considered definition of their role in ethically sensitive decision-making is crucial. (shrink)

For decades, healthcare has relied on data-driven algorithms to guide clinical practice. Recent advances in machine learning have opened up new possibilities in the field, enabling detailed analyse...

Cheating is a growing academic and ethical concern in higher education. This article examines the rise of artificial intelligence (AI) generative chatbots for use in education and provides a review of research literature and relevant scholarship concerning the cheating-related issues involved and their implications for pedagogy. The technological “arms race” that involves cheating-detection system developers versus technology savvy students is attracting increased attention to cheating. AI has added new dimensions to academic cheating challenges as students (as well as faculty and (...) staff) can easily access powerful systems for generating content that can be presented in assignments, exams, or published papers as their own. AI methodology is also providing some emerging anti-cheating approaches, including facial recognition and water- marking. This article provides an overview of human/AI collaboration approaches and frames some educational misuses of such AI generative systems as ChatGPT and Bard as forms of “misattributed co-authorship.” As with other kinds of collaborations, the work that students produce with AI assistance can be presented in transparent and straightforward modes or (unfortunately) in opaquer and ethically-problematic ways. However, rather than just for catching or entrapping students, the emerging varieties of technological cheating-detection strategies can be used to assist students in learning how to document and attribute properly their AI-empowered as well as human-human collaborations. Construing misuses of AI generative systems as misattributed co-authorship can recognize the growing capabilities of these tools and how stressing responsible and mindful usage by students can help prepare them for a highly collaborative, AI-saturated future. (shrink)