It has been suggested that to overcome the challenges facing the UK’s National Health Service (NHS) of an ageing population and reduced available funding, the NHS should be transformed into a more informationally mature and heterogeneous organisation, reliant on data-based and algorithmically-driven interactions between human, artificial, and hybrid (semi-artificial) agents. This transformation process would offer significant benefit to patients, clinicians, and the overall system, but it would also rely on a fundamental transformation of the healthcare system in a way that (...) poses significant governance challenges. In this article, we argue that a fruitful way to overcome these challenges is by adopting a pro-ethical approach to design that analyses the system as a whole, keeps society-in-the-loop throughout the process, and distributes responsibility evenly across all nodes in the system. (shrink)

Applications of artificial intelligence (AI) for cybersecurity tasks are attracting greater attention from the private and the public sectors. Estimates indicate that the market for AI in cybersecurity will grow from US$1 billion in 2016 to a US$34.8 billion net worth by 2025. The latest national cybersecurity and defence strategies of several governments explicitly mention AI capabilities. At the same time, initiatives to define new standards and certification procedures to elicit users’ trust in AI are emerging on a global scale. (...) However, trust in AI (both machine learning and neural networks) to deliver cybersecurity tasks is a double edged sword: it can improve substantially cybersecurity practices, but can also facilitate new forms of attacks to the AI applications themselves, which may pose severe security threats. We argue that trust in AI for cybersecurity is unwarranted and that, to reduce security risks, some form of control to ensure the deployment of ‘reliable AI’ for cybersecurity is necessary. To this end, we offer three recommendations focusing on the design, development and deployment of AI for cybersecurity. (shrink)

In this commentary, we discuss the nature of reversible and irreversible questions, that is, questions that may enable one to identify the nature of the source of their answers. We then introduce GPT-3, a third-generation, autoregressive language model that uses deep learning to produce human-like texts, and use the previous distinction to analyse it. We expand the analysis to present three tests based on mathematical, semantic, and ethical questions and show that GPT-3 is not designed to pass any of them. (...) This is a reminder that GPT-3 does not do what it is not supposed to do, and that any interpretation of GPT-3 as the beginning of the emergence of a general form of artificial intelligence is merely uninformed science fiction. We conclude by outlining some of the significant consequences of the industrialisation of automatic and cheap production of good, semantic artefacts. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...) methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

In this article, I shall argue that AI’s likely developments and possible challenges are best understood if we interpret AI not as a marriage between some biological-like intelligence and engineered artefacts, but as a divorce between agency and intelligence, that is, the ability to solve problems successfully and the necessity of being intelligent in doing so. I shall then look at five developments: (1) the growing shift from logic to statistics, (2) the progressive adaptation of the environment to AI rather (...) than of AI to the environment, (3) the increasing translation of difficult problems into complex problems, (4) the tension between regulative and constitutive rules underpinning areas of AI application, and (5) the push for synthetic data. (shrink)

Artificial intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently (...) interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)

AI, especially in the case of Deepfakes, has the capacity to undermine our confidence in the original, genuine, authentic nature of what we see and hear. And yet digital technologies, in the form of databases and other detection tools also make it easier to spot forgeries and to establish the authenticity of a work. Using the notion of ectypes, this paper discusses current conceptions of authenticity and reproduction and examines how, in the future, these might be adapted for use in (...) the digital sphere. (shrink)

In this paper, we demonstrate how the language and reasonings that academics, developers, consumers, marketers, and journalists deploy to accept or reject AI as authentic intelligence has far-reaching bearing on how we understand our human intelligence and condition. The discourse on AI is part of what we call the “authenticity negotiation process” through which AI’s “intelligence” is given a particular meaning and value. This has implications for scientific theory, research directions, ethical guidelines, design principles, funding, media attention, and the way (...) people relate to and act upon AI. It also has great impact on humanity’s self-image and the way we negotiate what it means to be human, existentially, culturally, politically, and legally. We use a discourse analysis of academic papers, AI education programs, and online discussions to demonstrate how AI itself, as well as the products, services, and decisions delivered by AI systems are negotiated as authentic or inauthentic intelligence. In this negotiation process, AI stakeholders indirectly define and essentialize what being human(like) means. The main argument we will develop is that this process of indirectly defining and essentializing humans results in an elimination of the space for humans to be indeterminate. By eliminating this space and, hence, denying indeterminacy, the existential condition of the human being is jeopardized. Rather than re-creating humanity in AI, the AI discourse is re-defining what it means to be human and how humanity is valued and should be treated. (shrink)

This paper presents an African relational view of social robots’ moral standing which draws on the philosophy of ubuntu. The introduction places the question of moral standing in historical and cultural contexts. Section 2 demonstrates an ubuntu framework by applying it to the fictional case of a social robot named Klara, taken from Ishiguro’s novel, Klara and the Sun. We argue that an ubuntu ethic assigns moral standing to Klara, based on her relational qualities and pro-social virtues. Section 3 introduces (...) a second fictional case, taken from McKeown’s novel, Machines Like Me, in which a social robot named Adam displays intrinsic qualities, such as sentience, rationality, and deductive moral reasoning, yet lacks close social ties to particular people. We argue that Adam is not a person in the African sense; however, he qualifies as a person according to many standard Western views, such as Kantian and utilitarian ethics. Section 4 further elaborates the African relational view by comparing the moral standing of social robots and humans in a forced choice scenario. Section 5 replies to objections. We conclude that an African relational approach captures important insights about the moral standing of social robots that many Western accounts miss and should be better incorporated into global frameworks for designing and deploying social robots. (shrink)

This study aims to investigate the worldview characteristics of current systems of artificial intelligence generated content (AIGC). Eight representative AIGC systems is selected as research objects and elicited responses and ratings to the viewpoints in Devlin’s CWQ worldview scale through a unified questioning approach. Based on the item-by-item ratings provided by the systems, the worldviews reflected from the AIGC systems were analyzed from three aspects: stability, tendency, and polarity. The research found that AIGC systems demonstrate general stability and relatively consistent (...) tendency but notable inter-system differences in polarization. By analyzing and categorizing differences in these characteristics, this paper outlines several potential types of worldviews held by AIGC systems. (shrink)

Presenting the challenges that face digital age citizens as philosophical, as opposed to technological, this paper explores the underpinning structure of digital civics through an overview of its four foundational pillars and their informing scholarship. Through this framework and the literature that supports it, a set of five key concepts are identified as a useful guide for developing digital civics initiatives, including policy and educational interventions. In closing, this work urges the further exploration of the conceptual underpinnings of digital civics (...) to improve future formulations of digital civics and digital citizenship endeavours. (shrink)

This commentary response presents two concepts that may address legal-political issues arising in the civics pillar of digital civics: the history of data and nomos. It suggests that we can draw from the history of data to supplement our understanding of datafication and formulate responses to surveillance capitalism. It then forwards the utility of the ancient concept of nomos as a means of approaching cultural and jurisdictional challenges arising in the infosphere.

This article contributes to the debate around the abilities of large language models such as GPT-3, dealing with: firstly, evaluating how well GPT does in the Turing Test, secondly the limits of such models, especially their tendency to generate falsehoods, and thirdly the social consequences of the problems these models have with truth-telling. We start by formalising the recently proposed notion of reversible questions, which Floridi & Chiriatti propose allow one to ‘identify the nature of the source of their answers’, (...) as a probabilistic measure based on Item Response Theory from psychometrics. Following a critical assessment of the methodology which led previous scholars to dismiss GPT’s abilities, we argue against claims that GPT-3 completely lacks semantic ability. Using ideas of compression, priming, distributional semantics and semantic webs we offer our own theory of the limits of large language models like GPT-3, and argue that GPT can competently engage in various semantic tasks. The real reason GPT’s answers seem senseless being that truth-telling is not amongst them. We claim that these kinds of models cannot be forced into producing only true continuation, but rather to maximise their objective function they strategize to be plausible instead of truthful. This, we moreover claim, can hijack our intuitive capacity to evaluate the accuracy of its outputs. Finally, we show how this analysis predicts that a widespread adoption of language generators as tools for writing could result in permanent pollution of our informational ecosystem with massive amounts of very plausible but often untrue texts. (shrink)