On the basis of a close reading of three authors , I try to elucidate what the growing presence of digital technologies in our lives implies for the sphere of schooling and education. Developing a technocentric perspective, I discuss whether what is happening today concerns just the newest form of humankind's fundamental dependency on a technological milieu or that it concerns a fundamental shift. From Flusser, I take the idea that the practice of writing shapes human subjectivity, as well as (...) our very sense of history and progress, and that with the advent of digital technologies the possibility of a posthistorical era is granted. I confront this idea with Stiegler's analysis of technological tools and practices as strongly materialized memories, which amounts to a plea for securing the link with the particular history behind the technologies we use. Here, education should play a conservative role and take responsibility for using technologies in a correct way. I argue that Stiegler is not wholly consistent on this point and, moreover, that his view precludes the possibility to rethink the very meaning of education under present conditions. This possibility is opened if we turn to a philosopher which is ruthlessly criticized by Stiegler for being a technophobe: Agamben. I argue, however, that a more detailed reading of Agamben—n combination with Flusser—might show a completely different and far more positive appreciation for digital technology and that this view offers an opening for rethinking what education is all about. (shrink)

This article is a commentary on ChatGPT and LLMs (Large Language Models) in general. It argues that this technology has matured to the point where calling systems such as ChatGPT “stochastic parrots” is no longer warranted. But it also argues that these systems continue to have serious limitations when it comes to reasoning. These limitations are much more severe than commonly thought. A large array of examples are given to support these claims.

Who is responsible when an AI machine causes something to go wrong? Or is there a gap in the ascription of responsibility? Answers range from claiming there is a unique responsibility gap, several different responsibility gaps, or no gap at all. In a nutshell, the problem is as follows: on the one hand, it seems fitting to hold someone responsible for a wrong caused by an AI machine; on the other hand, there seems to be no fitting bearer of responsibility (...) for this wrong. In this article, we focus on a particular (aspect of the) AI responsibility gap: it seems fitting that someone should bear the legal consequences in scenarios involving AI machines with design defects; however, there seems to be no such fitting bearer. We approach this problem from the legal perspective, and suggest vicarious liability of AI manufacturers as a solution to this problem. Our proposal comes in two variants: the first one has a narrower range of application, but can be easily integrated in current legal frameworks; the second one requires a revision of current legal frameworks, but has a wider range of application. The latter variant employs a broadened account of vicarious liability. We emphasise strengths of the two variants and finally highlight how vicarious liability offers important insights for addressing a moral AI responsibility gap. (shrink)

In this paper, in the first place, I aim to enquire into Bernard Stiegler’s critical appropriation of his mentor Jacques Derrida’s notion of différance, emphasizing how Stiegler’s philosophy of technology stems from an original interpretation of the main tenets of deconstruction. From this perspective, I will investigate Stiegler’s definition of technology as tertiary retention, i.e., exosomatized, artificial memory interrelating with biological memory, testing its hermeneutic strengths as well as possible weaknesses. In the second place, I aim to contrast Stiegler’s understanding (...) of technology with the concept of multistability brought forward by postphenomenological philosophies of technology such as those elaborated by Don Ihde and Peter-Paul Verbeek. This investigation will enable me to submit that Stiegler’s approach represents a peculiar and innovative way to conceive of technology. On the one hand, indeed, it does not seem to fall prey to the criticisms raised by postphenomenology against traditional philosophies of technology such as Martin Heidegger’s or Jacques Ellul’s, deemed to be overly deterministic, abstract and pessimistic in their understanding of technology. On the other, it retains important methodological precautions from deconstruction, thereby pointing at some possible blind spots of postphenomenology, especially concerning the vexed question of the empirical-transcendental divide, which Stiegler aims to develop beyond both Derrida’s and postphenomenology’s stances. (shrink)

COVID19 pandemic has clarified that public health policies are central for the future of human societies from several perspectives. As a matter of fact, they are based on certain premises that are practical-political (e.g., ensuring the health of citizens), moral (e.g., health is a value), or epistemological (e.g., certain ideas concerning expertise and shared knowledge). Indeed, effective policies require first and foremost not only to be based on reliable data and models (i.e., so-called evidence-based policy) but also to ensure that (...) these policies are democratically accepted, shared (e.g., considering both cognitive and social dimensions), and hopefully formed after a deliberative process involving experts from various fields alongside citizens. (shrink)

As AI algorithms advance and produce surprising outputs, the question of imagination arises. Can we classify their output as imaginative? And what is their effect on human imagination? Apparently, algorithms follow Kant’s explanations on human imagination, thereby pushing us to update our understanding of imagination by taking into account the co-shaping between humans and their technologies. Such a new understating is offered in this article based on the theories of Don Ihde and Bernard Stiegler. With Ihde, imagination is conceived as (...) tightly linked to perception. Imagination evolves from the modernist task of seeking new points-of-view to a new mode in which technologies present imaginative layers of information on top of reality. With Stiegler, imagination is regarded as tightly linked to memory. He demonstrates how imagination complements memory, serves as a condition to memory/technology and at the same time how imagination itself is conditioned by memory/technology. In this article I develop a new synthesis: imagination as composed of perception and memory. This is the foundation of my layered model of digital imagination. In this model, the task of AI algorithms is the filling in of the layers with data. By producing endless possibilities, these technologies “automate” the Kantian “free play” of imagination, allowing us to examine more options and focus on the best of them. The model reserves the production of meaning to humans. Our role as humans is to generate meaning to the link between layers and establish new layers. (shrink)

In the past few years, scholars have been questioning whether the current approach in data ethics based on the higher level case studies and general principles is effective. In particular, some have been complaining that such an approach to ethics is difficult to be applied and to be taught in the context of data science. In response to these concerns, there have been discussions about how ethics should be “embedded” in the practice of data science, in the sense of showing (...) how ethical issues emerge in small technical choices made by data scientists in their day-to-day activities, and how such an approach can be used to teach data ethics. However, a precise description of how such proposals have to be theoretically conceived and could be operationalized has been lacking. In this article, we propose a full-fledged characterization of ‘embedding’ ethics, and how this can be applied especially to the problem of teaching data science ethics. Using the emerging model of ‘microethics’, we propose a way of teaching daily responsibility in digital activities that is connected to the higher level ethical challenges discussed in digital/data ethics. We ground this microethical approach into a virtue theory framework, by stressing that the goal of a microethics is to foster the cultivation of moral virtues. After delineating this approach of embedding ethics in theoretical detail, this article discusses a concrete example of how such a ‘micro-virtue ethics’ approach could be practically taught to data science students. (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)

As machines have become increasingly smart and have entangled human thinking with artificial intelligences, it seems no longer possible to distinguish among levels of decision-making that occur in the newly formed space between critical reasoning, logical inference and sheer calculation. Since the 1980s, computational systems of information processing have evolved to include not only deductive methods of decision, whereby results are already implicated in their premises, but have crucially shifted towards an adaptive practice of learning from data, an inductive method (...) of retrieving information from the environment and establishing general premises. This shift in logical methods of decision-making does not simply concern technical apparatuses, but is a symptom of a transformation in logical thinking activated with and through machines. This article discusses the pioneering work of Katherine Hayles, whose study of the cybernetic and computational infrastructures of our culture particularly clarifies this epistemological transformation of thinking in relation to machines. (shrink)

As machines take over more tasks previously done by humans, artistic creation is also considered as a candidate to be automated. But, can machines create art? This paper offers a conceptual framework for a philosophical discussion of this question regarding the status of machine art and machine creativity. It breaks the main question down in three sub-questions, and then analyses each question in order to arrive at more precise problems with regard to machine art and machine creativity: What is art (...) creation? What do we mean by art? And, what do we mean by machines create art? This then provides criteria we can use to discuss the main question in relation to particular cases. In the course of the analysis, the paper engages with theory in aesthetics, refers to literature on computational creativity, and contributes to the philosophy of technology and philosophical anthropology by reflecting on the role of technology in art creation. It is shown that the distinctions between process versus outcome criteria and subjective versus objective criteria of creativity are unstable. It is also argued that we should consider non-human forms of creativity, and not only cases where either humans or machines create art but also collaborations between humans and machines, which makes us reflect on human-technology relations. Finally, the paper questions the very approach that seeks criteria and suggests that the artistic status of machines may be shown and revealed in the human/non-human encounter before any theorizing or agreement takes place; an experience which then is presupposed when we theorize. This hints at a more general model of what happens in artistic perception and engagement as a hybrid human-technological and emergent or even poetic process, a model which leaves more room for letting ourselves be surprised by creativity—human and perhaps non-human. (shrink)

This book is the first monograph that takes a comprehensive approach to Jacques Derrida as a philosopher of technology. It refines and complements his mainstream image as a philosopher of language and deconstructionist of classical literary and philosophical texts. This volume outlines the key features of Derrida’s alternative philosophy of technology, a philosophy which Sjöstrand argues, avoids the problems associated with, on the one hand, a Heideggerian orientation, which completely separates thinking and technology and, on the other, an empirically oriented (...) ”post-phenomenology” that can be said to be hegemonic within the field today. Based on a sustained interpretation of Derrida, and a robust, coherent philosophy of technology, a phenomenology of technology is developed that, in a radical way, extends the concept of technology to cover the entire field of phenomenology. This places the technological not in opposition to humanity, but rather always already in close proximity to man and, consequently, to life, ethics, politics, democracy and religion. Strikingly, this important aspect of Derrida’s thinking is only rarely analyzed or discussed by his many exegetes. This text appeals to graduates and researchers working on Derrida, phenomenology, and the philosophy of technology. (shrink)

In the present article we exploit the logical notions of correctness and completeness to provide an analysis of some fundamental problems that can be encountered by a software developer when transforming norms for traffic circulation into programming instructions. Relying on this analysis, we then introduce a question and answer procedure that can be helpful, in case of an accident, to clarify which components of an existing framework should be revised and to what extent software developers can be held responsible.