Philosophy and Technology最新文献

This paper proposes a new normative framework to think about Big Tech reform. Focusing on the case of digital communication, I argue that rethinking the corporate purpose of social media companies is a distinctive entry point to the debate on how to render the powers of tech corporations democratically legitimate. I contend that we need to strive for a reform that redefines the corporate purpose of social media companies. In this view, their purpose should be to create and maintain a free, egalitarian, and democratic public sphere rather than profit seeking. This political reform democratically contains corporate power in two ways: first, the legally enforceable fiduciary duties of corporate boards are reconceptualized in relation to democratic purposes rather than shareholder interests. Second, corporate governance structures should be redesigned to ensure that the abstract purpose is realized through representatives whose incentives align with the existence of a democratic public sphere. My argument complements radical proposals such as platform socialism by drawing a connection between democratizing social media governance and identifying the proper purpose of social media companies.

In April 2020, in the midst of its first pandemic lockdown, the Dutch government announced plans to develop a contact tracing app to help contain the spread of the coronavirus - the Coronamelder. Originally intended to address the problem of the overburdening of manual contract tracers, by the time the app was released six months later, the problem it sought to solve had drastically changed, without the solution undergoing any modification, making it a prime example of technosolutionism. While numerous critics have mobilised the concept of technosolutionism, the questions of how technosolutionism works in practice and which specific harms it can provoke have been understudied. In this paper we advance a thick conception of technosolutionism which, drawing on Evgeny Morozov, distinguishes it from the notion of technological fix, and, drawing on constructivism, emphasizes its constructivist dimension. Using this concept, we closely follow the problem that the Coronamelder aimed to solve and how it shifted over time to fit the Coronamelder solution, rather than the other way around. We argue that, although problems are always constructed, technosolutionist problems are badly constructed, insofar as the careful and cautious deliberation which should accompany problem construction in public policy is absent in the case of technosolutionism. This can lead to three harms: a subversion of democratic decision-making; the presence of powerful new actors in the public policy context - here Big Tech; and the creation of "orphan problems", whereby the initial problems that triggered the need to develop a (techno)solution are left behind. We question whether the most popular form of technology ethics today, which focuses predominantly on the design of technology, is well-equipped to address these technosolutionist harms, insofar as such a focus may preclude critical thinking about whether or not technology should be the solution in the first place.

Novel technological devices, applications, and algorithms can provide us with a vast amount of personal information about ourselves. Given that we have ethical and practical reasons to pursue self-knowledge, should we use technology to increase our self-knowledge? And which ethical issues arise from the pursuit of technologically sourced self-knowledge? In this paper, I explore these questions in relation to bioinformation technologies (health and activity trackers, DTC genetic testing, and DTC neurotechnologies) and algorithmic profiling used for recommender systems, targeted advertising, and technologically supported decision-making. First, I distinguish between impersonal, critical, and relational self-knowledge. Relational self-knowledge is a so far neglected dimension of self-knowledge which is introduced in this paper. Next, I investigate the contribution of these technologies to the three types of self-knowledge and uncover the connected ethical concerns. Technology can provide a lot of impersonal self-knowledge, but we should focus on the quality of the information which tends to be particularly insufficient for marginalized groups. In terms of critical self-knowledge, the nature of technologically sourced personal information typically impedes critical engagement. The value of relational self-knowledge speaks in favour of transparency of information technology, notably for algorithms that are involved in decision-making about individuals. Moreover, bioinformation technologies and digital profiling shape the concepts and norms that define us. We should ensure they not only serve commercial interests but our identity and self-knowledge interests.

Artificially generated content threatens to seriously disrupt the public sphere. Generative AI massively facilitates the production of convincing portrayals of fabricated events. We have already begun to witness the spread of synthetic misinformation, political propaganda, and non-consensual intimate deepfakes. Malicious uses of the new technologies can only be expected to proliferate over time. In the face of this threat, social media platforms must surely act. But how? While it is tempting to think they need new sui generis policies targeting synthetic content, we argue that the challenge posed by generative AI should be met through the enforcement of general platform rules. We demonstrate that the threat posed to individuals and society by AI-generated content is no different in kind from that of ordinary harmful content-a threat which is already well recognised. Generative AI massively increases the problem but, ultimately, it requires the same approach. Therefore, platforms do best to double down on improving and enforcing their existing rules, regardless of whether the content they are dealing with was produced by humans or machines.

In this paper, I explore Derrida's concept of exteriorization in relation to texts generated by machine learning. I first discuss Heidegger's view of machine creation and then present Derrida's criticism of Heidegger. I explain the concept of iterability, which is the central notion on which Derrida's criticism is based. The thesis defended in the paper is that Derrida's account of iterability provides a helpful framework for understanding the phenomenon of machine learning-generated literature. His account of textuality highlights the incalculability and mechanical elements characteristic of all texts, including machine-generated texts. By applying Derrida's concept to the phenomenon of machine creation, we can deconstruct the distinction between human and non-human creation. As I propose in the conclusion to this paper, this provides a basis on which to consider potential positive uses of machine learning.

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.

In this short paper, I respond to Keith Raymond Harris' paper "Synthetic Media, The Wheel, and the Burden of Proof". In particular, I examine his arguments against two prominent approaches employed to deal with synthetic media such as deepfakes and other GenAI content, namely, the "reactive" and "proactive" approaches. In the first part, I raise a worry about the problem Harris levels at the reactive approach, before providing a constructive way of expanding his worry regarding the proactive approach.