One of the biggest contemporary challenges in education is the appropriate application of advanced digital solutions. If properly implemented, AI could benefit students, opening the door for personalized study programs. However, we need to ensure that AI in classrooms is used responsibly and that it does not pose a threat to students in any way. More specifically, we need to preserve the moral and epistemic values we wish to pass on to future generations and ensure the inclusion of underprivileged students. (...) Moreover, there is a huge problem of different accessibility to technology and representation in the educational material between the Global South and the Global North. This can cause both global epistemic and moral injustice. On the other hand, AI has the potential to increase global justice in education. Therefore, it is of the utmost importance to use it in accordance with the values of equity, fairness, and inclusion that secure global justice for underprivileged groups. (shrink)

The potential use, relevance, and application of AI and other technologies in the democratic process may be obvious to some. However, technological innovation and, even, its consideration may face an intuitive push-back in the form of algorithm aversion (Dietvorst et al. J Exp Psychol 144(1):114–126, 2015). In this paper, I confront this intuition and suggest that a more ‘extreme’ form of technological change in the democratic process does not necessarily result in a worse outcome in terms of the fundamental concepts (...) of democracy and the Rule of Law. To provoke further consideration and illustrate that initial intuitions regarding democratic innovation may not always be accurate, I pose and explore four ways that AI and other forms of technology could be used to augment the representative democratic process. The augmentations range from voting online to the wholesale replacement of the legislature’s human representatives with algorithms. After first noting the intuition that less invasive forms of augmented democracy may be less objectionable than more extreme forms, I go on to critically assess whether the augmentation of existing systems satisfies or enhances ideas associated with democracy and the Rule of Law (provided by Dahl and Fuller). By imagining a (not too far-fetched) future in a (not too far-removed) democratic society, my conclusion is that, when it comes to democracy and the Rule of Law, intuitions regarding technology may lead us astray. (shrink)

This paper investigates how intuitions about scientific discovery using artificial intelligence can be used to improve our understanding of scientific discovery more generally. Traditional accounts of discovery have been agent-centred: they place emphasis on identifying a specific agent who is responsible for conducting all, or at least the important part, of a discovery process. We argue that these accounts experience difficulties capturing scientific discovery involving AI and that similar issues arise for human discovery. We propose an alternative, collective-centred view as (...) superior for understanding discovery, with and without AI. This view maintains that discovery is performed by a collective of agents and entities, each making contributions that differ in significance and character, and that attributing credit for discovery depends on various finer-grained properties of the contributions made. Detailing its conceptual resources, we argue that this view is considerably more compelling than its agent-centred alternative. Considering and responding to several theoretical and practical challenges, we point to concrete avenues for further developing the view we propose. (shrink)

Studies investigating the relationship between artificial intelligence and science tend to adopt a partial view. There is no broad and holistic view that synthesizes the channels through which this interaction occurs. Our goal is to systematically map the influence of the latest AI techniques on science. We draw on the work of Nathan Rosenberg to develop a taxonomy of the effects of technology on science. The proposed framework comprises four categories of technology effects on science: intellectual, economic, experimental and instrumental. (...) The application of the framework in the relationship between ML/DL and science allowed the identification of multiple triggers activated by the new techniques in the scientific field. Visualizing these different channels of influence allows us to identify two pressing, emerging issues. The first is the concentration of experimental effects in a few companies, which indicates a reinforcement effect between more data on the phenomenon and more capacity to commercialize the technique. The second is the diffusion of new techniques lacking in explanation throughout the fabric of science. The value of this article is twofold. First, it provides a simple framework to assess the relations between technology and science. Second, it provides this broad and holistic view of the influence of new AI techniques on science. More specifically, the article details the channels through which this relationship occurs, the nature of these channels and the loci in which the potential effects on science unfolds. (shrink)

Recent technological advances have accelerated the use of artificial intelligence (AI) in the world. Public concerns over AI in ethical, legal, and social issues (ELSI) may have been enhanced, but their awareness has not been fully examined between countries and cultures. We created four scenarios regarding the use of AI: “voice,” “recruiting,” “face,” and “immigration,” and compared public concerns in Japan, the US, Germany, and the Republic of Korea (hereafter Korea). Additionally, public ELSI concerns in respect of AI were measured (...) using four items: ethics, tradition, law and social benefit. Respondents with AI knowledge tended to exhibit stronger concern about ELSI in various situations. In terms of law concerns, Japanese respondents displayed greater concerns. In contrast, the US, when compared Japan, expressed a relatively optimistic view of the current law landscape. Regarding social benefits, Korea, compared to Japan, exhibited a more positive outlook, whereas Germany, in comparison to Japan, expressed heightened concerns about it across different scenarios. (shrink)