Intelligent machines are no longer distant fantasies of the future or solely used for industrial purposes; they are real “living” things that operate similarly to humans with verbal and nonverbal communication capabilities. Humans see in such technology the horrifying dangers and the bliss enabled by the saving power. Entrenched in the emotions of hope and fear concerning intelligent machines, humans’ attitudes toward intelligent machines are not free of expectations, judgments, strategies, and selfish agendas. As the discovery of the New Worlds (...) has led to the development of an understanding of self as distinct from the other, narratives on intelligent robots have taken on similar forms of colonial discourse: contradictory manifestations of ethnocentrism (anthropocentrism) and exoticism. Similar to colonial encounters, the racialized Mechanical _Other_ operates like any other marginalized group. Colonial discourse often regards the mechanical _Other_ as both inferior yet exotic, which usually produces a mixed sense of blessing and curse, ambivalence composed of both desire and aversion. In these representations of machinic _otherness,_ one should not miss the fundamental injustice of creating the “Machinic Other” and imposing humans’ viewpoints. The field must expand the purview of intelligent machines to consider the larger historical and cultural forces shaping our understanding of who we are as reflected in the mirror of intelligent machines. (shrink)

How to integrate artificial intelligence technologies in the functioning and structures of our society has become a concern of contemporary politics and public debates. In this paper, we investigate national AI strategies as a peculiar form of co-shaping this development, a hybrid of policy and discourse that offers imaginaries, allocates resources, and sets rules. Conceptually, the paper is informed by sociotechnical imaginaries, the sociology of expectations, myths, and the sublime. Empirically we analyze AI policy documents of four key players in (...) the field, namely China, the United States, France, and Germany. The results show that the narrative construction of AI strategies is strikingly similar: they all establish AI as an inevitable and massively disrupting technological development by building on rhetorical devices such as a grand legacy and international competition. Having established this inevitable, yet uncertain, AI future, national leaders proclaim leadership intervention and articulate opportunities and distinct national pathways. While this narrative construction is quite uniform, the respective AI imaginaries are remarkably different, reflecting the vast cultural, political, and economic differences of the countries under study. As governments endow these imaginary pathways with massive resources and investments, they contribute to coproducing the installment of these futures and, thus, yield a performative lock-in function. (shrink)

This paper addresses the gap between familiar popular narratives describing Artificial Intelligence (AI), such as the trope of the killer robot, and the realistic near-future implications of machine intelligence and automation for technology policy and society. The authors conducted a series of interviews with technologists, science fiction writers, and other experts, as well as a workshop, to identify a set of key themes relevant to the near future of AI. In parallel, they led the analysis of almost 100 recent works (...) of science fiction stories with AI themes to develop a preliminary taxonomy of AI in science fiction. These activities informed the commissioning of six original works of science fiction and non-fiction response essays on the themes of “intelligence” and “justice” that were published as part of the Slate Future Tense Fiction series in 2019 and 2020. Our findings indicate that artificial intelligence remains deeply ambiguous both in the policy and cultural contexts: we struggle to define the boundaries and the agency of machine intelligence, and consequently find it difficult to govern or interact with such systems. However, our findings also suggest more productive avenues of inquiry and framing that could foster both better policy and better narratives around AI. (shrink)

Most accounts of responsibility focus on one type of responsibility, moral responsibility, or address one particular aspect of moral responsibility such as agency. This article outlines a broader framework to think about responsibility that includes causal responsibility, relational responsibility, and what I call “narrative responsibility” as a form of “hermeneutic responsibility”, connects these notions of responsibility with different kinds of knowledge, disciplines, and perspectives on human being, and shows how this framework is helpful for mapping and analysing how artificial intelligence (...) (AI) challenges human responsibility and sense-making in various ways. Mobilizing recent hermeneutic approaches to technology, the article argues that next to, and interwoven with, other types of responsibility such as moral responsibility, we also have narrative and hermeneutic responsibility—in general and for technology. For example, it is our task as humans to make sense of, with and, if necessary, against AI. While from a posthumanist point of view, technologies also contribute to sense-making, humans are the experiencers and bearers of responsibility and always remain in charge when it comes to this hermeneutic responsibility. Facing and working with a world of data, correlations, and probabilities, we are nevertheless condemned to make sense. Moreover, this also has a normative, sometimes even political aspect: acknowledging and embracing our hermeneutic responsibility is important if we want to avoid that our stories are written elsewhere—through technology. (shrink)

There is a long history of the science of intelligent machines and its potential to provide scientific insights have been debated since the dawn of AI. In particular, there is renewed interest in the role of AI in research and research policy as an enabler of new methods, processes, management and evaluation which is still relatively under-explored. This empirical paper explores interviews with leading scholars on the potential impact of AI on research practice and culture through deductive, thematic analysis to (...) show the issues affecting academics and universities today. Our interviewees identify positive and negative consequences for research and researchers with respect to _collective_ and _individual use_. AI is perceived as helpful with respect to information gathering and other narrow tasks, and in support of impact and interdisciplinarity. However, using AI as a way of ‘speeding up—to keep up’ with bureaucratic and metricised processes, may proliferate negative aspects of academic culture in that the expansion of AI in research should assist and not replace human creativity. Research into the future role of AI in the research process needs to go further to address these challenges, and ask fundamental questions about how AI might assist in providing new tools able to question the values and principles driving institutions and research processes. We argue that to do this an explicit movement of meta-research on the role of AI in research should consider the effects for research and researcher creativity. Anticipatory approaches and engagement of diverse and critical voices at policy level and across disciplines should also be considered. (shrink)

This paper focuses on the fact that AI is predominantly portrayed as white—in colour, ethnicity, or both. We first illustrate the prevalent Whiteness of real and imagined intelligent machines in four categories: humanoid robots, chatbots and virtual assistants, stock images of AI, and portrayals of AI in film and television. We then offer three interpretations of the Whiteness of AI, drawing on critical race theory, particularly the idea of the White racial frame. First, we examine the extent to which this (...) Whiteness might simply reflect the predominantly White milieus from which these artefacts arise. Second, we argue that to imagine machines that are intelligent, professional, or powerful is to imagine White machines because the White racial frame ascribes these attributes predominantly to White people. Third, we argue that AI racialised as White allows for a full erasure of people of colour from the White utopian imaginary. Finally, we examine potential consequences of the racialisation of AI, arguing it could exacerbate bias and misdirect concern. (shrink)