The cultural rise of “big data” in the recent years has pressured a number of occupations to make an epistemological shift toward data-driven science. Though expressed as a professional move, this article argues that the push incorporates gendered assumptions that disadvantage women. Using the human resource occupation as an example, I demonstrate how normative perceptions of feminine “soft skills” are seen as irreconcilable with the masculine “hard numbers” of a data-driven epistemology. The history of human resources reflects how assumptions of (...) a biological fit with an occupation limit what women can convincingly describe as her skillsets. However, challenging this cannot stay within the confines of the occupation itself. To undo sexist thinking, it is necessary to understand the broader networks of patriarchal power that dictate how value is defined in corporate environments, especially within other high status professions in business. (shrink)

The article presents the sociology of knowledge approach to discourse (SKAD). SKAD, which has been in the process of development since the middle of the 1990s, is now a widely used framework among social scientists in discourse research in the German-speaking area. It links arguments from the social constructionist tradition, following Berger and Luckmann, with assumptions based in symbolic interactionism, hermeneutic sociology of knowledge, and the concepts of Michel Foucault. It argues thereby for a consistent theoretical and methodological grounding of (...) a genuine social sciences perspective on discourse interested in the social production, circulation and transformation of knowledge, that is in social relations and politics of knowledge in the so-called ‘knowledge societies’. Distancing itself from Critical Discourse Analysis, Linguistics, Ethnomethodology inspired discourse analysis and the Analysis of Hegemonies, following Laclau and Mouffe, SKAD’s framework has been built up around research questions and concerns located in the social sciences, referring to public discourse and arenas as well as to more specific fields of (scientific, religious, etc.) discursive struggles and controversies around problematizations (Foucault). (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)

In this article, we shed light on the emergence, diffusion, and use of socio-technological future visions. The artificial intelligence future vision of the German federal government is examined and juxtaposed with the respective news media coverage of the German media. By means of a content analysis of frames, it is demonstrated how the German government strategically uses its AI future vision to uphold the status quo. The German media largely adapt the government´s frames and do not integrate alternative future narratives (...) into the public debate. These findings are substantiated in the framing of AI futures in policy documents of the German government and articles of four different German newspapers. It is shown how the German past is mirrored in the German AI future envisioned by the government, safeguarding the present power constellation that is marked by a close unity of politics and industry. The German media partly expose the government´s frames and call for future visions that include fundamentally different political designs less influenced by the power structures of the past and present. (shrink)

This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial intelligence is viewed as amongst the technological advances that will reshape modern societies and their relations. While the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories (...) use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us, ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all. (shrink)

An arms race for an artificial general intelligence would be detrimental for and even pose an existential threat to humanity if it results in an unfriendly AGI. In this paper, an all-pay contest model is developed to derive implications for public policy to avoid such an outcome. It is established that, in a winner-takes-all race, where players must invest in R&D, only the most competitive teams will participate. Thus, given the difficulty of AGI, the number of competing teams is unlikely (...) ever to be very large. It is also established that the intention of teams competing in an AGI race, as well as the possibility of an intermediate outcome, is important. The possibility of an intermediate prize will raise the probability of finding the dominant AGI application and, hence, will make public control more urgent. It is recommended that the danger of an unfriendly AGI can be reduced by taxing AI and using public procurement. This would reduce the pay-off of contestants, raise the amount of R&D needed to compete, and coordinate and incentivize co-operation. This will help to alleviate the control and political problems in AI. Future research is needed to elaborate the design of systems of public procurement of AI innovation and for appropriately adjusting the legal frameworks underpinning high-tech innovation, in particular dealing with patenting by AI. (shrink)

The social impacts of computer technology are often glorified in public discourse, but there is growing concern about its actual effects on society. In this article, we ask: how does “consent” as an analytical framework make visible the social dynamics and power relations in the capture, extraction, and labor of data science knowledge production? We hypothesize that a form of boundary violation in data science workplaces—gender harassment—may correlate with the ways humans’ lived experiences are extracted to produce Big Data. The (...) concept of consent offers a useful way to draw comparisons between gender relations in data science and the means by which machines are trained to learn and reason. Inspired by how Big Tech leaders describe unsupervised machine learning, and the co-optation of “revolutionary” rhetoric they use to do so, we introduce a concept we call “techniques of invisibility.” Techniques of invisibility are the ways in which an extreme imbalance between exposure and opacity, demarcated along fault lines of power, are fabricated and maintained, closing down the possibility for bidirectional transparency in the production and applications of algorithms. Further, techniques of invisibility, which we group into two categories—epistemic injustice and the Brotherhood—include acts of subjection by powerful actors in data science designed to quell resistance to exploitative relations. These techniques may be useful in making further connections between epistemic violence, sexism, and surveillance, sussing out persistent boundary violations in data science to render the social in data science visible, and open to scrutiny and debate. (shrink)

In this article, the author describes sweeping changes in the gender system and offers explanations for why change has been uneven. Because the devaluation of activities done by women has changed little, women have had strong incentive to enter male jobs, but men have had little incentive to take on female activities or jobs. The gender egalitarianism that gained traction was the notion that women should have access to upward mobility and to all areas of schooling and jobs. But persistent (...) gender essentialism means that most people follow gender-typical paths except when upward mobility is impossible otherwise. Middle-class women entered managerial and professional jobs more than working-class women integrated blue-collar jobs because the latter were able to move up while choosing a “female” occupation; many mothers of middle-class women were already in the highest-status female occupations. The author also notes a number of gender-egalitarian trends that have stalled. (shrink)

The imaginative context in which artificial intelligence is embedded remains a crucial touchstone from which to understand and critique both the histories and prospective futures of an AI-driven world. A recent article from Cave and Dihal sets out a narrative schema of four hopes and four corresponding fears associated with intelligent machines and AI. This article seeks to respond to the work of Cave and Dihal by presenting a gendered reading of this schema of hopes and fears. I offer a (...) brief genealogy of narratives which feature female automata, before turning to examine how gendered technology today—particularly AI assistants like Siri and Alexa—reproduces the historical narratives associated with intelligent machines in new ways. Through a gendered reading of the hopes and fears associated with AI, two key responses arise. First, that the affective reactions to intelligent machines cannot be readily separated where such machines are gendered female. And second, that the gendering of AI technologies today can be understood as an attempt to reconcile the opposing hopes and fears AI produces, and that this reconciliation is based on the association of such technologies with traditional notions of femininity. Critically, a gendered reading enables us to problematize the narratives associated with AI and expose the power asymmetries that lie within, and the technologies which arise out of, such narratives. (shrink)

This paper sheds light on the role of digital platform labour in the development of today’s artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad ‘micro-workers’, recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, ‘artificial intelligence preparation’, ‘artificial intelligence verification’ (...) and ‘artificial intelligence impersonation’. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes – not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness. (shrink)

Since industrial trade fair Hannover Messe 2011, the term “Industrie 4.0” has ignited a vision of a new Industrial Revolution and has been inspiring a lively, ongoing debate among the German public about the future of work, and hence society, ever since. The discourse around this vision of the future eventually spread to other countries, with public awareness reaching a temporary peak in 2016 when the World Economic Forum’s meeting in Davos was held with the motto “Mastering the Fourth Industrial (...) Revolution.” How is it possible for a vision originally established by three German engineers to unfold and bear fruit at a global level in such a short period of time? This article begins with a summary of the key ideas that are discussed under the label Industrie 4.0. The main purpose, based on an in-depth discourse analysis, is to debunk the myth about the origin of this powerful vision and to trace the narrative back to the global economic crisis in 2009 and thus to the real actors, central discourse patterns, and hidden intentions of this vision of a new Industrial Revolution. In conclusion, the discourse analysis reveals that this is not a case of visioneering but one of a future told, tamed, and traded. (shrink)