Recent scholarship in science, technology, and society has emphasized the neoliberal character of science today. This article draws on the history of US science and technology policy to argue against thinking of recent changes in science as fundamentally neoliberal, and for thinking of them instead as reflecting a process of “economization.” The policies that changed the organization of science in the United States included some that intervened in markets and others that expanded their reach, and were promoted by some groups (...) who were skeptical of free markets and others who embraced them. In both cases, however, new policies reflected growing political concern with “the economy” and related abstractions and a new understanding of S&T as inputs into a larger economic system that government could manipulate through policy. Understanding trends in US S&T policy as resulting from economization, not just neoliberalism, has implications for thinking about the present and likely future of science and S&T policy. (shrink)

Two features characterize new and emerging technosciences. The first one is the production of peculiar ontologies. The human agent is confronted with a biophysical world the contingent, indeterminate character of which does not hamper but expands the scope of purposeful action. Uncertainty is increasingly regarded as a resource for an expanding will rather than a drawback for a disoriented agent. The second feature is that ethics is increasingly considered as the core regulatory means of this messy, ever-changing world. The ambivalences (...) of the ethical government of contingent assemblages are discussed by focusing on the governmentality perspective. The latter helps to make sense of the regulatory alliance between ethics and technoscience. A reflection on Foucault’s account of ethics shows that the emancipatory role of the latter is today hampered by its embroilment with the instrumental reason it aims to govern, nor can older models of ethical commitment find any straightforward application. Mapping the issue in terms of mutual constitution of power, potentiality and possibility gives salience to a particular question: what we are able not to do. (shrink)

National Artificial Intelligence (AI) strategies articulate imaginaries of the integration of AI into society and envision the governing of AI research, development and applications accordingly. To integrate these central aspects of national AI strategies under one coherent perspective, this paper presented an analysis of Germany’s strategy ‘AI made in Germany’ through the conceptual lens of ordoliberal political rationality. The first part of the paper analyses how the guiding vision of a human-centric AI not only adheres to ethical and legal principles (...) consistent with Germany’s liberal democratic constitutional system but also addresses the risks and promises inherent to the ordoliberal problematization of freedom. Second, it is scrutinized how the strategy cultivates the fear of not achieving technological sovereignty in the AI sector. Thereby, it frames the global AI race as a race of competing (national) approaches to governing AI and articulates an ordoliberal approach to governing AI (the ‘third way’), according to which government has to operate between the twin dangers of governing too much and not governing enough. Third, the paper analyses how this ordoliberal proportionality of governing structures Germany’s Science Technology & Innovation Policy. It is shown that the corresponding risk-based approach of regulating AI constitutes a security apparatus as it produces an assessment of fears: weighting the fear of the failure to innovate with the fear of the ramifications of innovation. Finally, two lines of critical engagement based on this analysis are conducted. (shrink)

Algorithms, or rather algorithmic actions, are seen as problematic because they are inscrutable, automatic, and subsumed in the flow of daily practices. Yet, they are also seen to be playing an important role in organizing opportunities, enacting certain categories, and doing what David Lyon calls “social sorting.” Thus, there is a general concern that this increasingly prevalent mode of ordering and organizing should be governed more explicitly. Some have argued for more transparency and openness, others have argued for more democratic (...) or value-centered design of such actors. In this article, we argue that governing practices—of, and through algorithmic actors—are best understood in terms of what Foucault calls governmentality. Governmentality allows us to consider the performative nature of these governing practices. They allow us to show how practice becomes problematized, how calculative practices are enacted as technologies of governance, how such calculative practices produce domains of knowledge and expertise, and finally, how such domains of knowledge become internalized in order to enact self-governing subjects. In other words, it allows us to show the mutually constitutive nature of problems, domains of knowledge, and subjectivities enacted through governing practices. In order to demonstrate this, we present attempts to govern academic writing with a specific focus on the algorithmic action of Turnitin. (shrink)

Developers are often the engine behind the creation and implementation of new technologies, including in the artificial intelligence surge that is currently underway. In many cases these new technologies introduce significant risk to affected stakeholders; risks that can be reduced and mitigated by such a dominant party. This is fully recognized by texts that analyze risks in the current AI transformation, which suggest voluntary adoption of ethical standards and imposing ethical standards via regulation and oversight as tools to compel developers (...) to reduce such risks. However, what these texts usually sidestep is the question of how aware developers are to the risks they are creating with these new AI technologies, and what their attitudes are towards such risks. This paper asks to rectify this gap in research, by analyzing an ongoing case study. Focusing on six Israeli AI startups in the field of radiology, I carry out a content analysis of their online material in order to examine these companies’ stances towards the potential threat their automated tools pose to patient safety and to the work-standing of healthcare professionals. Results show that these developers are aware of the risks their AI products pose, but tend to deny their own role in the technological transformation and dismiss or downplay the risks to stakeholders. I conclude by tying these findings back to current risk-reduction recommendations with regards to advanced AI technologies, and suggest which of them hold more promise in light of developers’ attitudes. (shrink)