How to translate artificial intelligence? Myths and justifications in public discourse

Jonathan Roberge, Marius Senneville & Kevin Morin (2020)

Big Data and Society, 7(1), Open access

Abstract. Automated technologies populating today’s online world rely on social expectations about how “smart” they appear to be. Algorithmic processing, as well as bias and missteps in the course of their development, all come to shape a cultural realm that in turn determines what they come to be about. It is our contention that a robust analytical frame could be derived from culturally driven Science and Technology Studies while focusing on Callon’s concept of translation. Excitement and apprehensions must find a specific language to move past a state of latency. Translations are thus contextual and highly performative, transforming justifications into legitimate claims, translators into discursive entrepreneurs, and power relations into new forms of governance and governmentality. In this piece, we discuss three cases in which artificial intelligence was deciphered to the public: (i) the Montreal Declaration for a Responsible Development of Artificial Intelligence, held as a prime example of how stakeholders manage to establish the terms of the debate on ethical artificial intelligence while avoiding substantive commitment; (ii) Mark Zuckerberg’s 2018 congressional hearing, where he construed machine learning as the solution to the many problems the platform might encounter; and (iii) the normative renegotiations surrounding the gradual introduction of “killer robots” in military engagements. Of interest are not only the rational arguments put forward, but also the rhetorical maneuvers deployed. Through the examination of the ramifications of these translations, we intend to show how they are constructed in face of and in relation to forms of criticisms, thus revealing the highly cybernetic deployment of artificial intelligence technologies.

Extract. “As stated above, debates are a constitutive part of complex phenomena such as the deployment of AI. Success is not defined as the absence of criticism but as its adequate handling. Following Natale and Ballatore, “skepticism and criticism [have] added to AI’s capacity of attracting attention and space [. . .] in the public arena” (2017: 2). What there is, in other words, is a subtle yet forceful dance between the capacity to denunciate and the capacity to justify the technology. From a theoretical point of view, this could be related to Boltanski’s sociology of critical capacities and how, for some, it represents the “symmetrical twin” of Latour and Callon’s theoretical frame (Boltanski, 1990; Boltanski and Thévenot, 2006; Guggenheim and Potthast, 2012).Whereas criticisms come forward in the public discourse questioning any potential flaws, justifications are offered as discourses aimed at appealing to higher principles their proponents hope will command respect—innovation, progress, etc. For Boltanski, criticism and justification are inseparable; as mentioned, they are constructed in face of and in relation to one another.” (p. 4)

Link to the review