The Cultural Life of Machine Learning

An Incursion into Critical AI Studies

Jonathan Roberge, Michael Castelle, Jonathan Roberge, Michael Castelle

• Publisher: Palgrave Macmillan
• Year: 2020
• Print ISBN:
  9783030562854
• eBook ISBN:
  9783030562861
• Topic: Social Sciences
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Social Sciences

This book brings together the work of historians and sociologists with perspectives from media studies, communication studies, cultural studies, and information studies to address the origins, practices, and possible futures of contemporary machine learning. From its foundations in 1950s and 1960s pattern recognition and neural network research to the modern-day social and technological dramas of DeepMind's AlphaGo, predictive political forecasting, and the governmentality of extractive logistics, machine learning has become controversial precisely because of its increased embeddedness and agency in our everyday lives. How can we disentangle the history of machine learning from conventional histories of artificial intelligence? How can machinic agents' capacity for novelty be theorized? Can reform initiatives for fairness and equity in AI and machine learning be realized, or are they doomed to cooptation and failure? And just what kind of "learning" does machine learning truly represent? We empirically address these questions and more to provide a baseline for future research.

Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

© The Author(s) 2021

J. Roberge, M. Castelle (eds.)The Cultural Life of Machine Learninghttps://doi.org/10.1007/978-3-030-56286-1_1

Begin Abstract

1. Toward an End-to-End Sociology of 21st-Century Machine Learning

Jonathan Roberge¹ and Michael Castelle²

(1)

Centre Urbanisation Culture Société, Institut National de La Recherche Scientifique, Quebec City, QC, Canada

(2)

Centre for Interdisciplinary Methodologies, University of Warwick, Coventry, UK

Jonathan Roberge

Email: [email protected]

Michael Castelle (Corresponding author)

Email: [email protected]

End Abstract

The world of contemporary machine learning (ML)—specifically in the domain of the multilayered “deep” neural networks, generative adversarial networks, differentiable programming, and related novelties in what is known as artificial intelligence (AI)—poses difficulties for those in the social sciences, like us, who wish to take its rich and varied phenomena as objects of study. We want, ideally, to be able to offer timely contributions to present-day, pressing debates regarding these technologies and their impacts; but at the same time, we would like to make claims that persist beyond the specific features of today’s (or yesterday’s) innovations. The rapid pace of technical and institutional change in ML today—in which researchers, practitioners, think tanks, and policymakers are breathlessly playing a game of catch-up with each other—only exacerbates this tension. While the topic of AI has attracted interest from social scientists and humanists in the past, the recent conjunction of ML hype, massive allocations of technological and financial resources, internal scientific controversies about the validity of connectionist approaches, and discourses about hopes and fears all mark the rise to prominence of twenty-first-century machine learning and deep learning (DL) as a paradigmatically novel sociotechnical phenomenon. In a nutshell, what we are witnessing is nothing less than an epistemic shock or what Pasquinelli (2015) has referred to as an epistemic “trauma.” For scholars of cultural life—such as sociologists, media scholars, and those affiliated with science and technology studies—this situation forces us to ask by what methods we can possibly stay up to date with these radical transformations‚ while also being able to provide commentary of some significance. How, especially, would it be possible to make sense of the present challenges posed by ML, but in a way that allows for a more complex (and indeed “deeper”) understanding currently unavailable to ML’s practitioners? In this introduction, we want to wager that it may be more productive to embrace these tensions than to attempt to fully resolve them. For instance, it is certainly possible to be technically precise while proposing perspectives quite distant from the computing sciences—the different chapters assembled here are a testimony to this—and it is certainly possible to engage with these technologies and their many subtleties while remaining focused (or, indeed, “trained”) on the more historical and cultural if not mythical aspects of their deployment. The list of dualities does not stop there, of course. ML and modern AI models are simultaneously agents for epistemology and, increasingly, ontology; that is to say, they are a way of knowing as well as of being in the world. They are part of a discourse as much as they are a mode of action, and they are a description of the world and its social composition as much as a prescription of what it ought to be. In turn, the study of machine learning must be aware of this epistemological/ontological tension and be willing to carefully navigate it.

It should perhaps not be surprising that this is not the first time that critical reflections on artificial intelligence emerging from the social sciences have had to fight for their legitimacy. In the mid-1980s, Bloomfield’s “The Culture of Artificial Intelligence” (1987)—a work today almost entirely forgotten—forcefully argued against the “exclusion of sociological questions from any serious examination of AI” and the “foreclosure of sociology to questions of social impact” (pp. 63–67). Around the same time, a better-remembered piece by Woolgar (1985) raised the question: “why not a sociology of machines?”—primarily to indicate that such an endeavor must go beyond simply examining the impacts of technology and attend to its genesis and social construction. What these kinds of positions had in common was a commitment to develop a more holistic approach, in which no aspect of these so-called intelligent technologies would be left out of consideration; so we see in Schwartz (1989) the idea that a proper sociology of AI could ask “under what conditions and in what settings is a model deemed adequate?,” and in Forsythe’s (1993) work the argument that “engineers’ assumptions have some unintended negative consequences for their practice, for the systems they build, and (potentially at least) the broader society” (p. 448). Fast forward some 30-plus years, and the need to make social-scientific discourse on what one might call “21st-century” AI both socially pertinent and accurate has returned with a vengeance. If we consider the sociotechnical genesis of these techniques as “upstream” and their eventual social impact as “downstream,” then we can see critics like Powles and Nissenbaum (2018), who write of the “seductive diversion of ‘solving’ bias in artificial intelligence,” as warning against an overemphasis on upstream engineering dilemmas without considering how “scientific fairness” comes to be deployed in practice; and we can see Roberge, Senneville, and Morin (2020) discussion of regulatory bodies such as Quebec’s Observatory on the Social Impact of AI (OBVIA) as warning of a corresponding overemphasis on “downstream” social impact, which does not see that said social impact is explicitly entangled with the development of the commercial AI research power center known as the Montréal hub.

As a corrective, we want to propose the need for what could be called—with a wink and a nod to deep learning methodology—an end-to-end sociology of contemporary ML/AI, which understands this explicit entanglement of “upstream” and “downstream” and instead trains itself on the entire sociotechnical and political process of modern machine learning from genesis to impact and back again. In this, we find ourselves in line with scholars like Sloane and Moss (2019) who have recently argued, for an audience of AI practitioners, that it is necessary to overcome “AI’s social science deficit” by “leveraging qualitative ways of knowing the sociotechnical world.” Such a stance justifies the value of historical, theoretical, and political research at both an epistemological level of how AI/ML comes to produce and justify knowledge, and at an ontological level of understanding the essence of these technologies and how we can come to coexist with them in everyday practice. But to do so requires an epistemic step that ML practitioners have not fully accepted themselves, namely, to insist on a definition of ML/AI as a “co-production requiring the interaction of social and technical processes” (Holton & Boyd, 2019, p. 2). Radford and Joseph (2020), for their part, have proposed a comparable framework that they call “theory in, theory out,” in which “social theory helps us solve problems arising at every step in the machine learning for social data pipeline” (p. 2; emphasis added). These perspectives represent threads that weave in and out of the chapters in this book as they address machine learning and artificial intelligence from differing historical, theoretical, and political perspectives from their epistemic genesis to sociotechnical implementations to social impact. These chapters can be seen to represent a different attempt to bring these proposals into reality with empirically motivated thinking and research.

To engage with machine learning requires, to some extent, understanding better what these techniques and technologies are about in the first place for its practitioners. What are the baseline assumptions and technical-historical roots of ML? What ways of knowing do these assumptions promote? While it is not uncommon to read that ML represents a “black boxed” technology by both insiders and outsiders, it is nonetheless important to stress how counterproductive such a claim can be, in part because of its bland ubiquity. Yes, ML can be difficult to grasp due to its apparent (if not always actual) complexity of large numbers of model parameters, the rapid pace of its development in computer science, and the array of sub-techniques it encompasses (whether they be the genres of learning, such as supervised, unsupervised, self-supervised, or the specific algorithmic models such as decision trees, support vector machines, or neural networks). As of late, different scholars have tried to warn that the “widespread notion of algorithms as black boxes may prevent research more than encouraging it” (Bucher, 2016, p. 84; see also Burrell, 2016; Geiger, 2017; Sudmann, 2018). Hence, the contrary dictum—“do not fear the black box” (Bucher, 2016, p. 85)—encourages us to deconstruct ML’s fundamental claims about itself, while simultaneously paying special attention to its internal logics and characteristics and, to some degree, aligning social scientists with AI researchers who are also genuinely curious about the apparent successes and...

Table of contents

• Cover
• Front Matter
• 1. Toward an End-to-End Sociology of 21st-Century Machine Learning
• 2. Mechanized Significance and Machine Learning: Why It Became Thinkable and Preferable to Teach Machines to Judge the World
• 3. What Kind of Learning Is Machine Learning?
• 4. The Other Cambridge Analytics: Early “Artificial Intelligence” in American Political Science
• 5. Machinic Encounters: A Relational Approach to the Sociology of AI
• 6. AlphaGo’s Deep Play: Technological Breakthrough as Social Drama
• 7. Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge
• 8. Planetary Intelligence
• 9. Critical Perspectives on Governance Mechanisms for AI/ML Systems
• Back Matter