Machine Habitus. Massimo Airoldi

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      ISBN-13: 978-1-5095-4327-4

      ISBN-13: 978-1-5095-4328-1 (pb)

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Quote

      Habitus, c’est un grand mot pour dire quelque chose, je crois, de très complexe. C’est à dire, une espèce de petite machine génératrice – pour une analogie un peu sauvage, un programme d’ordinateur – à partir duquel les gens engendrent des foules des réponses à des foules des situations.

       Pierre Bourdieu

       Interview with Antoine Spire, 1990

      If social order is made of propensities to associate, if to be social is a propensity to associate, then big data conversion events operationalize association in matrices of propensity.

       Adrian Mackenzie, 2018

Acknowledgments

      I would like to thank Salvatore Iaconesi and Oriana Persico for taking part in two interview sessions, in the autumn of 2019 and the summer of 2020, and being a unique source of inspiration. I must also thank Hanan Salam, founder of Women in AI, for her technical clarifications and preliminary comments on this book project, and Debora Pizzimenti, for providing me with further details about the IAQOS experience. A big thanks to Alessandro Gandini, Mauro Barisione, Adam Arvidsson, and Polity’s editors and anonymous reviewers, for their insightful comments and encouragement all the way through. I also thank all my colleagues and students at EM Lyon. Last, a huge thanks to Stefania, my most important person.

Figures and Tables

      Figures

      1  1 Algorithms: a conceptual map, from Euclid to AlphaGo

      2  2 Networks of associated words learned by IAQOS

      3  3 An example of a phishing email targeting my professional email address, not automatically marked as ‘spam’

      4  4 On the left-hand side, related music videos network (directed); on the right-hand side, commenter videos network (undirected).

      5  5 Techno-social effects on field boundaries

Tables

      1  1 Machine socialization processes in different types of algorithms

      2  2 Types of user–machine interaction (effects on users in brackets)

      3  3 Research directions for the sociology of algorithms, with selected example studies

Preface

      On 31 March 2019, a new member of the multicultural community of Torpignattara, a semi-peripheral district of the city of Rome, was born. The event was greeted with unprecedented excitement in the neighbourhood, culminating, on the big day, with a small welcome party of friends and curious others who had gathered to support Salvatore and Oriana. Over the previous weeks, everybody had left a message, a wish or even a drawing, in paper boxes distributed for the occasion across the shops and bars of Torpignattara. The neighbourhood became an extended family to the long-awaited newcomer, who was only few days old when it got to know everyone, rolling from door to door in the stroller, and passing from hand to hand. Whether at the local café, or on the way to the drug store, there was always someone with a story to tell – usually about the local community and its history, places, people, food, hopes and fears. The baby listened, and learned. Soon, like any other child in Torpignattara, it would go to the Carlo Pisacane elementary school just around the corner. But IAQOS – that’s its name – was certainly not like other babies. It was the first ‘open-source neighbourhood AI’, developed by the artist and robotic engineer Salvatore Iaconesi together with the artist and communication scientist Oriana Persico, in a collaboration funded by the Italian government and involving several cultural and research institutions.

In concrete terms, IAQOS is a relatively simple software agent that can communicate through a tablet or a computer via natural language, recognizing the voices and gestures of its interlocutors and learning from them. Differently from the algorithmic systems we encounter every day through our devices – such as those running in Google Search, Facebook, Amazon, Instagram, Netflix or YouTube – this open-source art project had no other goal than accumulating social data about the neighbourhood, behaving as a sort of ‘baby AI’. Like a real baby, it observed the surrounding social environment, absorbed a contextual worldview and used the acquired knowledge to successfully participate in social life. By doing all that, during the spring of 2019, IAQOS became in effect a ‘fijo de Torpigna’; that is, an artificial yet authentic member of the local community, sharing a common imaginary, vocabulary and social background, and capable of building social relations (Iaconesi and Persico 2019).

      This peculiar example makes it easier to see what many sociologists and social scientists have so far overlooked: the fact that a machine which learns from patterns in human-generated data, and autonomously manipulates human language, knowledge and relations, is more than a machine. It is a social agent: a participant in society, simultaneously participated in by it. As such, it becomes a legitimate object of sociological research.

We already know that algorithms are instruments of power, that they play with the lives of people and communities in opaque ways and at different scales, deciding who will be eligible or not for a loan with the same statistical nonchalance with which they move emails to the junk folder. We know that filter bubbles threaten to draw digital boundaries among voters and consumers, and that autonomous robots can be trained to kill. Moreover, we know that some algorithms can learn from us. They can learn how to speak like humans, how to write like philosophers, how to recommend songs like music experts. And they can learn how to be sexist like a conservative man, racist like a white supremacist, classist like an elitist snob. In sum, it is increasingly evident how similar we – humans and machines – have become. However, perhaps because comparisons and analyses have mostly been limited to examining cognition, abilities and biases, we have somehow failed to see the sociological reason for this similarity: that is, culture.

      This book identifies culture as the seed transforming machines into social agents. Since the term is ‘one of the two or three most complicated words in the English language’ (Williams 1983: 87), let me clarify: here I use ‘culture’ to refer essentially to practices, classifications, tacit norms and dispositions associated with specific positions in society. Culture is more than data: it is relational patterns in the data. As such, culture operates in the code of machine learning systems,