Elena Esposito - Artificial Communication: How Algorithms Produce Social Intelligence

Ideas Series

Edited by David Weinberger

The Ideas Series explores the latest ideas about how technology is affecting culture, business, science, and everyday life. Written for general readers by leading technology thinkers and makers, books in this series advance provocative hypotheses about the meaning of new technologies for contemporary society.

The Ideas Series is published with the generous support of the MIT Libraries.

Hacking Life: Systematized Living and Its Discontents, Joseph M. Reagle, Jr.

The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future, Ben Green

Sharenthood: Why We Should Think before We Post about Our Kids, Leah A. Plunkett

Data Feminism, Catherine DIgnazio and Lauren Klein

Artificial Communication: How Algorithms Produce Social Intelligence, Elena Esposito

The Digital Closet: How the Internet Became Straight, Alexander Monea

Elena Esposito

The MIT Press

Cambridge, Massachusetts

London, England

2022 Massachusetts Institute of Technology

This work is subject to a Creative Commons CC-BY-NC-ND license.

Subject to such license, all rights are reserved.

The MIT Press would like to thank the anonymous peer reviewers who provided comments on drafts of this book. The generous work of academic experts is essential for establishing the authority and quality of our publications. We acknowledge with gratitude the contributions of these otherwise uncredited readers.

Library of Congress Cataloging-in-Publication Data

Names: Esposito, Elena, author.

Title: Artificial communication : how algorithms produce social intelligence / Elena Esposito.

Description: Cambridge, Massachusetts : The MIT Press, [2022] | Series: Strong ideas series | Includes bibliographical references and index.

Identifiers: LCCN 2021013271 | ISBN 9780262046664 (hardcover)

Subjects: LCSH: TelecommunicationSocial aspects. | Artificial intelligenceSocial aspects. | Online identities. | Social intelligence.

Classification: LCC HM851 .E765 2022 | DDC 303.48/33dc23

LC record available at https://lccn.loc.gov/2021013271

d_r0

For Emma

Algorithms that work with deep learning and big data are getting better and better at doing more and more things: They quickly and accurately produce information, and are learning to drive cars more safely and reliably than humans. They can answer our questions, make conversation, compose music, and read books. And they can even write interesting, appropriate, andif requiredfunny texts.

Yet when it comes to observing this progress, we are seldom completely at easenot only because of our worries about bias, errors, threats to privacy, or malicious uses by corporations and governments. Actually, the better the algorithms become, the more our discomfort increases. A recent article in the New Yorker describes one journalists experience with Smart Compose, a feature of Gmail that suggests endings to your sentences as you type them. The algorithm completed the journalists emails so appropriately, pertinently, and in line with his style that he found himself learning from the machine not only what he would have written, but also what he should have written (and had not thought to), or could want to write. And he didnt like it at all.

This experience, extremely common in our interactions with supposedly intelligent machines, has been labeled the uncanny valley: We compare ourselves to machines, and we dont like it if they seem to be winning. In our endeavors to build intelligent machines, we do not just wonder whether we have succeeded, but if the machines are becoming too smart.

But is this really what we have to worry about? While we may get an eerie feeling around machines that resemble us a little too closely, should we say that the fundamental risk of algorithms is that they might compare or compete with human intelligence? This book starts from the hypothesis that analogies between the performance of algorithms and human intelligence are not only unnecessary, but misleadingeven if the reasoning behind them appears plausible. Today, after all, many algorithms seem to be able to think and communicate. In communication as we know it, our partners have always been human beings, and human beings are endowed with intelligence. If our interlocutor is an algorithm, we impulsively attribute to him or her the characteristics of a human being. If the machine can communicate autonomously, one thinks, it must also be intelligent, although perhaps in a different way than humans. On the basis of this analogy, research has focused on the parallels and differences between human intelligence and machine performance, observing their limits and making comparisons. But is it really advisable to continue following this analogy?

That we can communicate with machines, I argue, does not imply that they have their own intelligence that needs to be explained (an explanation that may also require explaining the mysteries of natural intelligence), but that, foremost, communication is changing. The object of study in this book is not intelligence, which is and remains a mystery, but communication, which we can observe and about which we already know a great deal. For example, we know how communication has changed over centuries and with the evolution of human society. We know that communication has moved from simple interactions between parties sharing physical space to more flexible and inclusive forms, which have also allowed communication with previously inaccessible partners distant in space and time, in increasingly anonymous and impersonal settings.

Within the evolution of communication, the role of human beings has changed profoundly. Today there is no need for partners to be present; there is no need to know who they are and why they communicate, nor to know what they mean and to take it into account. We can read and understand the instruction booklet of a dishwasher without knowing who wrote it and without identifying ourselves with the writers point of view; we interpret a work of art without being bound to the perspective and intention of the artist. There is no need for most information to be stored in someones mind (nobody knows the civil code by heart), and in all cases of fiction, we identify with the characters of novels and films knowing that they never existed and that they are not the authors of the communication they carry along. The idea of successful communication as a precise sharing of identical content between the minds of participants has been unrealistic for many centuries, in practice if not in theory. In most cases, issuers and receivers do not know each other, do not know each others perspectives, contexts, or constraintsand do not need to do so. On the contrary, this lack of transparency allows for otherwise unthinkable degrees of freedom and abstraction.

That communication changes its forms is not new and is not an enigma. Rather, the issue is identifying and understanding the differences and continuities between forms old and new. Today, the autonomy of communication from the cognitive processes of its participants has gone a step further. We need a concept of communication that can take into account the possibility that a communication partner may not be a human being, but instead is an algorithm. The result, already observed today, is a condition in which we have information whose development or genesis we often cannot reconstruct, yet which is nevertheless not arbitrary. The information generated autonomously by algorithms is not random at all and is completely controlledbut not by the processes of the human mind.