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Illuminates how algorithms, intertwined with human biases, damage political discourse and civic engagement.

Algorithmic Worldmaking is an urgent exploration of the dynamic relationship between algorithms that encode their human creators’ assumptions and the humans whose choices are shaped by these algorithms in search engines, social media, and other digital spaces. Transcending discussions of one or the other, Jeremy David Johnson traces the corrupting political and social influences that arise from their mutual interaction.

Johnson uses the concept of kosmos in its sense of a dynamic order to frame the interplay between algorithms, humans, and their environments. He first shows how algorithms, far from being objective or unbiased, perpetuate human errors. Johnson then suggests a framework of four parts—navigation, exploration, maintenance, and monetization—to map the variety of political consequences to a society influenced by these four factors.

Citing controversies at major platforms such as Google, YouTube, and Facebook, Johnson demonstrates how algorithms limit and shape human thought. He makes several persuasive arguments. First, algorithms and humans share agency but humans have exceptional responsibility. Second, the algorithmic kosmos mirrors and shapes social oppression. Third, algorithms incentivize capitalist exploitation. Last, these influences damage democratic deliberation.

This landmark study is essential for scholars and students of political science, media studies, and those interested in the perilous implications of algorithmic systems on civic and political life.

A critique of what lies behind the use of data in contemporary education policy 
 

While the science fiction tales of artificial intelligence eclipsing humanity are still very much fantasies, in Algorithms of Education the authors tell real stories of how algorithms and machines are transforming education governance, providing a fascinating discussion and critique of data and its role in education policy.

Algorithms of Education explores how, for policy makers, today’s ever-growing amount of data creates the illusion of greater control over the educational futures of students and the work of school leaders and teachers. In fact, the increased datafication of education, the authors argue, offers less and less control, as algorithms and artificial intelligence further abstract the educational experience and distance policy makers from teaching and learning. Focusing on the changing conditions for education policy and governance, Algorithms of Education proposes that schools and governments are increasingly turning to “synthetic governance”—a governance where what is human and machine becomes less clear—as a strategy for optimizing education.

Exploring case studies of data infrastructures, facial recognition, and the growing use of data science in education, Algorithms of Education draws on a wide variety of fields—from critical theory and media studies to science and technology studies and education policy studies—mapping the political and methodological directions for engaging with datafication and artificial intelligence in education governance. According to the authors, we must go beyond the debates that separate humans and machines in order to develop new strategies for, and a new politics of, education.

An incisive framework for understanding how predictive AI has reshaped our relationship with time.

Ex Ante argues that AI systems draw us into a new experience of time, a continuous stream of machine-generated futures that reshapes our relationship to ourselves, to capitalism, and to infrastructure. Unlike other predictive tools that help us prepare for what might come, these projections—produced by algorithms and sourced by massive flows of data—calibrate our actions moment by moment, second by second. 

In this book, John Cheney-Lippold offers a framework he calls the ex ante, Latin for “before the event,” as a way to consider how the future has become capitalism’s latest resource for exploitation. Through case studies spanning TikTok beauty filters that have fueled a surge in plastic surgery consultations, chess engines that redefine how the game is played, online sports betting platforms that commodify probability itself, hospital sepsis algorithms that transform the labor of nursing, and coordinated vehicle networks that collectively anticipate their surroundings to function, Ex Ante offers an exploration and powerful critique of our algorithmic present and the powers behind it.

Uncovering race, gender, and sexual biases in popular self-publishing platforms

The average reader need not go far in a bookstore before, knowingly or not, they encounter authors who started their careers by self-publishing prior to achieving commercial success. Examples include Margaret Atwood, Andy Weir, Colleen Hoover, Anna Todd, E. L. James, Scarlett St. Clair, and many more. Such stories of self-made writers are compelling and seem more attainable to others with the accessibility of modern publishing platforms such as Amazon, Apple, Google, Kobo, Wattpad, Webtoon, Radish, Inkitt, Qidian, Tapas, and Swoon Reads. However, as Claire Parnell uncovers in her examination of the two most popular—Amazon and Wattpad—these services in fact perpetuate systemic racial, gender, and sexual bias against authors of color and queer authors through their technological, economic, social, and cultural structures.

At a time when there is a real reckoning with the discrimination that has resulted in publishing opportunities for only relatively few privileged authors—who are often White, upper class, and male—self-publishing presents itself as an equalizer of sorts. Exploring that idea, Parnell shows that these platforms are not just intermediaries for information; they structure content and users in multiple, often inequitable, ways through their ability to set market conditions and apply algorithmic sorting. Combining original interviews, walkthrough method, metadata analysis, and more, Parnell finds that self-publishing platforms reproduce challenges for authors from marginalized communities. Far from equalizing the market, the new platforms instead frequently perpetuate the stubborn barriers to mainstream success for BIPOC and queer authors.

A wide-ranging history of the algorithm.

Bringing together the histories of mathematics, computer science, and linguistic thought, Language and the Rise of the Algorithm reveals how recent developments in artificial intelligence are reopening an issue that troubled mathematicians well before the computer age: How do you draw the line between computational rules and the complexities of making systems comprehensible to people? By attending to this question, we come to see that the modern idea of the algorithm is implicated in a long history of attempts to maintain a disciplinary boundary separating technical knowledge from the languages people speak day to day.
 
Here, Jeffrey M. Binder offers a compelling tour of four visions of universal computation that addressed this issue in very different ways: G. W. Leibniz’s calculus ratiocinator; a universal algebra scheme Nicolas de Condorcet designed during the French Revolution; George Boole’s nineteenth-century logic system; and the early programming language ALGOL, short for algorithmic language. These episodes show that symbolic computation has repeatedly become entangled in debates about the nature of communication. Machine learning, in its increasing dependence on words, erodes the line between technical and everyday language, revealing the urgent stakes underlying this boundary.
 
The idea of the algorithm is a levee holding back the social complexity of language, and it is about to break. This book is about the flood that inspired its construction.