New book explores uncommon ground we share in a post-truth world

BUFFALO, N.Y. – Whether through political partisanship or algorithms, virtual assistants and other technology, people’s grasp of the truth and concrete fact is becoming increasingly tenuous. A University at Buffalo professor’s new book examines what happens when, as German philosopher Friedrich Nietzsche famously said, “There are no facts, only interpretations”?

“There Are No Facts: Attentive Algorithms, Extractive Data Practices, and the Quantification of Everyday Life” (MIT Press) is due out Nov. 22. The book is the first monograph by Mark Shepard, an associate professor who holds a joint appointment in the Department of Architecture in UB’s School of Architecture and Planning and the Department of Media Study in UB’s College of Arts and Sciences.

A book launch and panel discussion will take place from 6-7:30 p.m. Nov. 9 in Hayes Hall 403 on UB’s South Campus, with a reception immediately following where the book will be available for purchase from local bookstore affiliate Fitz Books. The event will also be live-streamed via Zoom.

Shepard will be joined by experts from across the fields of architecture and media, including Hadas Steiner, an associate professor of architecture at UB who will serve as moderator for the panel discussion.

Panelists include Malcolm McCullough, a professor of architecture in the University of Michigan’s Taubman College of Architecture and Urban Planning, and Molly Wright Steenson, vice provost for faculty at Carnegie Mellon University, associate professor in the School of Design and the K&L Gates Associate Professor of Ethics & Computational Technology.

“This book explores the uncommon ground we share in a post-truth world,” says Shepard.

The book examines the entanglements of people and data, code and space, knowledge and power that have produced an uncommon ground — a disaggregated public sphere where the extraction of behavioral data and their subsequent processing and sale have led to the emergence of micropublics of ever-finer granularity.

Several of the key ideas developed in the book can be traced back to a 2016 workshop Shepard participated in at the Radcliffe Institute for Advanced Study at Harvard University, where he gave a presentation on the changing nature of bias in urban research.

This all emerged amidst the popularization of the idea of “post-truth,” the Oxford Dictionaries Word of the Year in 2016, which refers to circumstances in which appeals to emotion and personal belief are more influential in shaping public opinion than objective facts. The term post-truth was coined by Steve Teisch in a 1992 article for The Nation. Years later, Stephen Colbert famously coined the term “truthiness” to describe similar circumstances.

“How we come to know cities, move through them, orient ourselves within them and otherwise inhabit them is based on how we develop spatial knowledge of them,” Shepard says, adding that the workshop looked at how various observational devices such as those for still photography and moving images or, more recently, data-centric approaches, shape what we see through their inherent biases.

“The basis there was connecting on the one hand artificial intelligence techniques for generating insight into and making truth claims about cities involving machine learning and big data with how they influence what we come to know about cities,” he says. “Of course, their role in the dramatic political events of 2016 — the U.S. presidential election and the Brexit vote in the UK — were also very influential in shaping the contents of the book.”

Shepard conducted the bulk of the research and initial writing in 2019 in a cabin in the woods on the outskirts of Peterborough, New Hampshire, while he was a resident artist at MacDowell. Much of the writing took place during the COVID-19 pandemic, which further shaped the manuscript, including a chapter devoted to the spread of mis- and disinformation about the virus and the vaccines.

“The book is organized according to an increasing scale of sites within which these practices play out, from the trans-locality of the home to the planetary reach of the COVID-19 pandemic, with stops along the way at the corner urban minimarket, a neighborhood for the proverbial 1%, a waterfront district in Toronto, and a national election,” Shepard says.

Other examples include virtual assistants such as Apple’s Siri and Amazon’s Alexa, the Jan. 6 insurrection on the U.S. Capitol and social media.

“This statistical world of big data and machine learning — one where correlation supersedes causation — is driven by probable futures ranked by degrees of confidence,” Shepard says.

“It presents an epistemology that eschews expressing facts, representing spaces and developing representative models in favor of evolving models that are in and of themselves territories. Within this context, the notion of ground truth is replaced by that of ground fiction, whereby inference supplants direct observation.”

Shepard adds, “How we navigate and negotiate this epistemic uncertainty and learn to live with the doubt that accompanies it will likely define both the futures we hope to share and those we must prevent.”