This talk interrogates the history of models of decision making and agency in machine learning, neo-liberal economic thought, and finance in order to interrogate how reactionary politics, population and sex, and technology are being reformulated in our present. While the relationship between the Right, post-truth, suggestion algorithms, and social media has long been documented, rarely has there been extensive investigation of how ideas of choice and freedom become recast in a manner amenable to machine automation and to the particular brands of post-1970s alt-Right discourses. An analysis of this history demonstrates a new logic within algorithmic and artificial intelligent rationalities that intersects with, but is also not merely a recursive repetition of, earlier histories of eugenics and racism. This situation provokes serious challenges to political action, but also to our theorization of histories of race and sex capitalism.
Orit Halpern is Full Professor and Chair of Digital Cultures and Societal Change at Technische Universität Dresden. Her work bridges the histories of science, computing, and cybernetics with design. She completed her Ph.D. at Harvard. She has held numerous visiting scholar positions including at the Max Planck Institute for the History of Science in Berlin, IKKM Weimar, and at Duke University. She is currently working on two projects. The first is a history of automation, intelligence, and freedom; the second project examines extreme infrastructures and the history of experimentation at planetary scales in design, science, and engineering.
She has also published widely in many venues including Critical Inquiry, Grey Room, and Journal of Visual Culture, and E-Flux. Her first book Beautiful Data: A History of Vision and Reason (2015) investigates histories of big data, design, and governmentality. Her second book The Smartness Mandate (with Robert Mitchell, 2023) is a genealogy of the current obsession with smart technologies and artificial intelligence.
In English
Organized by
ICI Berlin
Models
Lecture Series 2022-23
A model can be an object of admiration, a miniature or a prototype, an abstracted phenomenon or applied theory, a literary text — practically anything from a human body on a catwalk to a mathematical description of a system. It can elicit desire, provide understanding, guide action or thought. Despite the polysemy of the term, models across disciplines and fields share a fundamental characteristic: their effect depends on a specific relational quality. A model is always a model of or for something else, and the relation is reductive insofar as it is selective and considers only certain aspects of both object and model.
Critical discussions of models often revolve around their restrictive function. And yet models are less prescriptive and more ambiguous than codified rules or norms. What is the critical purchase of models and how does their generative potential relate to their constitutive reduction? What are the stakes in decreasing or increasing, altering or proliferating the reductiveness of models? How can one work with and on models in a creative, productive manner without disavowing power asymmetries and their exclusionary or limiting effects?
