Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer, Hito Steyerl: Pattern Discrimination (2018)

14 November 2018, dusan

“Algorithmic identity politics reinstate old forms of social segregation—in a digital world, identity politics is pattern discrimination. It is by recognizing patterns in input data that Artificial Intelligence algorithms create bias and practice racial exclusions thereby inscribing power relations into media. How can we filter information out of data without reinserting racist, sexist, and classist beliefs?”

Publisher meson press, Lüneburg, in collaboration with the University of Minnesota Press, 2018
In Search of Media series
CC-BY-NC 4.0 International License
ISBN 9781517906450
xii+123 pages

Publisher
WorldCat

PDF, PDF

Mario Hibert: Digitalni odrast i postdigitalna dobra: kritičko bibliotekarstvo, disruptivni mediji i taktičko obrazovanje (2018) [Croatian]

20 October 2018, dusan

“Knjiga tematizira ideje kritičkog bibliotekarstva, specifičan oblik profesionalne kulture zasnovan na konceptu društvene odgovornosti. Propitivanjem kredibiliteta javne misije bibliotekarstva u umreženom društvu podcrtana je važnost kritičke medijske pismenosti posebice kritičkih studija Interneta. Poseban akcenat je stavljen na aspekte informacijske i komunikacijske komodifikacije, artikulaciju epistemoloških i političkih prijepora podatkovnog društva (datafied society) kao i upravljanje digitalnim zajedničkim dobrom koje u svjetlu teorije odrasta (degrowth) bibliotekarstvu nudi konstruktivni imaginarij za socijalnu reorganizaciju tehnologije. ”

Publisher Multimedia Institute & Institut za političku ekologiju, Zagreb, September 2018
Basic series
Anti-copyright
ISBN 9789537372484, 9789535893868
152 pages

PDF (updated on 2018-12-10 to a newer version with minor changes)

Adrian Mackenzie: Machine Learners: Archaeology of a Data Practice (2017)

22 June 2018, dusan

“If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought?

Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking.

Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures.

Mackenzie’s account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.”

Publisher MIT Press, November 2017
ISBN 9780262036825, 0262036827
272 pages
via A.B.

Publisher
WorldCat

PDF (removed on 2018-8-20 upon request from publisher)
Draft and code samples on GIT