Pattern Discrimination

Clemens Apprich
Fine Arts and Media Art, Media Theory
2021W, scientific seminar (SEW), 4.0 ECTS, 2.0 semester hours, course number S03778

Description

How do ‘human’ prejudices re-emerge in algorithmic cultures allegedly devised to be blind to them? To answer this question, this seminar investigates a fundamental axiom in data analytics and machine learning: pattern discrimination. By imposing identity on input data, in order to filter – that is, to discriminate – signals from noise, patterns become a highly political issue. Hence, in this course, we will look into the politics of pattern discrimination and discuss its relation to recent developments in digital cultures. We will engage with algorithmic filtering and machine learning which reinstate old forms of social segregation, such as class, race, and gender, and develop a critical vocabulary to better understand those issues.

In this course, we will discuss key issues related to datafication and machine learning, including (among others): the history of pattern discrimination, data politics and dataveillance, as well as filter algorithms and recommendation systems. Drawing on the work of contemporary thinkers as well as well as everyday examples, the course aims to critically engage with the politics of data analytics, the consequences of algorithmic filtering, as well as activist and artistic practices dealing with these issues.

Examination Modalities

Active Participation (20%): The class activity will centre around close interactive reading of texts and debates around the specific arguments forwarded in them. In order to facilitate productive in-class discussions, you are asked to read and collaboratively annotate the texts beforehand, using the open-source annotation tool Hypothesis.

Research Paper/Book Review (80%): Students will be responsible for a final research paper/book review due at the end of the term. The research paper should be similar in scope and format to a scholarly conference paper (ca. 20.000 characters, including spaces and footnotes, but excluding appendices, references, or figures) based on at least three references from the seminar literature as well as at least three external references (you can, of course, also use more references). Alternatively, students can write a book review, which has the same format as the research paper (see above) but focuses on one (or two) recent publications dealing with the seminar's topic (a list of books will be provided).

A course outline with the teaching material will be made available online.

Comments

The seminar will be held in English. Papers/reviews can be submitted in English or German.

An OA publication on the topic can be found here

Key Words

pattern discrimination, data bias, algorithmic filtering, machine learning, digital cultures, activist and artistic practices

Dates

06 October 2021, 15:00–16:30 Seminar Room 8
13 October 2021, 15:00–16:30 Seminar Room 8
20 October 2021, 15:00–16:30 Seminar Room 8
27 October 2021, 15:00–16:30 Seminar Room 8
03 November 2021, 15:00–16:30 Seminar Room 8
10 November 2021, 15:00–16:30 Seminar Room 8
17 November 2021, 15:00–16:30 Seminar Room 8
24 November 2021, 15:00–16:30 Seminar Room 8
01 December 2021, 15:00–16:30 Seminar Room 8
15 December 2021, 15:00–16:30 Seminar Room 8
12 January 2022, 15:00–16:30 Seminar Room 8
19 January 2022, 15:00–16:30 Seminar Room 8
26 January 2022, 15:00–16:30 Seminar Room 8
02 February 2022, 15:00–16:30 Seminar Room 8

Course Enrolment

Via online registration

co-registration: possible

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