The course's teaching objective is to allow students to gain an understanding and discuss the basic research questions of the emerging field of Critical Algorithm Studies.

The seminar will introduce participants to the emerging field of Critical Algorithm Studies. Blocked weekly discussions of assigned reading material will focus on interdependencies between society, culture and algorithms, and critical reflections of their ethics and politics. The course aims to bridge the gap between bleeding-edge technological advancements and the scientific and social discourse, by introducing perspectives from academic disciplines such as STS, Sociology and Law.

Preliminary list of topics

Introduction to Critical Algorithm Studies
(Re-)production of Inequality through Technology
Computer Science Culture I: Production
Computer Science Culture II: Impacts and Influences
Critical Data Studies
Combating inequality in Tech
Surveillance, Authoritarianism and War
Imaginaries of (near) futures

Open Learning Questions

Why study social and political aspects of algorithmic systems?
What constitutes an algorithm? What do users/developers/society understand about algorithms?
Why do algorithms have embedded values and biases?
How can we conceptualise algorithmic fairness, develop ethics for algorithmic systems and deal with accountability in complex algorithmic assemblages made of developers, users, management, law, code, computers, and many others?
How does culture and society influence the creation of algorithms and vice versa?
How does more algorithmic management foster the erasure of human judgement through increasing rationalisation and automation? What are benefits and issues here?
What methods and approaches are available to study algorithmic systems?
What futures are currently being imagined?

Introductory Materials

Many of the seminar's topics are controversial and highly discussed. We provide the following materials as motivational introduction to some exemplary topics of the course:

"The Trouble with Bias" (Kate Crawford, (formerly NIPS) 2017 Keynote)
"There is a blind spot in AI research" (Kate Crawford& Ryan Calo, Nature)
Do algorithms reveal sexual orientation or just expose our stereotypes? (Blaise Aguera y Arcas, Medium)
Bias und Diskriminierung beim AMS Algorithmus (Cech, 2018 - German only)

The material for this course will include a subset of the reading list of the Social Media Collective. The following blog will contain up-to-date information about the course: https://algorithmstudies.wordpress.com/

Course Modalities

After an initial talk at the beginning of the semester (introduction and preliminary topic assignment), the remaining sessions will be held weekly towards the end of the semester. During each session, one or more participants will present the topic of the week after a short, general introduction by the lecturers. The presenters are required read the papers on the topic, present the content to the other participants and conclusions and prepare a list of discussion points. Each participant has to write a final write-up after the presentations reflecting on the content of the seminar.

Contrary to the listed eligibility for different curricula below, the course is at least eligible for the following modules:

• Module “Emergent Ethical Challenges in Informatics”

• Master Media Informatics [066 935]

• Module “Fachübergreifende Qualifikationen”

• Master Logic and Computation[066 931]

• Master Visual Computing[066 932]

• Master Medical Informatics[066 936]

• Master Software & Information Engineering[066 937]

• Master Computer Engineering[066 938]

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