Subject of course
The seminar will introduce participants to the emerging and interdisciplinary 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.
Learning outcomes
After successful completion of the course, students are able to identify crucial points of critique and problematic issues in relation to algorithmic systems. They have gained an overview over the topics Inequality, Bias, Fairness, Transparency and Accountability, and gained in-depth understanding of at least one of those topics.
Preliminary list of topics - 2020
- Introduction to Critical Algorithm Studies
- (Future) Imaginaries
- Computer Science Culture
- (Re-)producing & Combating Inequality through Technology
- Critical Data Studies
- Transparency and Accountability of Algorithmic Systems
- The Politics of Algorithms
- Case Study Session
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 involving algorithms 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)
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 seminar paper after the presentations reflecting on the content of the seminar.
Teaching methods
Blocked weekly discussions of assigned reading material will focus on interdependencies between society, culture and algorithms, and critical reflections of their ethics and politics. Questions and contributions to the discussions are supplement the mandatory reading, presentations of selected literature and the final seminar paper.
Mode of examination
Immanent
Additional information
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]