Second Lecture – “What is an Algorithm?” and “Embedded Values and Biases of Algorithms”

The seminar will feature six lectures. We have decided to also post the slides of the presentations online and also a very small excpert from the discussions. There will also be a final submission by every participant, which will be posted here. This lecture has dealt mainly with two topics:

  • What is an Algorithm?

First we started with a short presentation: Critical Algorithm Studies – Introduction

Then we discussed and reflected upon different definitions of Algorithm. Technological definitions focus on tool-perspective and socio-technical definitions acknowledge people using, developing, thinking about, being influenced by Algorithms.  Usually Algorithms are obfuscated and almost mystical, which is maybe not clear to many computer scientists. We came to the conclusion that we do not really need an all encompassing definition of Algorithm.

  • Embedded Values and Biases of Algorithms

Again a short presentation was given: Embedded Values and Biases

The first part of the discussion was focused on responsibility. Then we talked about good practises and how to reduce “bias”. We came to the conclusion that models inherently have bias, since they can not completly represent reality and are done by people with a goal in mind. The goal should be to be aware of biases and then to ethically “balance” them out. The paper on facial recognition showed that the researchers were not aware of the bias in data and only indipendent tests outside the lab showed them the problematic results, which in turn means participatory approaches need to be considered in algorithm design.

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First Lecture – Introduction

Today the first lecture of the seminar series at TU Wien in “Critical Algorithm Studies” was held. It features biweekly discussions of assigned reading material which focus on interdependencies between society, culture and algorithms, and critical reflections of their ethics and politics.

The slides for the first lecture: First Lecture
The reading list for the semiar: Readings

List of topics:

  • Field Survey, What is an Algorithm?
  • Embedded Values and Biases
  • Erasure of human judgement through rationalization and automation
  • Algorithmic Culture
  • Ethics, Accountability and Algorithms
  • Surveillance, Privacy and Data
  • Methods and approaches for studying algorithmic systems
  • What do users understand about algorithms, Futures

During each session one or more participants should present the topic of the week. They should have read all the papers, tell the others about their content and conclusions and prepare a list of discussion points. Sessions will be held approximately every two weeks. After several sessions, participants should decide on a topic for their final assignment. The submission can be written either alone or in a group and can be either a short article or in a different format. Grading will be based on the presentation of the papers, participation in the discussion and the final assignment.