Data, Automation and Social Justice (Autumn 2023)

Abstract

This course enables students to understand the effects of automated digital systems on the way a welfare society is governed and experienced by social groups and individuals.

Description

The main learning objective of this 7,5 elective is for students to understand and purposefully engage with social justice issues that emerge as part of the digital transformation of the welfare state.

The course introduces students to research and cases that focus on automated systems from decision-making systems based on artificial intelligence to systems facing citizens like chatbots and welfare technology such as VR or robots. Analyzing automated systems for welfare purposes is done to better understand their situation of use. Students will get a better understanding of what it means to use/collaborate/be used by automated systems. This leads to insights pertaining to data ethics and social justice as examples of how a broader context for systems development and implementation can impact citizens’ possibilities to act autonomously in a digitalized welfare state.

After finishing the course students will know how to consult and (project)manage digitalization processes in private businesses and public organizations with insight into the complex relations between automation and social justice. Specifically, the course aims at bringing to the fore issues of data ownership, data experience, data work, and data governance. These four themes inform the selection of readings, the lectures, and the workshops. The course is distinct in its focus on building sociological imagination and robust analytical skills in the students as they learn about how they can impact digitalization projects. The motto of the course: Data ethics is not enough when putting in place the infrastructures of future digitalized societies. We also need an understanding of social justice in the context of digitalization.

Formal prerequisites

Intended learning outcomes

After the course, the student should be able to:

• Explain key elements of social justice as explained in the literature.
• Describe differences between automated digital systems based on their effects on citizen autonomy.
• Generate examples of the role of data in contemporary public-private partnerships.
• Analyse consequences of data collection and use by these partnerships for citizens (individuals and groups).
• Discuss and suggest ways in which social justice can become part of technology design for future societies.

Learning activities

Reading, seminar discussions, exercises, presentations, debate.  

Course literature

The course literature is published in the course page in LearnIT.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 30%
• Lectures: 10%
• Assignments: 10%
• Exam with preparation: 10%
• Other: 40%

Ordinary exam

Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C11: Submission of written work

reexam

Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C11: Submission of written work

Time and date

Ordinary Exam - submission Mon, 8 Jan 2024, 08:00 - 14:00
Reexam - submission Wed, 28 Feb 2024, 08:00 - 14:00