Data and Society

The course is given as a freestanding course and as an optional course in the Human-centered Artificial Intelligence Master's Programme.

To be eligible for the course the student must have a Bachelor's degree (180 credits).

Applicants must prove their knowledge of English: English 6/English B from Swedish Upper Secondary School or the equivalent level of an internationally recognized test, for example TOEFL, IELTS, or alternatively a bachelor's degree from an education held in English.

The course introduces students to the social, political, and ethical aspects of data science work. It aims to create reflective practitioners who can think critically about how collecting, aggregating, and analyzing data are sociotechnical processes that affect people. Students are also encouraged to consider how they view their responsibilities to those who produce the data that data scientists collect and analyze. The course is organized into three modules, each with lectures and student-led seminars. Students are expected to participate in class discussions, complete homework assignments and to do a final small group work.

Course Modules

1) Theoretical: this module will teach theoretical concepts from Science and Technology Studies to reflect critically on the constructed nature of data and the sociotechnical context in which data is generated. It will also examine how the increasing quantification of human life promoted by digital technologies shapes society.

2) Methodological: this module will present tools for collecting, cleaning, analyzing, and visualizing data.

3) Practical: this module will involve group work focusing on small case studies on subjects relevant to the course. During the group work, the students will examine a specific example of data inequality and develop an idea for an intervention to raise awareness about the data inequality discussed in the chosen article or report. The results of the group work will be presented in an oral presentation and a final short written report.

On successful completion of the course the student will be able to:

Knowledge and understanding

• critically reflect on key concepts and perspectives related to the constructed nature of data and the role of digital data platforms, such as social media and data infrastructures;
• describe how circulation and use of data are shaped by social and material factors, including social institutions and technologies;
• describe the social and cultural consequences of how data is constructed, particularly with regard to gender, class, and ethnicity;

Competence and skills

• select and use relevant digital tools to engage in the analysis and interpretation of a dataset leading to proposed hypotheses of root causes that can inform improvement actions;
• present results orally and visually in a professional manner;
• discuss the potential and constraints of using data critically and constructively;

Judgement and approach

• understand the potential and constraints of using data for public discourse and social intervention;
• use data and evidence to understand the needs of communities to develop and implement interventions that address their issues;
• critically evaluate possible data biases for the development of Artificial Intelligence.

Several co-lecturers will teach the course. The course will be offered in person unless circumstances require otherwise. Student attendance is expected.

The course will consist primarily of lectures, student-led seminars, and group work. Active participation in group work preparation and oral presentation is compulsory. Active participation in student-led seminars to discuss readings is also compulsory. At the end of the course, students will also write short individual reflective essays, which should include reflections on the group work and their learning experience in the course.

Language of instruction: English

Assessment and grading in the course are based on three compulsory parts:

a) active participation in four compulsory student-led seminars, 3 credits. Grading scale: Pass (G) and Fail (U). In case of absence from more than one seminar, a student must do a compensatory assignment determined in consultation with the course leader.

b) individual reflective essay, 1,5 credits. Grading scale: Pass with Distinction (VG), Pass (G) and Fail (U).

c) the content of group work and conduction of the oral presentation of group work, 3 credits. Grading scale: Pass (G) and Fail (U). If a student is prevented from taking part in the group work for a legitimate reason of absence (e.g., medical condition, caretaking responsibilities), the examiner may decide to provide an alternative form of examination. If a student fails the alternative examination form, s/he can redo it at the next course occasion.

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University of Gothenburg with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed. The same applies to placement and internship (VFU) except that this is restricted to only one further examination session.

If a student has been notified that they fulfil the requirements for being a student at Riksidrottsuniversitetet (RIU student), to combine elite sports activities with studies, the examiner is entitled to decide on adaptation of examinations if this is done in accordance with the Local Rules Regarding RIU Students at the University of Gothenburg.

The grading scale comprises: Pass with Distinction (VG), Pass (G) and Fail (U).

The assessment consists of multiple assignments. Each assignment contributes to the course grade. Detailed assessment criteria for each assignment will be available to students in Canvas.

To get the grade Pass (G) for the entire course, the student must receive the grade Pass on all three assignments. To get the grade Pass with Distinction (VG) for the entire course, the student must receive the grade Pass on both the group work and the participation in student-led seminars and get the grade Pass with Distinction in the individual reflective essay.

An anonymous course evaluation will be conducted at the end of the course. The results of and possible changes to the course will be shared with students who participated in the evaluation and students who are starting the course.

The course meets the following sustainability criterion confirmed by the University of Gothenburg:

5. Human rights and social equity

Distribution, discrimination, health and poverty issues and the mutual interaction between social inequality, poor health, the natural environment and people's opportunities for good living conditions.