Big Data: Social Processes and Ethical Issues, 7.5 credits

Main field of study

Course level

Advancement level

Course offered for

• Master´s Programme in Computational Social Science

Entry requirements

A bachelor's degree or equivalent in the humanities, social-, cultural-, behavioural-, natural-, computer-, or engineering-sciences.
English corresponding to the level of English in Swedish upper secondary education (English 6/B).

45 ECTS credits completed in Computational Social Science.

Intended learning outcomes

After completion of the course, the student should at an advanced level be able to:

• identify and examine ethical issues involved in the use of large data troves in commercial and non-commercial settings
• analyse the social tensions and mutual benefits arising between those who contribute big data observations, organizations who collect data, and social actors using data to inform decision making
• formulate models of social behaviours that lead to the production of large data archives and engagement with digital platforms that generate these data
• design ethical methodological frameworks for collection, usage, and analysis of data
• critizise the use of digital data in social analysis in terms of bias, external validity, representativeness, and related issues
• analyse case studies exploring contemporary issues of big data collection, use, and analysis

Course content

The course is about ethical issues related to the use of big data. It also considers social processes involving diverse actors who interact with digital platforms, produce and store big data, and analyse those data. Issues related to individual privacy, anonymity, and confidentiality are discussed. Unique ethical concerns that arise from the production of big data are scrutinized, including representativeness, sample selection, non-human and bad faith actors, and the reproduction of social biases through AI and machine learning. Social research and organizational decision-making processes that rely on big data are critiqued on this basis. The course also investigates the motivations, of organizations collecting and making use of big data directly or indirectly for profit and decision-making. These issues are explored through contemporary case studies.

Teaching and working methods

The teaching consists of lectures, readings, and seminars. Homework and independent studies are a necessary complement to the course.

Language of instruction: English.

Examination

The course is examined through written assignments, active participation in seminars, and a written individual final assignment. Detailed information about the examination can be found in the course's study guide.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it. If the coordinator has instead recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Grades

Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

Department