Social Aspects of Big Data 2700-M-ZBD-D2SABD
The course focuses on the analysis of Big Data and machine learning as socio-technical phenomena that increasingly affect the functioning of organizations, decision-making processes and relations between individuals, business and public institutions. Big Data and analytical algorithms are not treated only as technical tools, but as elements that co-shape the contemporary social and economic reality.
The classes are carried out in the form of a seminar with workshop elements. The theoretical part includes an introduction to the concepts of Big Data and machine learning, a discussion of the sources and nature of data used in organizations, and a presentation of the role of algorithms as tools supporting and automating managerial decisions. Particular emphasis is placed on the issues of data quality, bias, organizational responsibility, privacy and information security related to data processing.
The workshop part is devoted to the analysis of real and hypothetical use cases of Big Data in business. Students learn to identify potential social, ethical, and organizational risks to data-driven projects and critically evaluate the effects of decisions made based on algorithms. The classes are aimed at developing the competencies necessary for conscious and responsible participation in data-driven management processes.
Course coordinators
Learning outcomes
Knowledge:
• knows the basic concepts related to Big Data and machine learning,
• understands the role of data and algorithms in the decision-making processes of the organization,
• knows the social, ethical and legal determinants of the use of data analytics in business and public institutions.
Skills:
• is able to analyze examples of Big Data applications in management,
• is able to identify social, ethical and organizational risks of data-driven projects,
• is able to critically evaluate decisions made on the basis of algorithms and analytical models.
Social competences:
• is ready to participate responsibly in data-driven projects,
• shows sensitivity to ethical and social issues related to data processing and decision automation.
Assessment criteria
The basis for the assessment of the achieved learning outcomes are:
• student activity in classes, including participation in discussions and workshop exercises,
• A final single-choice test to check the knowledge of the theoretical and problem issues discussed.
The condition for passing the course is to obtain a positive grade from the final test and active participation in classes.
Bibliography
• Viktor Mayer-Schönberger, Kenneth Cukier, Big Data. A revolution that will change the way we think, work and live
• Christian Fuchs, Social Media: A Critical Introduction
• Danah Boyd, Kate Crawford, Critical Questions for Big Data
• Zuboff, Shoshana, The Age of Surveillance Capitalism