Teaching

In the spring semester, we teach the Social Computing course which is recommended for students in the 4th term of the BSc.

In the fall semester 2024, we are offering the Algorithmic Auditing Seminar for the first time; see Course link (MSc) and Course link (BSc).

Use these links to access the OLAT courses: OLAT course MSc & OLAT course BSc

Content

Algorithms increasingly govern what we see and our access to opportunities. In this seminar we will explore this topic and learn ways to understand if specific systems are functioning fairly within society. You will learn how to audit societal facing algorithms with experimental approaches, then using statistics draw scientific conclusions.

Learning Outcome

This course will provide students with an understanding of and how to conduct algorithm auditing. This includes knowledge of digital experiment planning, conducting experiments, web scraping and digital system measurement, and statistical decision making of experimental outcomes.

Prior Knowledge

Programming experience (language agnostic), web scraping experience is helpful, knowledge of the scientific process is also helpful, a background in foundational statistics will be helpful.

Use this link to access the OLAT course.

Content

Social computing studies the intersection of social behavior and computing systems. This intersection is not static: social behavior affects how computing systems are designed and computing systems affect how humans interact. Traditionally, social computing was more concerned with the first direction, how social norms and individual behavior affect the growth, success, and usage of online platforms. In recent years, however, it is increasingly recognized that online platforms affect how people perceive the world, interact with each other, and ultimately how societal norms develop. Today, online platforms affect people’s daily lives in essentially all domains: healthcare, the labor market, social and family lives, education, information access, etc, and thus have a growing social, economic, and political impact.

Regulatory action tries to react to changes in technology (GDPR, online discrimination law, competition law, etc) but as argued in this course, still much power and social responsibility remains in the hands of the online platform provider.

Learning Outcomes

Studying the ecosystem of online platforms and their users poses a multitude of challenges, given their complexity and how quickly they evolve. Understanding them requires a combination of technical skills (platform design, digital trace data collection, software tools), social science knowledge (from political science, social science, economics, and law), and research design knowledge.

Throughout the semester we will discuss the evolution of online platforms and smartphone apps, their use, structure, and purpose in society. We will learn about the underlying data and algorithmic eco- systems. The students will learn to scrape digital trace data and use this data to answer societally relevant research questions.

By the end of the course, students will be able to a) summarize the most important papers addressing societal problems from different domains related to online platforms, b) design an empirical research project bringing together research questions and technical/societal background, and c) identify and critically discuss ethical and bias-related issues with their research design.

Prerequisites

Basics of computer science, basic programming skills (python), and basic statistics.