Machine learning for socio-technical systems

Machine Learning (ML) is increasingly seen as a crucial part of the puzzle to solving the socio-technical challenges of today's networked and urbanised knowledge-driven societies. Successful adoption of ML does, however, not only require skilled computer scientists who do hard-core programming. Also, professionals are needed who have both the domain knowledge of socio-technical systems and a profound understanding of ML.

This course aims to provide students in the socio-technical domain with a profound understanding of ML. It prepares students for the challenges and questions ML will pose them in their later careers.

To this end, the course consists of three parts:

1. Fundamentals of ML

2. Explainability of ML

3. Applications of ML for socio-technical challenges

In part 1, students learn about ML fundamentals and methods. These weeks, a critical technical foundation is laid for grasping the strengths and weaknesses of ML for the analysis and design of socio-technical systems.

Part 2 is devoted to the explainability of ML. For public decision-making as well as for decision-making in high-stake contexts, such as, e.g. autonomous vehicles, legal systems’ transparency and explainability of the models are of critical importance. Students learn several popular explainability techniques and discuss their value for applications in socio-technical systems.

In part 3, a group of scholars provides exemplary applications of ML in socio-tech systems. This serves two purposes: (1) to show where and how ML is applied for analysis and application in socio-technical systems and (2) to deepen reflection on the impact of ML-based solutions and interventions on individuals, organisations, and society. Consecutively, students work in a group on a final project, building forth on one of the presented applications. This project brings together the three parts of this course: Fundamentals of ML, Explainability of ML, and application of ML for socio-technical challenges. Students need to apply ML models and techniques to real-world data in a notebook and interpret and communicate their results, taking into account the socio-technical setting through a presentation.

The course consists of oral lectures and lab sessions. The aim of the lab sessions is to show and reinforce how the ML models, explainable ML techniques, and ML ideas presented in the oral lectures are put into practice. Also, they help students gather hands-on machine learning skills. The lab sessions involve a series of exercises in the form of Jupyter notebooks.

The course consists of ~2 oral lectures and 1 lab session per week. Attendance of the lectures and lab sessions is highly recommended to keep up with the course but is not mandatory.