Data Literacy for Algorithmic Decision Support

Course type: Lecture and exercise
Institution: Faculty of Business Administration
Funding period: 01.04.2023 to 31.03.2024

Extract from the funding application: "Digitalisation is triggering comprehensive change processes in almost all branches of industry and is having a profound impact on human work. A key aspect of this change is the increasing use of algorithmic procedures in operational decision-making processes that were originally reserved for human decision-makers. This inevitably requires a new form of division of labour in which planning and control activities are increasingly carried out with machine support."

Orientation of the project

In this DLE project, the students examined the application of algorithmic processes for operational decision-making. Using the fictitious ridesharing provider "MichelSprinter", an experimental setting was designed that offered the opportunity to understand and discuss these decision-making processes realistically and also in connection with the stakeholders. Algorithmic processes for operational decision-making are revolutionising the way in which companies make strategic and operational decisions.

By using algorithms, huge amounts of data can be analysed in a short space of time and patterns identified that would be difficult for human decision-makers to access on their own. Machine learning and artificial intelligence in particular play a central role here. Nevertheless, it is essential to consider the ethical and social implications of these technologies to ensure that they are used responsibly. Overall, algorithmic processes offer enormous potential, which was explored in an exciting experiment in this project.

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Review and results

In the first phase of the course, the students create a database for their fictitious setting with the help of a questionnaire that is developed in a preceding seminar. This data is statistically analysed in the second phase with the help of R. The necessary programming concepts are taught in a low-threshold manner with explanatory videos and programming tasks. The analyses are then implemented in presence in supervised tutorials with Jupyter notebooks. In the final phase of the course, the results obtained are utilised with Python for algorithmic decision-making for the "MichelSprinter".

In addition to the technical processes, the interaction between human decision-makers and machine processes, the consideration of the preferences of the company's stakeholders and the possible effects on society and the environment are also important here. Overall, students learn about processes and challenges for the application of algorithmic procedures in the context of operational decision-making and learn to assess the strengths and weaknesses of algorithmic decision-making procedures.

This DLE course is open to all Master's students of the Faculty of Business Administration. It is to be permanently included in the curriculum of the business administration methods programme.