Seminar Data-Science II (Empirical Studies)

IS 723 for Master students (M.Sc. MMM, M.Sc. WiPäd, MMDS)

LecturerProf. Dr. Markus Strohmaier, Tobias Schumacher, Marlene Lutz
Course FormatSeminar
Credit Points6 ECTS
GradingWritten report (50%), oral presentation (40%) and discussion (10%)
Examination dateSee schedule below
Information for Students

The course is limited to 15 participants. The registration process is explained below.


For administrative questions, please contact

Tobias Schumacher, M.Sc.

Tobias Schumacher, M.Sc.

Wissenschaft­licher Mitarbeiter am Lehr­stuhl für Data Science in den Wirtschafts- und Sozial­wissenschaften
Universität Mannheim
L 15, 1–6
3. OG – Raum 323
68161 Mannheim

Course Information

  • Course Description

    The achievement of the learning goals is pursued by practicing on the basis of personally assigned in-depth scientific topics as well as by actively participating in the presentation dates. The organizer will choose subject areas within the field of Data-Science (see Topics) and provide scientific papers to students to work through.

    Previous participation in the courses offered by our chair are recommended.

  • Topics

    This seminar will be split into two main topic blocks. Every student will be assigned a research paper from only one of these blocks to work on. Yet, it is expected that students also actively participate in discussion on papers from the other topic blocks after they have been presented.

    When applying for this seminar, please indicate whether you would be interested in only one or both topic blocks. The exact research articles that are to be worked on will be determined once we have an overview over the number of participants and their rough preferences.

    The two topics we discuss this semester are the following:

    1. Explainability in AI decision-making. Explainability is a crucial aspect of AI systems and algorithmic decision making as it fosters trust, accountability, and transparency. By providing transparency into how AI systems arrive at conclusions or recommendations, explainability promotes trust and effective collaboration between humans and AI. Users can understand the rationale behind algorithmic decisions, allowing them to validate and augment the system's outputs with their expertise and enabling them to make informed decisions in high-stakes scenarios. We will examine the impact of various types of explanations on user acceptance and trust in AI systems.
    2. Advertising on Social Media in Political Contexts. Social networks have emerged as a very common platform to discuss political topics with other people. While this may foster a sense of community, the algorithms that operate social media may also harm political discourse and enable polarization. In that context, political actors may also use ads on these platforms to move discussions, and, in consequence, the users into their desired direction. Therefore, we will investigate state-of the art research papers that empirically investigate how online advertising has been used in political contexts on social media.
  • Objectives

    On the basis of suitable literature, in particular original scientific articles, students independently familiarize themselves with a topic in data-science, classify and narrow down the topic appropriately and develop a critical evaluation. Students work out concepts, procedures and results of a given topic clearly and with appropriate formalisms in a timely manner and to a defined extent in depth in writing; Evidence of independent development by presenting self-selected examples. Descriptive oral presentation of an in-depth data science topic using suitable media and examples in a given format.

  • Schedule

    Registration period

    until 04.09.23 (11.59 PM) see „Registration“
    Notification of acceptance/rejection06.09.23  
    Drop-out until07.09.23 
    Kick-off meeting

    13.09.23, 14:45   

    general information

    16.10.23 (tentative)

    midterm presentations

    01.12.23 (tentative)

    endterm presentations
    Submission deadline08.12.23 (11.59 PM) 
  • Registration

    If you are interested in this seminar, please apply to Tobias Schumacher via email. 

    Please start the Subject Line with “[SemDSII]”, and provide some details about your background, e.g., whether you have taken some relevant classes before (cf. course description) and your motivation to take this seminar. Also, make sure to indicate which of the two given topics – Explainability in AI Decision-Making and Advertising on Social Media in Political Contexts – you are  interested in.