Machine Learning for Complex Networks

    Data Science, AI, and Society

    Due to the growing digitalisation of all aspects of everyday life, more and more digital traces of human behavior are becoming
    available in the form of Big Data. These data are increasingly used to personalise services, content, and advertisements using
    recommender systems and machine learning techniques, to better understand human and social behavior based on the mining
    and analysis of behavioral data e.g. from social media, or to make algorithmic decisions based on predictions of future human
    behavior. But which challenges do such new technologies bring for our society? How do social media and recommender systems
    affect public opinion? How can we use data analytics to measure and forecast potentially harmful effects? And how can we
    design algorithmic decision-making systems such that they are fair and transparent?

    In this seminar, we explore these questions based on recent research at the intersection of Big Data, Artificial Intelligence,
    and Computational Social Science. Each participant will be assigned one topic from a range of topics, including but not limited
    to those listed below. A successful participation requires an oral presentation about the selected topic, as well a written review
    report compiled for two papers in the respective area. Moreover, each participant is expected to read and comment about the
    review reports provided by other participants.

    List of topics and potential papers: 

    Recommender Systems and Polarisation

    Fairness in Algorithmic Decision-Making

    Human-AI Collaboration

    Big Data and Psychological Profiling

    Network Effects in Online Privacy