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
- T Nguyen, P-M Hui, F M Harper, L Terveen, J A Konstan: Exploring the filter bubble: the effect of using recommender systems on content diversity, WWW, April 2014
- R Fletcher, RK Nielsen: Are people incidentally exposed to news on social media? A comparative analysis, New media & society, 2018
- D Lazer, O Tsur, T Eliassi-Rad: Understanding offline political systems by mining online political data, ACM International Conference on Web Search and Data Mining. 2016.
- Dandekar, Pranav, Ashish Goel, and David T. Lee. "Biased assimilation, homophily, and the dynamics of polarization." Proceedings of the National Academy of Sciences 110.15 (2013)
Fairness in Algorithmic Decision-Making
- A Singh, T Joachims: Fairness of Exposure in Rankings, KDD 2018
- J Kleinberg, H Lakkaraju, J Leskovec, J Ludwig, S Mullainathan: Human Decisions and Machine Predictions, The Quarterly Journal of Economics, August 2017
- J Kleinberg, S Mullainathan, M Raghavan: Inherent Trade-Offs in the Fair Determination of Risk Scores, arXiv:1609.05807
- Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A. : Algorithmic decision making and the cost of fairness. InProceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining 2017
Human-AI Collaboration
- Bansal et al.: Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff, AAAI Conference on Artificial Intelligence, 2019
- Okamura and Yamada: Adaptive trust calibration for human-AI collaboration, PLOS ONE, 2020
- Patel et al: Human–machine partnership with artificial intelligence for chest radiograph diagnosis, NPJ Digital Medicine, 2019
- Ashktorab Z, Liao QV, Dugan C, Johnson J, Pan Q, Zhang W, Kumaravel S, Campbell M. : Human-AI collaboration in a cooperative game setting: measuring social perception and outcomes. Proceedings of the ACM on Human-Computer Interaction. 2020
Big Data and Psychological Profiling
- G Chittaranjan, J Blom, D Gatica-Perez: Mining large-scale smartphone data for personality studies, Personal and Ubiquitous Computing, 2013
- M Kosinski, D Stillwell, T Graepl: Private traits and attributes are predictable from digital records of human behavior, PNAS, 2013
- Y Wang, M Kosinski: Deep neural networks are more accurate than humans at detecting sexual orientation from facial images, psyarxiv/hv28a, 2018
- Stachl C, Au Q, Schoedel R, Gosling SD, Harari GM, Buschek D, Völkel ST, Schuwerk T, Oldemeier M, Ullmann T, Hussmann H. Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences. 2020
Network Effects in Online Privacy
- D Garcia: Leaking privacy and shadow profiles in online social networks, Science Advances, 2017
- Such JM, Criado N. : Multiparty privacy in social media. Communications of the ACM. 2018
- E Sarigol, D Garcia, F Schweitzer: Online privacy as a collective phenomenon, COSN, 2014
- G Cormode: Individual Privacy vs Population Privacy: Learning to Attack Anonymization, arXiv:1011.2511, 2011