FairUMAP 2019 Chairs' Welcome Overview


Bettina Berendt, Veronika Bogina, Robin Burke, Michael D. Ekstrand, Alan Hartman, Stylani Kleanthous, Tsvi Kuflik, Bamshad Mobasher, and Janna Otterbacher. 2019. FairUMAP 2019 Chairs' Welcome Overview. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization (UMAP '19). ACM. DOI 10.1145/3314183.3323842.


It is our great pleasure to welcome you to the Second FairUMAP workshop at UMAP 2019. This full-day workshop brings together researchers working at the intersection of user modeling, adaptation, and personalization on one hand, and bias, fairness and transparency in algorithmic systems on the other hand. The workshop was motivated by the observation that these two fields increasingly impact one another. Personalization has become a ubiquitous and essential part of systems that help users find relevant information in today’s highly complex, information-rich online environments. Machine learning techniques applied to big data, as done by recommender systems, and user modeling in general, are key enabling technologies that allow intelligent systems to learn from users and adapt their output to users’ needs and preferences. However, there has been a growing recognition that these underlying technologies raise novel ethical, legal, and policy challenges. It has become apparent that a single-minded focus on user characteristics has obscured other important and beneficial outcomes such systems must be able to deliver. System properties such as fairness, transparency, balance, and other social welfare considerations are not captured by typical metrics based on which data-driven personalized models are optimized. Indeed, widely-used personalization systems in popular sites such as Facebook, Google News and YouTube have been heavily criticized for personalizing information delivery too heavily at the cost of these other objectives.


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