This tutorial aims to provide the ICDM community with a comprehensive survey on data and algorithmic bias in personalized rankings, with a focus on state-of-the-art recommendation algorithms and systems. First, we present conceptual fundamentals on bias based on the existing literature, including a broad discussion on real-world perspectives, ethical aspects, and system's objectives impacted by this phenomenon. Then, we dive deeper into concepts, techniques, metrics, and frameworks that allow to detect, understand, and mitigate bias along the algorithm design pipeline, by presenting the theoretical foundations behind state-of-the-art approaches and their implementations. We showcase hands-on examples of pre-, in-, and post-processing bias mitigation techniques by means of open-source tools and public datasets, engaging tutorial participants in mitigation design and in articulating impacts on stakeholders. Finally, we shed light on how the research in this novel and fast-growing field has opened challenges and opportunities for the ICDM community.
This tutorial is accessible to researchers, industry technologists and practitioners. For people not familiar with rankings, this tutorial covers necessary background material. No prior knowledge on bias is assumed.
One aspect relevant from the outline is that bias is a highly interdisciplinary topic, touching on several dimensions beyond algorithms. Hence, our tutorial is of interest for an interdisciplinary audience attending ICDM, with different backgrounds.
After this tutorial, attendees will be able to understand key aspects of bias in personalized rankings, materialize biases into underlying systems, play with mitigation and articulate impacts on stakeholders, identify challenges and opportunities.
Due to the ongoing worldwide COVID-19 situation, ICDM 2020 will take place online.
|5 mins||Welcome and Presenters' Introduction|
|130 mins||Session I: Foundations|
Algorithmic Bias Foundations
Bias through the Pipeline
|Questions and Discussion|
|30 mins||Coffee Break|
|110 mins||Session II: Hands-on Studies|
Recommender Systems in Practice
Investigation on Item Popularity Bias
Investigation on Item Provider Fairness
|10 mins||Open Issues and Research Challenges|
|15 mins||Questions and Discussion|
Lecture slides, Github repository, and Jupyter notebooks will be made available to attendees before the tutorial.
Ludovico Boratto EURECAT - Centre Tecnòlogic de Catalunya (Spain)
Ludovico Boratto is senior research scientist in at EURECAT. His research focuses on recommender systems and on their impact on stakeholders. His research has been published in top-tier conferences and journals. He is editor of the book "Group Recommender Systems: An Introduction" (Springer). He is editorial board member of the "Information Processing & Management" journal (Elsevier) and guest editor of other special issues. He is regularly PC member of the main Data Mining conferences. In 2012, he got a Ph.D. at the University of Cagliari, where he was research assistant until May 2016.
Mirko Marras University of Cagliari (Italy)
Mirko Marras is a Postdoctoral Researcher at the University of Cagliari (Italy). His research focuses on machine learning for recommender systems, with particular attention to bias issues. He authored papers in top-tier journals, such as Pattern Recognition Letters and Computers Human Behavior. He gave talks and demos at international conferences and workshops, e.g., TheWebConf2018, ECIR2019, and INTERSPEECH2019. He is PC member of major conferences, e.g., ACL, AIED, EDM, ECML-PKDD, EMNLP, ITICSE, ICALT, UMAP. He co-chaired the BIAS2020 workshop at ECIR2020 and gave a tutorial on Bias in Recommender Systems at UMAP2020. In 2020, he received a Doctoral Degree from University of Cagliari.
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