Recommender Systems and Misinformation: The Problem or the Solution?

Recommender Systems have been pointed as one of the major culprits of misinformation spreading in the digital sphere. These systems have recently gone under heavy criticism for promoting the creation of filter bubbles, lowering the diversity of information users are exposed to and the social contacts they create. This influences the dynamics of social news sharing, and particularly the ways misinformation initiates and propagates. However, while Recommender Systems have been accused of fuelling the spread of misinformation, it is still unclear which particular types of recommender algorithms are more prone to recommend misinforming news, and if, and how, existing recommendation algorithms and evaluation metrics, can be modified or adapted to mitigate the misinformation spreading effect. In this position paper, we describe some of the key challenges behind assessing and measuring the effect of existing recommendation algorithms on the recommendation of misinforming articles and how such algorithms could be adapted, modified, and evaluated to counter this effect based on existing social science and psychology research

Hands on Data and Algorithmic Bias in Recommender Systems

This tutorial provides a common ground for both researchers and practitioners interested in data and algorithmic bias in recommender systems. Guided by real-world examples in various domains, we introduce problem space and concepts underlying bias investigation in recommendation. Then, we practically show two use cases, addressing biases that lead to disparate exposure of items based on their popularity and to systematically discriminate against a legally-protected class of users. Finally, we cover a range of techniques for evaluating and mitigating the impact of these biases on the recommended lists, including pre-, in-, and post-processing procedures. This tutorial is accompanied by Jupyter notebooks putting into practice core concepts in data from real-world platforms