International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020)
Workshop at the 42nd European Conference on Information Retrieval (ECIR 2020) on April 14, 2020
Organisers
Prof. Giovanni Stilo
Search and recommendation are getting closer and closer as research areas. Though they require fundamentally different inputs, i.e., the user is asked to provide a query in search, while implicit and explicit feedback is leveraged in recommendation, existing search algorithms are being personalized based on users’ profiles and recommender systems are optimizing their output on the ranking quality.
Both classes of algorithms aim to learn patterns from historical data that conveys biases in terms of imbalances and inequalities. These hidden biases are unfortunately captured in the learned patterns, and often emphasized in the results these algorithms provide to users. When a bias affects a sensitive attribute of a user, such as their gender or religion, the inequalities that are reinforced by search and recommendation algorithms even lead to severe societal consequences, like users discrimination.
For this critical reason, being able to detect, measure, characterize, and mitigate these biases while keeping high effectiveness is a prominent and timely topic for the IR community. Mitigating the effects generated by popularity bias, ensuring results that are fair with respect to the users, and being able to interpret why a model provides a given recommendation or search result are examples of challenges that may be important in real-world applications. This workshop aims to collect new contributions in this emerging field and to provide a common ground for interested researchers and practitioners. More info at workshop website