Both search and recommendation algorithms provide results based on their relevance for the current user. In order to do so, such a relevance is usually computed by models trained on historical data, which is biased in most cases. Hence, the results produced by these algorithms naturally propagate, and frequently reinforce, biases hidden in the data, consequently strengthening inequalities. Being able to measure, characterize, and mitigate these biases while keeping high effectiveness is a topic of central interest for the information retrieval community. In this workshop, we aim to collect novel contributions in this emerging field and to provide a common ground for interested researchers and practitioners.

International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020)

Stilo, Giovanni
2020

Abstract

Both search and recommendation algorithms provide results based on their relevance for the current user. In order to do so, such a relevance is usually computed by models trained on historical data, which is biased in most cases. Hence, the results produced by these algorithms naturally propagate, and frequently reinforce, biases hidden in the data, consequently strengthening inequalities. Being able to measure, characterize, and mitigate these biases while keeping high effectiveness is a topic of central interest for the information retrieval community. In this workshop, we aim to collect novel contributions in this emerging field and to provide a common ground for interested researchers and practitioners.
978-3-030-45442-5
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/160380
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