Creating search and recommendation algorithms that are efficient and effective has been the main goal for the industry and the academia for years. However, recent research has shown that these algorithms lead to models, trained on historical data, that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing and mitigating these biases throughout experimental pipelines is hence a core step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. The Bias 2022 workshop aims to collect novel contributions in this field and offer a common ground for interested researchers and practitioners. The workshop website is available at https://biasinrecsys.github.io/ecir2022/.

Third International Workshop on Algorithmic Bias in Search and Recommendation (BIAS@ECIR2022)

Boratto L.;Marras M.;
2022-01-01

Abstract

Creating search and recommendation algorithms that are efficient and effective has been the main goal for the industry and the academia for years. However, recent research has shown that these algorithms lead to models, trained on historical data, that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing and mitigating these biases throughout experimental pipelines is hence a core step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. The Bias 2022 workshop aims to collect novel contributions in this field and offer a common ground for interested researchers and practitioners. The workshop website is available at https://biasinrecsys.github.io/ecir2022/.
978-3-030-99738-0
978-3-030-99739-7
Algorithms; Bias; Fairness; Recommendation; Search
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/348266
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