Providing efficient and effective search and recommendation algorithms has been traditionally the main objective for the industrial and academic research communities. However, recent studies have shown that optimizing models through these algorithms may reinforce the existing societal biases, especially under certain circumstances (e.g., when historical users’ behavioral data is used for training). Identifying and mitigating data and algorithmic biases thus becomes a crucial aspect, ensuring that these models have a positive impact on the stakeholders involved in the search and recommendation processes. The BIAS 2021 workshop aims to collect novel contributions in this emerging field, providing a common ground for researchers and practitioners.

Second International Workshop on Algorithmic Bias in Search and Recommendation (BIAS@ECIR2021)

Boratto L.;Marras Mirko;
2021

Abstract

Providing efficient and effective search and recommendation algorithms has been traditionally the main objective for the industrial and academic research communities. However, recent studies have shown that optimizing models through these algorithms may reinforce the existing societal biases, especially under certain circumstances (e.g., when historical users’ behavioral data is used for training). Identifying and mitigating data and algorithmic biases thus becomes a crucial aspect, ensuring that these models have a positive impact on the stakeholders involved in the search and recommendation processes. The BIAS 2021 workshop aims to collect novel contributions in this emerging field, providing a common ground for researchers and practitioners.
978-3-030-72239-5
978-3-030-72240-1
Algorithms; Bias; Fairness; Recommendation; Search
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/321865
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