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

Alejandro Bellogín, Ludovico Boratto, Styliani Kleanthous, Elisabeth Lex, Francesca Maridina Malloci, Mirko Marras

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Creating efficient and effective search and recommendation algorithms has been the main objective of industry practitioners and academic researchers over the years. However, recent research has shown how these algorithms trained on historical data lead to models that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing, and mitigating these biases throughout experimental pipelines is a primary step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. This workshop aims to collect novel contributions in this field and offer a common ground for interested researchers and practitioners. More information about the workshop is available at https://biasinrecsys.github.io/sigir2024/

Original languageEnglish
Title of host publicationSIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation of Computing Machinery
Pages3033-3035
Number of pages3
ISBN (Electronic)9798400704314
DOIs
Publication statusPublished - 10 Jul 2024
Event47th International ACM SIGIR Conference on Research and Development in Information Retrieval: SIGIR 2024 - Washington, United States
Duration: 14 Jul 202418 Jul 2024

Conference

Conference47th International ACM SIGIR Conference on Research and Development in Information Retrieval
Abbreviated titleSIGIR 2024
Country/TerritoryUnited States
CityWashington
Period14/07/2418/07/24

Keywords

  • algorithms
  • bias
  • fairness
  • recommendation
  • search

ASJC Scopus subject areas

  • Information Systems
  • Software

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