Work

International Workshop on Algorithmic Bias in Search and Recommendation (BIAS 2025)

Year: 2025

Type: conference-paper

Abstract: Designing search and recommendation models that are both efficient and effective has long been a central objective for both industry professionals and academic researchers. Yet, growing evidence highlights how models trained on historical data can reinforce pre-existing biases, potentially leading to harmful outcomes. Addressing these challenges by defining, evaluating, and mitigating bias across development workflows is a crucial step toward the responsible deployment of search and recommendation models in practice. The BIAS 2025 workshop seeks to gather innovative research and foster a shared space for dialogue among researchers and practitioners committed to advancing this fundamental direction. Workshop website: https://biasinrecsys.github.io/sigir2025/.

Source: International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

DOI: 10.1145/3726302.3730357

Authors: Alejandro Bellogin, Ludovico Boratto, Styliani Kleanthous, Elisabeth Lex, Francesca Maridina Malloci, Mirko Marras

Institutions: Universidad Autónoma de Madrid, University of Cagliari, Open University of Cyprus, Graz University of Technology

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: bronze

Funders: European Commission, NextGenerationEU