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Advances in Bias and Fairness in Information Retrieval

Third International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Ludovico Boratto

    1. University of Cagliari, Cagliari, Italy

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  2. Stefano Faralli

    1. Sapienza University of Rome, Rome, Italy

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  3. Mirko Marras

    1. University of Cagliari, Cagliari, Italy

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  4. Giovanni Stilo

    1. University of L’Aquila, L’Aquila, Italy

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 1610)

Included in the following conference series:

Conference proceedings info: BIAS 2022.

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Table of contents (13 papers)

  1. Front Matter

    Pages i-x
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  2. Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

    • Dominik Kowald, Emanuel Lacic
    Pages 1-11

  3. The Impact of Recommender System and Users’ Behaviour on Choices’ Distribution and Quality

    • Naieme Hazrati, Francesco Ricci
    Pages 12-20

  4. Sequential Nature of Recommender Systems Disrupts the Evaluation Process

    • Ali Shirali
    Pages 21-34

  5. Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures

    • Julia Neidhardt, Mete Sertkan
    Pages 35-42

  6. A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches

    • Binh Le, Damiano Spina, Falk Scholer, Hui Chia
    Pages 43-55

  7. The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

    • Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, Mohammadmehdi Naghiaei
    Pages 56-68

  8. The Unfairness of Popularity Bias in Book Recommendation

    • Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mahdi Dehghan
    Pages 69-81

  9. Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

    • Anastasiia Klimashevskaia, Mehdi Elahi, Dietmar Jannach, Christoph Trattner, Lars Skjærven
    Pages 82-90

  10. Analysis of Biases in Calibrated Recommendations

    • Carlos Rojas, David Contreras, Maria Salamó
    Pages 91-103

  11. Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?

    • Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz
    Pages 104-116

  12. Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach

    • Giordano d’Aloisio, Giovanni Stilo, Antinisca Di Marco, Andrea D’Angelo
    Pages 117-129

  13. Keyword Recommendation for Fair Search

    • Harshit Mishra, Sucheta Soundarajan
    Pages 130-142

  14. FARGO: A Fair, Context-AwaRe, Group RecOmmender System

    • Davide Azzalini, Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca
    Pages 143-154

  15. Back Matter

    Pages 155-155
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  1. Advances in Bias and Fairness in Information Retrieval

Keywords

About this book

This book constitutes refereed proceedings of the Third International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2022, held in April, 2022. 

The 9 full papers and 4 short papers were carefully reviewed and selected from 34 submissions. The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact of gender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web. 

Editors and Affiliations

  • University of Cagliari, Cagliari, Italy

    Ludovico Boratto, Mirko Marras

  • Sapienza University of Rome, Rome, Italy

    Stefano Faralli

  • University of L’Aquila, L’Aquila, Italy

    Giovanni Stilo

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