Advances in Bias and Fairness in Information Retrieval

1. Front Matter

   Pages i-x

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2. Towards Fairness-Aware Ranking by Defining Latent Groups Using Inferred Features

   • Yunhe Feng, Daniel Saelid, Ke Li, Ruoyuan Gao, Chirag Shah

   Pages 1-8

3. Media Bias Everywhere? A Vision for Dealing with the Manipulation of Public Opinion

   • Michael Färber, Frederic Bartscherer

   Pages 9-13

4. Users’ Perception of Search-Engine Biases and Satisfaction

   • Bin Han, Chirag Shah, Daniel Saelid

   Pages 14-24

5. Preliminary Experiments to Examine the Stability of Bias-Aware Techniques

   • Toshihiro Kamishima, Shotaro Akaho, Yukino Baba, Hisashi Kashima

   Pages 25-35

6. Detecting Race and Gender Bias in Visual Representation of AI on Web Search Engines

   • Mykola Makhortykh, Aleksandra Urman, Roberto Ulloa

   Pages 36-50

7. Equality of Opportunity in Ranking: A Fair-Distributive Model

   • Elena Beretta, Antonio Vetrò, Bruno Lepri, Juan Carlos De Martin

   Pages 51-63

8. Incentives for Item Duplication Under Fair Ranking Policies

   • Giorgio Maria Di Nunzio, Alessandro Fabris, Gianmaria Silvello, Gian Antonio Susto

   Pages 64-77

9. Quantification of the Impact of Popularity Bias in Multi-stakeholder and Time-Aware Environments

   • Francisco Guíñez, Javier Ruiz, María Ignacia Sánchez

   Pages 78-91

10. When Is a Recommendation Model Wrong? A Model-Agnostic Tree-Based Approach to Detecting Biases in Recommendations

    • Joanna Misztal-Radecka, Bipin Indurkhya

    Pages 92-105

11. Examining Video Recommendation Bias on YouTube

    • Baris Kirdemir, Joseph Kready, Esther Mead, Muhammad Nihal Hussain, Nitin Agarwal

    Pages 106-116

12. An Information-Theoretic Measure for Enabling Category Exemptions with an Application to Filter Bubbles

    • Chenyu Jiang, Bowen Wu, Sanghamitra Dutta, Pulkit Grover

    Pages 117-129

13. Perception-Aware Bias Detection for Query Suggestions

    • Fabian Haak, Philipp Schaer

    Pages 130-142

14. Crucial Challenges in Large-Scale Black Box Analyses

    • Tobias D. Krafft, Martin Reber, Roman Krafft, Anna Coutrier, Katharina A. Zweig

    Pages 143-155

15. New Performance Metrics for Offline Content-Based TV Recommender System

    • Luisa Simões, Vaibhav Shah, João Silva, Nelson Rodrigues, Nuno Leite, Nuno Lopes

    Pages 156-169

16. Back Matter

    Pages 171-171

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This book constitutes refereed proceedings of the Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, held in April, 2021. Due to the COVID-19 pandemic BIAS 2021 was held virtually. 

The 11 full papers and 3 short papers were carefully reviewed and selected from 37 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. 

• Computer Science
• Informatics
• Conference Proceedings
• Research
• Applications

• Eurecat - Centre Tecnològic de Catalunya, Barcelona, Spain

  Ludovico Boratto

• Unitelma Sapienza University of Rome, Rome, Italy

  Stefano Faralli

• École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

  Mirko Marras

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

  Giovanni Stilo

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