Algorithms can remove bias blind spots, study shows
Co.Exist,
Algorithms are a staple of modern life. People rely on algorithmic recommendations to wade through deep catalogs and find the…
Advances in Bias and Fairness in Information Retrieval
Overview of attention for book
Table of Contents
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Altmetric BadgeBook Overview
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Altmetric BadgeChapter 1 Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
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Altmetric BadgeChapter 2 The Impact of Recommender System and Users’ Behaviour on Choices’ Distribution and Quality
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Altmetric BadgeChapter 3 Sequential Nature of Recommender Systems Disrupts the Evaluation Process
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Altmetric BadgeChapter 4 Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures
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Altmetric BadgeChapter 5 A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches
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Altmetric BadgeChapter 6 The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation
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Altmetric BadgeChapter 7 The Unfairness of Popularity Bias in Book Recommendation
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Altmetric BadgeChapter 8 Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches
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Altmetric BadgeChapter 9 Analysis of Biases in Calibrated Recommendations
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Altmetric BadgeChapter 10 Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?
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Altmetric BadgeChapter 11 Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach
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Altmetric BadgeChapter 12 Keyword Recommendation for Fair Search
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Altmetric BadgeChapter 13 FARGO: A Fair, Context-AwaRe, Group RecOmmender System
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Citations
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