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FaiRecSys: mitigating algorithmic bias in recommender systems · Study

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ArticleFaiRecSys: mitigating algorithmic bias in recommender systems

Bora Edizel2019

Article2019

FaiRecSys: mitigating algorithmic bias in recommender systems

Bora Edizel · Francesco Bonchi · Sara Hajian · André Panisson · Tamir Tassa

71 citations·0/0 files stored·International Journal of Data Science and Analytics

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Ethics and Social Impacts of AI Recommender Systems and Techniques Privacy-Preserving Technologies in Data

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References

2012Fairness through awareness 2015Certifying and Removing Disparate Impact 2011Data preprocessing techniques for classification without discrimination 2013Learning Fair Representations 2010Three naive Bayes approaches for discrimination-free classification 2013Discrimination in online ad delivery 2008Discrimination-aware data mining 2015Automated Experiments on Ad Privacy Settings

Cited by

2021Fair Top-k Ranking with multiple protected groups 2021Fairness metrics and bias mitigation strategies for rating predictions 2022Towards a multi-stakeholder value-based assessment framework for algorithmic systems 2021When the Umpire is also a Player 2021ARTIFICIAL INTELLIGENCE’S ALGORITHMIC BIAS: ETHICAL AND LEGAL ISSUES 2024The Role of Human-Centered AI in User Modeling, Adaptation, and Personalization—Models, Frameworks, and Paradigms

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2023Learning fair representations via an adversarial frameworkarticle · 13 shared references 2021Fair Top-k Ranking with multiple protected groupsarticle · 12 shared references 2017Model-based and actual independence for fairness-aware classificationarticle · 12 shared references 2017Achieving Non-Discrimination in Data Releaseother · 11 shared references 2017Anti-discrimination learning: a causal modeling-based frameworkarticle · 11 shared references 2021Fairness metrics and bias mitigation strategies for rating predictionsarticle · 11 shared references

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Year2019

Typearticle

Published2019-03-30

VenueInternational Journal of Data Science and Analytics

ISSN-L2364-415X

Languageen

Citations71

Files0 public / 0

ShelfEthics and Social Impacts of AI

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doi10.1007/s41060-019-00181-5

openalex_workW2932004331

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2024

Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation

2022Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling