Workgreen2022

CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

Mohammadmehdi Naghiaei · Hossein A. Rahmani · Yashar Deldjoo

125 citations0/0 files storedInternational ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

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Ethics and Social Impacts of AI Recommender Systems and Techniques Game Theory and Voting Systems

Abstract

Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments.

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References

2021A Survey on Bias and Fairness in Machine Learning 2012Fairness through awareness 2009Measuring Discrimination in Socially-Sensitive Decision Records

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2024How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study 2024Fairness of ChatGPT and the Role of Explainable-Guided Prompts

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