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Mitigating algorithmic bias is a critical task in the development and deployment of machine learning models.
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Detecting race and gender bias in visual representation of AI on web search engines. In International Workshop on Algorithmic Bias in Search and Recommendation . Springer, 36–50
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Algorithmic misogynoir in content moderation practice
Brandeis Marshall. 2021 · 2021
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Does robustness improve fairness? Approaching fairness with word substitution robustness methods for text classification. In ACL-IJCNLP 2021
Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, and Kai-Wei Chang. 2021 · 2021
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Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–13
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From reality to world. A critical perspective on AI fairness
Jean-Marie John-Mathews, Dominique Cardon, and Christine Balagué. 2022 · 2022
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Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness
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Towards Involving End-users in Interactive Human-in-the-loop AI Fairness
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Your Fairness May Vary: Pretrained Language Model Fairness in Toxic Text Classification. In Annual Meeting of the Association for Computational Linguistics
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