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Fairness in AI for healthcare
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Fairness in AI for healthcare (2024)Fairness in AI for healthcareIn-Processing Modeling Techniques for Machine Learning Fairness: A Survey (2022)In-Processing Modeling Techni…A Survey on Bias and Fairness in Machine Learning (2021)A Survey on Bias and Fairness…New evidence pyramid (2016)New evidence pyramidGRADE guidelines: 9. Rating up the quality of evidence (2011)MetaInsight: An interactive web‐based tool for analyzing, interrogating, and visualizing network meta‐analyses using R‐shiny and netmeta (2019)Systematic Literature Reviews: An Introduction (2019)The Handbook of Behavior Change (2020)Fairness through awareness (2012)Semantics derived automatically from language corpora contain human-like biases (2017)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Algorithmic Bias in Education (2021)Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023)Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023)Justice as Fairness (1958)A scoping review of fair machine learning techniques when using real-world data (2024)Assessing trustworthy AI: Technical and legal perspectives of fairness in AI (2024)Fairness for machine learning software in education: A systematic mapping study (2024)
this work references cited by second hop

References

  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2016New evidence pyramid1,382 cited
  • 2022In-Processing Modeling Techniques for Machine Learning Fairness: A Survey99 cited

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