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Multidisciplinary considerations of fairness in medical AI: A scoping review
16 references·2 citing·37 second hop
Multidisciplinary considerations of fairness in medical AI: A scoping review (2023)Multidisciplinary considerations of fairn…A Review on Fairness in Machine Learning (2022)A Review on Fairness in Machi…Updated methodological guidance for the conduct of scoping reviews (2021)Updated methodological guidan…Addressing bias in big data and AI for health care: A call for open science (2021)Addressing bias in big data a…The Ethics of AI Ethics: An Evaluation of Guidelines (2020)The Ethics of AI Ethics: An E…PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation (2018)PRISMA Extension for Scoping …Counterfactual Fairness (2017)Counterfactual FairnessFairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment (2016)Fairness Beyond Disparate Tre…Equality of Opportunity in Supervised Learning (2016)Equality of Opportunity in Su…Guidance for conducting systematic scoping reviews (2015)Guidance for conducting syste…Fairness Constraints: Mechanisms for Fair Classification (2015)Fairness Constraints: Mechani…Certifying and Removing Disparate Impact (2015)Certifying and Removing Dispa…Fairness-Aware Classifier with Prejudice Remover Regularizer (2012)Fairness-Aware Classifier wit…Three naive Bayes approaches for discrimination-free classification (2010)Three naive Bayes approaches …Scoping studies: advancing the methodology (2010)Scoping studies: advancing th…Generalization in quantitative and qualitative research: Myths and strategies (2010)Generalization in quantitativ…Scoping studies: towards a methodological framework (2005)Scoping studies: towards a me…Identifying and mitigating algorithmic bias in the safety net (2025)Identifying and mitigating al…Algorethics in Healthcare: Balancing Innovation and Integrity in AI Development (2024)Algorethics in Healthcare: Ba…The Interpretation of Cultures (1974)Qualitative evaluation and research methods (1990)Generalizability in qualitative research: misunderstandings, opportunities and recommendations for the sport and exercise sciences (2017)The Interpretation of Cultures (2017)Improving the practical application of the Delphi method in group-based judgment: A six-step prescription for a well-founded and defensible process (2019)Sample Size for Survey Research: Review and Recommendations (2020)Uniform Guidelines on Employee Selection Procedures (2007)Fairness through awareness (2012)European Union Regulations on Algorithmic Decision Making and a “Right to Explanation” (2017)Semantics derived automatically from language corpora contain human-like biases (2017)A Survey on Bias and Fairness in Machine Learning (2021)Three Approaches to Qualitative Content Analysis (2005)A typology of reviews: an analysis of 14 review types and associated methodologies (2009)Updated methodological guidance for the conduct of scoping reviews (2020)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Big Data's Disparate Impact (2014)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope (2023)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)The PRISMA 2020 statement: an updated guideline for reporting systematic reviews (2021)The PRISMA 2020 statement: an updated guideline for reporting systematic reviews (2021)The PRISMA 2020 statement: An updated guideline for reporting systematic reviews (2021)Structural Equations with Latent Variables. (1991)Testing Structural Equation Models. (1995)Fairness definitions explained (2018)AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias (2019)Bias in data‐driven artificial intelligence systems—An introductory survey (2020)Experimental evidence of massive-scale emotional contagion through social networks (2014)AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations (2018)The global landscape of AI ethics guidelines (2019)Emerging challenges in AI and the need for AI ethics education (2020)Ethical principles for artificial intelligence in education (2022)Trustworthy Artificial Intelligence: A Review (2022)Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach (2018)Tools to support the automation of systematic reviews: a scoping review (2021)Best practice guidance and reporting items for the development of scoping review protocols (2022)“How-to”: scoping review? (2024)
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References

  • 2018PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation43,318 cited
  • 2005Scoping studies: towards a methodological framework36,603 cited
  • 2010Scoping studies: advancing the methodology15,226 cited
  • 2015Guidance for conducting systematic scoping reviews7,457 cited
  • 2016Equality of Opportunity in Supervised Learning2,535 cited
  • 2020The Ethics of AI Ethics: An Evaluation of Guidelines1,900 cited
  • 2015Certifying and Removing Disparate Impact1,827 cited
  • 2010Generalization in quantitative and qualitative research: Myths and strategies1,767 cited
  • 2021Updated methodological guidance for the conduct of scoping reviews1,448 cited
  • 2017Counterfactual Fairness995 cited
  • 2021Addressing bias in big data and AI for health care: A call for open science902 cited
  • 2010Three naive Bayes approaches for discrimination-free classification756 cited
  • 2012Fairness-Aware Classifier with Prejudice Remover Regularizer694 cited
  • 2015Fairness Constraints: Mechanisms for Fair Classification644 cited
  • 2022A Review on Fairness in Machine Learning596 cited
  • 2016

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Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
559 cited

Cited by

  • 2025Identifying and mitigating algorithmic bias in the safety net22 cited
  • 2024Algorethics in Healthcare: Balancing Innovation and Integrity in AI Development17 cited