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A step toward building a unified framework for managing AI bias
16 references·1 citing·42 second hop
A step toward building a unified framework for managing AI bias (2023)A step toward building a unified framewor…Towards a standard for identifying and managing bias in artificial intelligence (2022)Towards a standard for identi…A Survey on Bias and Fairness in Machine Learning (2021)A Survey on Bias and Fairness…A Framework of Potential Sources of Harm Throughout the Machine Learning Life Cycle (2021)A Framework of Potential Sour…Bias in data‐driven artificial intelligence systems—An introductory survey (2020)Bias in data‐driven artificia…The global landscape of AI ethics guidelines (2019)The global landscape of AI et…AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias (2019)AI Fairness 360: An extensibl…The accuracy, fairness, and limits of predicting recidivism (2018)The accuracy, fairness, and l…Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Gender Shades: Intersectional…Ensuring Fairness in Machine Learning to Advance Health Equity (2018)Ensuring Fairness in Machine …Bias on the web (2018)Bias on the webWeapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2017)Weapons of Math Destruction: …The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity (2017)The Ugly Truth About Ourselve…An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence (2017)An Intelligence in Our Image:…Discrimination-aware data mining (2008)Discrimination-aware data min…Conducting research literature reviews: from the Internet to paper (2005)Conducting research literatur…Sex Bias in Graduate Admissions: Data from Berkeley (1975)Sex Bias in Graduate Admissio…Biases in Artificial Intelligence Application in Pain Medicine (2025)Biases in Artificial Intellig…Big data: a revolution that will transform how we live, work, and think (2013)Big Data: A Revolution That Will Transform How We Live, Work, and Think (2014)Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject (2018)Algorithms at Work: The New Contested Terrain of Control (2019)A META‐ANALYSIS OF THE PREDICTORS OF ADULT OFFENDER RECIDIVISM: WHAT WORKS!* (1996)Predicting relapse: A meta-analysis of sexual offender recidivism studies. 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References

  • 2019The global landscape of AI ethics guidelines6,172 cited
  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2017Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy3,071 cited
  • 2005Conducting research literature reviews: from the Internet to paper1,853 cited
  • 2018Ensuring Fairness in Machine Learning to Advance Health Equity1,334 cited
  • 2018The accuracy, fairness, and limits of predicting recidivism1,071 cited
  • 2020Bias in data‐driven artificial intelligence systems—An introductory survey1,040 cited
  • 2019AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias884 cited
  • 2008Discrimination-aware data mining676 cited
  • 1975Sex Bias in Graduate Admissions: Data from Berkeley659 cited
  • 2021A Framework of Potential Sources of Harm Throughout the Machine Learning Life Cycle573 cited
  • 2022Towards a standard for identifying and managing bias in artificial intelligence541 cited
  • 2018Bias on the web430 cited
  • 2017The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity

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336 cited
  • 2017An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence326 cited
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    • 2025Biases in Artificial Intelligence Application in Pain Medicine19 cited