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Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers
16 references·1 citing·46 second hop
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References

  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2017Semantics derived automatically from language corpora contain human-like biases2,845 cited
  • 2019Model Cards for Model Reporting2,653 cited
  • 2020Deep Facial Expression Recognition: A Survey1,942 cited
  • 2020Data and its (dis)contents: A survey of dataset development and use in machine learning research519 cited
  • 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias437 cited
  • 2020Towards a critical race methodology in algorithmic fairness295 cited
  • 2006Research Misconduct, Retraction, and Cleansing the Medical Literature: Lessons from the Poehlman Case199 cited
  • 2021Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases159 cited
  • 2019Racial categories in machine learning110 cited
  • 2019Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning103 cited
  • 2020Continued post-retraction citation of a fraudulent clinical trial report, 11 years after it was retracted for falsifying data88 cited
  • 2015Perpetuation of Retracted Publications Using the Example of the Scott S. Reuben Case: Incidences, Reasons and Possible Improvements87 cited
  • 2020Large image datasets: A pyrrhic win for computer vision?

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87 cited
  • 2020REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets72 cited
  • 2020Does retraction after misconduct have an impact on citations? A pre–post study67 cited
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    • 2024Identifying and Categorizing Bias in AI/ML for Earth Sciences24 cited