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Eliciting High School Students’ Conceptions and Intuitions about Algorithmic Bias

Published:07 August 2022Publication History

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References

  1. Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency(Proceedings of Machine Learning Research, Vol. 81), Sorelle A. Friedler and Christo Wilson (Eds.). PMLR, 77–91. https://proceedings.mlr.press/v81/buolamwini18a.htmlGoogle ScholarGoogle Scholar
  2. Victor R. Lee, Michelle H. Wilkerson, and Kathryn Lanouette. 2021. A Call for a Humanistic Stance toward K-12 Data Science Education. Educational Researcher 50, 9 (2021), 664–672. https://doi.org/10.3102/0013189X211048810Google ScholarGoogle ScholarCross RefCross Ref
  3. Safiya Noble. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press, New York.Google ScholarGoogle Scholar
  4. David Touretzky, Christina Gardner-McCune, Fred Martin, and Deborah Seehorn. 2019. Envisioning AI for K-12: What Should Every Child Know about AI?Proceedings of the AAAI Conference on Artificial Intelligence 33, 01(2019), 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Conferences
    ICER '22: Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2
    August 2022
    57 pages
    ISBN:9781450391955
    DOI:10.1145/3501709

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    • Published: 7 August 2022

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