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Bias detectives: the researchers striving to make algorithms fair

Author

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  • Rachel Courtland

Abstract

As machine learning infiltrates society, scientists are trying to help ward off injustice.

Suggested Citation

  • Rachel Courtland, 2018. "Bias detectives: the researchers striving to make algorithms fair," Nature, Nature, vol. 558(7710), pages 357-360, June.
    • Handle: RePEc:nat:nature:v:558:y:2018:i:7710:d:10.1038_d41586-018-05469-3
    DOI: 10.1038/d41586-018-05469-3
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    Citations

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    Cited by:

    1. David Rozado, 2020. "Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-26, April.
    2. Dario Onorati & Pierfrancesco Tommasino & Leonardo Ranaldi & Francesca Fallucchi & Fabio Massimo Zanzotto, 2020. "Pat-in-the-Loop : Declarative Knowledge for Controlling Neural Networks," Future Internet, MDPI, vol. 12(12), pages 1-12, December.
    3. Dessislava Pachamanova & Vera Tilson & Keely Dwyer-Matzky, 2022. "Case Article—Machine Learning, Ethics, and Change Management: A Data-Driven Approach to Improving Hospital Observation Unit Operations," INFORMS Transactions on Education, INFORMS, vol. 22(3), pages 178-187, May.
    4. Fellnhofer, Katharina & Sornette, Didier, 2022. "Embracing The Intuitive-Analytical Paradox? How Intuitive And Analytical Decision-Making Drive Paradoxes In Simple And Complex Environments," OSF Preprints evjd6, Center for Open Science.
    5. Muhammad Tanveer & Shafiqul Hassan & Amiya Bhaumik, 2020. "Academic Policy Regarding Sustainability and Artificial Intelligence (AI)," Sustainability, MDPI, vol. 12(22), pages 1-13, November.
    6. Vesnic-Alujevic, Lucia & Nascimento, Susana & Pólvora, Alexandre, 2020. "Societal and ethical impacts of artificial intelligence: Critical notes on European policy frameworks," Telecommunications Policy, Elsevier, vol. 44(6).
    7. Sanaz Honarmand Ebrahimi & Marinus Ossewaarde & Ariana Need, 2021. "Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    8. Veale, Michael & Brass, Irina, 2019. "Administration by Algorithm? Public Management meets Public Sector Machine Learning," SocArXiv mwhnb, Center for Open Science.
    9. Emma Dahlin, 2021. "Mind the gap! On the future of AI research," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-4, December.
    10. Michael Färber & Melissa Coutinho & Shuzhou Yuan, 2023. "Biases in scholarly recommender systems: impact, prevalence, and mitigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2703-2736, May.
    11. Scott Thiebes & Sebastian Lins & Ali Sunyaev, 2021. "Trustworthy artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 447-464, June.

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