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Creators/Authors contains: "Abbasi, Mohsen"

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  1. ; ; ( , ACM Conference on Fairness, Accountability and Transparency)
    null (Ed.)
    Full Text Available 
  2. ; ; ; ( , Proceedings of the 2019 SIAM International Conference on Data Mining)
    While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this paper, we formalize two notions of stereotyping and show how they manifest in later allocative harms within the machine learning pipeline. We also propose mitigation strategies and demonstrate their effectiveness on synthetic datasets. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ( , Proceedings of the 2019 SIAM International Conference on Data Mining)
    While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this paper, we formalize two notions of stereotyping and show how they manifest in later allocative harms within the machine learning pipeline. We also propose mitigation strategies and demonstrate their effectiveness on synthetic datasets. 
    more » « less
    Accepted Manuscript Full Text Available