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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2021
    • vol. 6, nº 5, march 2021
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    •   Inicio
    • UNIR REVISTAS
    • Revista IJIMAI
    • 2021
    • vol. 6, nº 5, march 2021
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    Achieving Fair Inference Using Error-Prone Outcomes

    Autor: 
    Boeschoten, Laura
    ;
    van Kesteren, Erik-Jan
    ;
    Bagheri, Ayoub
    ;
    Oberski, Daniel L.
    Fecha: 
    03/2021
    Palabra clave: 
    algorithmic bias; latent variable model; error analysis; fair machine learning; measurement invariance; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12889
    DOI: 
    https://doi.org/10.9781/ijimai.2021.02.007
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2895
    Open Access
    Resumen:
    Recently, an increasing amount of research has focused on methods to assess and account for fairness criteria when predicting ground truth targets in supervised learning. However, recent literature has shown that prediction unfairness can potentially arise due to measurement error when target labels are error prone. In this study we demonstrate that existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest, when an error-prone proxy target is used. As a solution to this problem, we suggest a framework that combines two existing fields of research: fair ML methods, such as those found in the counterfactual fairness literature and measurement models found in the statistical literature. Firstly, we discuss these approaches and how they can be combined to form our framework. We also show that, in a healthcare decision problem, a latent variable model to account for measurement error removes the unfairness detected previously.
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