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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/153165


    Title: 對神經網路模型的個體公平性進行動態型式測試
    Concolic Testing on Individual Fairness of Neural Network Models
    Authors: 黃名儀
    Huang, Ming-I
    Contributors: 郁方
    洪智鐸
    Yu, Fang
    Hong, Chih-Duo
    黃名儀
    Huang, Ming-I
    Keywords: 政治大學
    深度神經網路
    動態符號執行測試
    公平性測試
    NCCU
    Concolic Testing
    Fairness Testing
    Deep Neural Networks
    Date: 2024
    Issue Date: 2024-09-04 14:06:44 (UTC+8)
    Abstract: 深度神經網絡（DNNs）在刑事司法、招聘實踐和金融貸款決策等關鍵社會領域中變得越來越普遍。然而，這些應用往往無意中延續了偏見，導致對個體的歧視，從而限制了它們對社會的更廣泛利益。本研究針對深度神經網絡（DNNs）中的個體公平性進行探討。與以往研究相比，我們的研究在系統性公平性檢查方面做出了貢獻，提供了一種自動化和嚴謹的方法來識別DNN中的不公平實例。
    Deep neural networks (DNNs) are becoming more prevalent in crucial societal domains such as criminal justice, hiring practices, and financial lending decisions. However, these applications frequently unintentionally perpetuate biases that lead to individual discrimination, thus constraining their broader societal benefits. This study addresses individual fairness in deep neural networks (DNNs). Compared to previous work, our research contributes on systematic fairness checking, offering an automatic and rigorous approach to identify instances of unfairness in DNNs.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    111356047
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111356047
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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