Survey of Fairness in Machine Learning for Indonesian General Election Research

Ghiffari Assamar Qandi, Nur Aini Rakhmawati
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Abstract

Machine Learning is one of the popular fields of scientific research at the moment. Fairness is one of the trust parameters in machine learning research. Machine Learning principle is to remove human biases in providing assessment. Therefore, we conducted a survey using Google Scholar to find out the fairness of Machine Learning research about Indonesian General Election 2019. First, we collect all Machine Learning research related to topics. Then, we made the selection to ensure the research meets up criteria. The main criteria in this study are data collection and data set selection process. Our study finds out that the current state of fairness in machine learning research related to the topic is shallow. Most of the studies almost have no transparency in data collection, and final data set being selected.
印尼大选研究中机器学习公平性调查
机器学习是目前科学研究的热门领域之一。公平性是机器学习研究中的信任参数之一。机器学习的原理是在提供评估时消除人类的偏见。因此,我们使用谷歌Scholar进行了一项调查,以找出关于2019年印度尼西亚大选的机器学习研究的公平性。首先,我们收集了所有与机器学习相关的研究课题。然后,我们进行选择,以确保研究符合标准。本研究的主要标准是数据收集和数据集选择过程。我们的研究发现,目前与该主题相关的机器学习研究的公平状况很浅。大多数研究在数据收集上几乎没有透明度,最终的数据集被选择。
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