Survey of Fairness in Machine Learning for Indonesian General Election Research

Ghiffari Assamar Qandi, Nur Aini Rakhmawati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
EditorsIndra Hermawan, Muhammad Yusuf Bagus Rasyidin, Malisa Huzaifa, Iklima Ermis Ismail, Asep Taufik Muharram, Anggi Mardiyono, Noorlela Marcheeta, Dewi Kurniawati, Ade Rahma Yuly, Ariawan Andi Suhanda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-24
Number of pages6
ISBN (Electronic)9781728182476
DOIs
Publication statusPublished - 15 Sept 2020
Event3rd International Conference on Computer and Informatics Engineering, IC2IE 2020 - Depok, Indonesia
Duration: 15 Sept 202016 Sept 2020

Publication series

Name2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020

Conference

Conference3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
Country/TerritoryIndonesia
CityDepok
Period15/09/2016/09/20

Keywords

  • data bias
  • fairness
  • machine learning
  • social media data

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