Improvement and DIscovery of Policy through Utilization of Gender Big Data
Type Occasional Period 2018
Manager You Kyoung Moon Date 2019-01-23
Fiie Coopration_01 Improvement and Discovery of Policy through Utilization of Gender Big Data(Ⅱ).pdf ( 79.66 KB )

2018 KWDI Abstract


Improvement and Discovery of Policy through Utilization of Gender Big Data()


You Kyung Moon

Ki Taek Jeon

Young Taek Kim

Sungmi Jung

Ho Joong Bae

Hee-Tae Chung

Youjing Kim


This study is a three-year project that takes place from 2017 to 2019. This year is the second year. The purpose of this year's research is to create a foundation for gender big data's utilization. To this end, we looked at policies for promoting women's big data in international organizations and major countries. We also studied the possibility of gender discrimination in the utilization of big data. Second, it is a pilot analysis of big data by each sector. To this end, we analyzed the credit card big data of the private sector and the health insurance big data of the public sector at the same time, along with the existing statistics on the status of high-risk drinking women. In addition, spatial big data combined with space, facilities, and location information were analyzed to explain the safety of women. Finally, we analyzed the ramifications of social data discourse on "Me too Movement" that is the big issue of this year Space data shows a close relationship with women's safety. Credit card sales in a region's entertainment industry are highly related to the level of sexual violence. In addition, the current population of women in their 20s is very similar to the current status of sexual violence.


The analysis of women's high-risk drinking consisted of three layers: national approved statistics, credit card performance big data, and the big data of National Health Insurance. All three data are consistent with the constant increase in women's high-risk drinking, decrease in men, and decrease in gender differences. In particular, the high-risk drinking rate of women in their 20s and 30s is increasing very steeply.

We analyzed "Me-Too" using Facebook's public postings and comments, recomments, shared postings. A result of analyzing shows that on-line conflict between sex is growing. So efforts are needed to resolve the conflict structure. Second, judicial fairness needs to be secured. The current legal system does not sufficiently protect victims of sexual violence. The accumulation of gender aware and equitable precedents in the judiciary is necessary Big data algorithms fully contain potential concerns to regenerate and reinforce discrimination by including gender discrimination. Research on whether Big Data algorithms involved in decision-making on eligibility are regenerating discrimination, particularly in the financial and professional sectors, needs to be promoted at a government level. Guidelines should be provided to avoid potential issues and problems that exist throughout the development of algorithms. To secure the accountability and transparency of Big Data algorithms by selecting departments responsible for quality diagnosis of Big Data, it is necessary to develop and diffuse meta information prototypes of algorithms. Empirical research is needed on the existence of gender discrimination issues in screening algorithms such as credit assessment, loan screening, insurance review, job referral, and admission.