Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Gendered innovation for algorithm through case studies

음성·영상 신호 처리 알고리즘 사례를 통해 본 젠더혁신의 필요성

  • Lee, JiYeoun (Department of Biomedical Engineering, Jungwon University) ;
  • Lee, Heisook (Center for Gendered Innovations in Science &Technology, KOFWST)
  • 이지연 (중원대학교 생체의공학과) ;
  • 이혜숙 (한국여성과학기술단체총연합회, 젠더혁신연구센터)
  • Received : 2018.10.30
  • Accepted : 2018.12.20
  • Published : 2018.12.28
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Abstract

Gendered innovations is a term used by policy makers and academics to refer the process of creating better research and development (R&D) for both men and women. In this paper, we analyze the literatures in image and speech signal processing that can be used in ICT, examine the importance of gendered innovations through case study. Therefore the latest domestic and foreign literature related to image and speech signal processing based on gender research is searched and a total of 9 papers are selected. In terms of gender analysis, research subjects, research environment, and research design are examined separately. Especially, through the case analysis of algorithms of the elderly voice signal processing, machine learning, machine translation technology, and facial gender recognition technology, we found that there is gender bias in existing algorithms, and which leads to gender analysis is required. We also propose a gendered innovations method integrating sex and gender analysis in algorithm development. Gendered innovations in ICT can contribute to the creation of new markets by developing products and services that reflect the needs of both men and women.

젠더혁신은 연구개발의 전 과정에서 남녀의 생물학적, 인지적, 사회적 특성 및 행동방식의 차이에 의한 성 젠더 요소를 고려하여 남녀 모두를 위한 보다 나은 연구개발과 지식을 창출하는 과정을 의미한다. 본 논문의 연구목적은 ICT산업, 자동차 산업, 빅데이터, 로봇 산업 등에 활용할 수 있는 영상 음성신호처리에서 문헌연구 및 기존 자료를 분석하고 사례 조사를 통하여 젠더혁신의 중요성을 고찰하는 것이다. 본 연구에서는 젠더 연구를 기반으로 영상 음성신호처리의 관련된 최신 국내외 문헌을 검색하고 총 8편의 논문을 선정한다. 그리고 젠더분석 측면에서, 연구대상, 연구 환경, 연구 설계로 구분하여 살펴본다. 연구결과로써, 노인음성 신호처리, 기계학습과 젠더, 기계번역 기술, 안면 젠더인식 기술의 음성 영상신호 처리 알고리즘 논문 사례 분석을 통하여 기존의 알고리즘에 젠더편향성이 있음을 밝히고 이들 알고리즘 개발에서 상황에 맞는 성 젠더 분석이 필요함을 보인다. 또한 알고리즘 개발에 다양한 성 젠더 요소를 반영하는 젠더혁신 방법과 정책을 제안한다. 추후 ICT에서의 젠더혁신은 남녀 모두의 요구를 반영한 제품과 서비스를 개발로 새로운 시장 창출에 기여할 수 있다.

Keywords

Table 1. Examples of gender bias in Google translation

DJTJBT_2018_v16n12_459_t0001.png 이미지

Table 2. Facial gender recognition error rate(%)

DJTJBT_2018_v16n12_459_t0002.png 이미지

Table 3. Research methodology for gender innovation

DJTJBT_2018_v16n12_459_t0003.png 이미지

References

  1. Center for Women in Science, Engineering, and Technology. (2013). How gender analysis of science and technology gender innovation contributes to research. Seoul : WISET. ISBN 978-89-97520-24-4
  2. KOFWST. (2017). http://gister.re.kr/#!/main
  3. M. O. Moon. (2014). 'Gender Innovation' in Science and Technology R & D and Knowledge Diffusion. Seoul : Policy Research Report of Science and Technology Advisory Council.
  4. L. Zhang & M. S. Na. (2017). A qualitative study on women's daily lives and smartphone use - focusing on interviews with Chinese women in their twenties. Journal of the Korea Convergence Society, 15(10), 467-483.
  5. D. C. Park. (2013). Effect of news anchor's gender on affect of viewers and memory of news. Journal of the Korea Convergence Society, 11(9), 333-339.
  6. J. Y. Lee. (2018). Gender Analysis in Elderly Speech Signal Processing. Journal of the Korea Convergence Society, 10(10), 351-356.
  7. J. H. Moon & J. Y. Lee. (2015). Development of medical/electrical convergence software for classification between normal and pathological voices. Journal of the Korea Convergence Society, 13(12), 187-192.
  8. K. N. Lee. (2017). Voice language processing technology, how far have you come. National Korean Language Institute, New Language Life, 27(4), 99-116.
  9. B. C. Cho, S. Cheon, K. N. Kim & H. S. Yuk. (2018). A policy study for the voice recognition technology based on elderly health care. Journal of the Korea Convergence Society, 16(2), 9-17.
  10. S. Y. Lee. (2011). The overall speaking rate and articulation rate of normal elderly people, Graduate program in speech and language pathology, Master these, Yonsei University.
  11. J. Lee. (2014). KHIDI Brief. Korea Health Industry Development Institute, 140(2014), 1-2.
  12. H. T. Kim, S. H. Cho, S. M. Youn, D. I. Sun & M. S. Kim. (2000). The Changes and Characteristics of Acoustic Parameters with Aging in Korean, Korean J Otolaryngol, 2000(43), 69-74
  13. H. J. Moon, M. H, Lee & K. H. Jeong. (2015). Authentication Performance Optimization for Smart-phone based Multimodal Biometrics. Journal of the Korea Convergence Society, 13(6), 151-156.
  14. S. W. Kim, H. H. Park, E. S. Park & H. S. Choi. (2010). Acoustic Characteristics of Normal Healthy Koreans with Advancing age, Phonetics and Speech Sciences, 2(4), 19-28
  15. J. Y. Lee. (2016). Development of Voice Activity Detection Algorithm for Elderly Voice based on the Higher Order Differential Energy Operator, Journal of Digital Convergence. 14(11), 249-255. https://doi.org/10.14400/JDC.2016.14.11.249
  16. A. Caliskan, J. J. Bryson, & A. Narayanan. (2017). Semantics derived automatically from language corpora contain human-like biases, Science. 356(6334), 183-186. DOI: 10.1126/science.aal4230
  17. T. Bolukbasi, K. Chang, J. Zou, V. Saligrama & A. Kalai. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. DOI; arXiv:1607.06520
  18. J. M. Twenge, W. K. Cambell & B. Gentile. (2012). Male and Female Pronoun Use in the U.S. Books Reflects Women's Status, 1900-2008, Sex Role, 67(9-10), 488-493. DOI : 10.1007/s11199-012-0194-
  19. L. Schiebinger. (2014). Gendered Innovations: harnessing the creative power of sex and gender anaysis to discover new ideas and develop new technologies, Triple Helix, 1-9.
  20. P. Jonathon Phillps et al. (2011). An Other-Race Effect for Face Recognition Algorithms, ACM digital library, 8(2). DOI: 10.1145/1870076.1870082
  21. J. Buolamwini & T. Gebru. (2018). Gender Shade: Intersectional Accuracy Disparities in Commercial Gender Classification, Proceedings of Machine Learning Research, 81, 1-15.