Eksik temaların ortaya çıkarılması: Yapay zeka destekli bibliyometrik analiz yoluyla cinsiyetçi önyargının analizi
Yıl 2024,
Cilt: 2 Sayı: 2, 1 - 24, 31.12.2024
Senem Gürkan
,
Seçil Duran
,
Volkan Duran
Öz
Bu çalışma, literatürdeki eksik temaları ortaya çıkarmak için yapay zeka ve bibliyometrik analizden
yararlanarak, literatürde cinsiyet yanlılığı odaklarının az araştırılmış alanlarını araştırmaktadır. Çalışmada Web
of Science Core Collection'dan derlenen veri setine yapay zeka destekli nitel araştırma teknikleri uygulanmıştır.
Bulgular, zihinsel sağlığın, çeşitli kimliklerin ve teknoloji ve medyadaki önyargıların azaltılmasının önemini
vurgulayan, belirlenen eşitsizlikleri ele alan daha toplum merkezli araştırmaları ve kapsayıcı literatürü
savunmaktadır. Ayrıca bu çalışma, toplumsal cinsiyet yanlılığı ve şiddetle etkili bir şekilde mücadele etmek için
akademik araştırmaları toplulukların gerçek kaygılarıyla uyumlu hale getirmenin gerekliliğinin altını çizmektedir.
Kaynakça
- Agudo, U., Arrese, M., Liberal, K. G., & Matute, H. (2022). Assessing emotion and sensitivity of AI
artwork. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.879088
- Bhasin, K. (2003). Toplumsal cinsiyet “Bize yüklenen roller” (Kader Ay, Trans.). Kadınlarla Dayanışma
Vakfı Yayınları.
- Bora, A. (2021). Feminizm kendi arasında. İletişim Yayınevi
- Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016). Man is to computer programmer
as woman is to homemaker? Debiasing word embeddings. arXiv: Computation and Language
(cs.CL), 1-20. https://doi.org/10.48550/arXiv.1607.06520
- Caldas-Coulthard, C., & Moon, R. (2010). 'Curvy, Hunky, Kinky': Using corpora as tools for critical
analysis. Discourse & Society, 21(2), 99-133. https://doi.org/10.1177/0957926509353843
- Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric
analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
https://doi.org/10.1016/j.jbusres.2021.04.070
- EIGE (2020). Gender equality index, Retrieved December 24 2024 from https://eige.europa.eu/genderequality-index/2022
- Font, J. E., & Costa-Jussa, M. R. (2019). Equalizing gender biases in neural machine translation with
word embedding techniques. Xiv preprint arXiv:1901.03116.
https://doi.org/10.48550/arXiv.1901.03116
- González, A. S., & Rampino, L. (2024). A design perspective on how to tackle gender biases when
developing AI-driven systems. AI Ethics. https://doi.org/10.1007/s43681-023-00386-2
- Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative
Health Research, 15, 1277-1288. https://doi.org/10.1177/1049732305276687
- Jones, Henry, A. J. I., Artikis, A., & Pitt, J. (2013). The design of intelligent sociotechnical systems.
Artificial Intelligence Review, 39, 5–20. https://doi.org/10.1007/s10462-012-9387-2
- Kaplan, C., & Grewal, I. (2002). Transnational practices and interdisciplinary feminist scholarship:
Refiguring women’s and gender studies (Robyn Wiegman, Inderpal Grewal & Caren Kaplan,
Eds.) In Women's studies on its own: A next wave reader in institutional change (pp. 66-81). Duke
University Press. https://doi.org/10.1515/9780822384311-003
- Leavy, S. (2018). Gender bias in artifcial intelligence: The need for diversity and gender theory in
machine learning. In Proceedings of the 1st international workshop on gender equality in software
engineering (pp. 14–16). ACM.
- Lindgren, S., & Holmström, J. (2020). Social science perspective on artifcial intelligence. Journal of
Digital Social Research (JDSR), 2(3), 1-15. https://doi.org/10. 33621/jdsr.v2i3.65
- Litosseliti, L., & Sunderland, J. (2002). Gender identity and discourse analysis. John Benjamins
Publishing.
- Lu, D., Neves, L., Carvalho, V., Zhang, N., & Ji, H. (2018). Visual attention model for name tagging in
multimodal social media. In Proceedings of the 56th Annual Meeting of the Association for
Computational Linguistics (Volume 1: Long Papers) (pp. 1990–1999). Association for
Computational Linguistics.
- Machimbarrena, J. M., Calvete, E., Fernández-González, L., Álvarez-Bardón, A., Álvarez- Fernández,
L., & González-Cabrera, J. (2018). Internet risks: An overview of victimization in cyberbullying,
cyber dating abuse, sexting, online grooming and problematic internet use. International Journal
of Environmental Research and Public Health. 15(11), 2471.
https://doi.org/10.3390/ijerph15112471
- Matthews, A., Grasso, I., Mahoney, C., Chen, Y., Wali, E., Middleton, T., Njie, M., & Matthews, J.
(2021, June). Gender bias in natural language processing across human languages In (Y.
Pruksachatkun, A. Ramakrishna, K.-W. Chang, S. Krishna, J. Dhamala, T. Guha, & X. Ren, Eds.)
Proceedings of the first workshop on trustworthy natural language processing (pp. 45–54).
Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.trustnlp-1.6
- Motschenbacher, H. (2013). Gentlemen before ladies? A corpus-based study of conjunct order in
personal binomials. Journal of English Linguistics, 41(3), 212-242.
- Pathak, S., Solanki, V. K., & Linh, N. T. D. (2024). Gender biasness – a victim of artificial intelligencebased development (Mishra, D., Ngoc Le, A., & McDowell, Z., Eds.) In Communication
technology and gender violence. Signals and communication technology. Springer, Cham.
https://doi.org/10.1007/978-3-031-45237-6_8
- Poole, M. S., & Folger, J. P. (1981). Modes of observation and the validation of interaction analysis
schemes. Small Group Behavior, 12, 477-493 https://doi.org/10.1177/104649648101200406
- Rodríguez-Rodríguez, I., & Heras-González, P. (2020). How are universities using Information and
Communication Technologies to face sexual harassment and how can they improve? Technology
in Society, 62. https://doi.org/10.1016/j.techsoc.2020.101274
- Savoldi, B., Gaido, M., Bentivogli, L., Negri, M., & Turchi, M. (2022). Under the morphosyntactic lens:
A multifaceted evaluation of gender bias in speech translation. arXiv: Computation and Language
(cs.CL), 1-18. https://doi.org/10.48550/arXiv.2203.09866
- Shah, D. S., Schwartz, H. A., & Hovy, D. (2020). Predictive biases in natural language processing
models: A conceptual framework and overview. In Proceedings of the 58th annual meeting of the
association for computational linguistics (pp. 5248–5264). Association for Computational
Linguistics.
- Marts, S. A. (2004). Interdisciplinary research is key to understanding sex differences: Report from the
Society for women's health research meeting on understanding the biology of sex differences.
Journal of Women's Health & Gender-Based Medicine, 11(6), 245-249.
https://doi.org/10.1089/152460902760277859
- Shields, S. A. (2002). Speaking from the heart: Gender and the social meaning of emotion. Cambridge
University Press.
- Smith, B. G. (2022). Kadın çalışmaları temeller (Özde Çakmak, Trans.). İletişim Yayınları.
- Swift, S., & Stillwell, E. (2015, April, 16-18). Gender disparities in the tech industry: The effects of
gender and stereotypicality on perceived environmental fit [Oral presentation]. National
Conference on Undergraduate Research (NCUR) 2015, Cheney, WA, ABD.
- UN (2015). Sustainable development goals. Retrieved December 24 2024 from https://sdgs.un.org/goals
- UNESCO Report. (2019). Artificial intelligence. Retrieved December 24 2024 from
https://en.unesco.org/ARTIFICIALINTELLIGENCE-and-GE-2020
- Vaismoradi, M., Jones, J., Turunen, H., & Snelgrove, S. (2016). Theme development in qualitative
content analysis and thematic analysis. Journal of Nursing Education and Practice, 6(5).
https://doi.org/10.5430/jnep.v6n5p100
- Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V. Domisch, S., Felländer, A., Langhans, S.
D., Tegmark, M., & Nerini, F. F. (2020). The role of artifcial intelligence in achieving the
sustainable development goals. Nature Communications, 11, 233. https://doi.org/10.1038/s41467-
019-14108-y
- Watkins, H., & Pak, R. (2020). Investigating user perceptions and stereotypic responses to gender and
age of voice assistants. Proceedings of the Human Factors and Ergonomics Society Annual
Meeting, 64(1), 1800–1804. https://doi.org/10.1177/1071181320641434
- Weber, R. P. (1990). Basic content analysis. CA: Sage
- Wood, W., & Ridgeway, C. L. (2010). Gender: An interdisciplinary perspective. Social Psychology
Quarterly, 73(4), 334-339. https://doi.org/10.1177/0190272510389005
- Yaşın-Dökmen, Z. (2016). Toplumsal cinsiyet sosyal psikolojik açıklamalar. Remzi Kitabevi.
Unveiling missing themes: An analysis of gender bias through artificial intelligence assisted bibliometric analysis
Yıl 2024,
Cilt: 2 Sayı: 2, 1 - 24, 31.12.2024
Senem Gürkan
,
Seçil Duran
,
Volkan Duran
Öz
This study explores the under-researched areas of gender bias foci in literature, employing AI and
bibliometric analysis to uncover overlooked themes. The periods and techniques of qualitative research paradigm
were conducted to the data set gathered from Web of Science Core Collection. The findings highlight the need for
more community-centric research and inclusive literature that address the existing disparities, emphasizing the
importance of mental health, diverse identities, and the reduction of biases in technology and media. Moreover,
this study underscores the necessity of aligning academic research with the actual concerns of communities to
effectively tackle gender bias and violence.
Kaynakça
- Agudo, U., Arrese, M., Liberal, K. G., & Matute, H. (2022). Assessing emotion and sensitivity of AI
artwork. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.879088
- Bhasin, K. (2003). Toplumsal cinsiyet “Bize yüklenen roller” (Kader Ay, Trans.). Kadınlarla Dayanışma
Vakfı Yayınları.
- Bora, A. (2021). Feminizm kendi arasında. İletişim Yayınevi
- Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016). Man is to computer programmer
as woman is to homemaker? Debiasing word embeddings. arXiv: Computation and Language
(cs.CL), 1-20. https://doi.org/10.48550/arXiv.1607.06520
- Caldas-Coulthard, C., & Moon, R. (2010). 'Curvy, Hunky, Kinky': Using corpora as tools for critical
analysis. Discourse & Society, 21(2), 99-133. https://doi.org/10.1177/0957926509353843
- Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric
analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
https://doi.org/10.1016/j.jbusres.2021.04.070
- EIGE (2020). Gender equality index, Retrieved December 24 2024 from https://eige.europa.eu/genderequality-index/2022
- Font, J. E., & Costa-Jussa, M. R. (2019). Equalizing gender biases in neural machine translation with
word embedding techniques. Xiv preprint arXiv:1901.03116.
https://doi.org/10.48550/arXiv.1901.03116
- González, A. S., & Rampino, L. (2024). A design perspective on how to tackle gender biases when
developing AI-driven systems. AI Ethics. https://doi.org/10.1007/s43681-023-00386-2
- Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative
Health Research, 15, 1277-1288. https://doi.org/10.1177/1049732305276687
- Jones, Henry, A. J. I., Artikis, A., & Pitt, J. (2013). The design of intelligent sociotechnical systems.
Artificial Intelligence Review, 39, 5–20. https://doi.org/10.1007/s10462-012-9387-2
- Kaplan, C., & Grewal, I. (2002). Transnational practices and interdisciplinary feminist scholarship:
Refiguring women’s and gender studies (Robyn Wiegman, Inderpal Grewal & Caren Kaplan,
Eds.) In Women's studies on its own: A next wave reader in institutional change (pp. 66-81). Duke
University Press. https://doi.org/10.1515/9780822384311-003
- Leavy, S. (2018). Gender bias in artifcial intelligence: The need for diversity and gender theory in
machine learning. In Proceedings of the 1st international workshop on gender equality in software
engineering (pp. 14–16). ACM.
- Lindgren, S., & Holmström, J. (2020). Social science perspective on artifcial intelligence. Journal of
Digital Social Research (JDSR), 2(3), 1-15. https://doi.org/10. 33621/jdsr.v2i3.65
- Litosseliti, L., & Sunderland, J. (2002). Gender identity and discourse analysis. John Benjamins
Publishing.
- Lu, D., Neves, L., Carvalho, V., Zhang, N., & Ji, H. (2018). Visual attention model for name tagging in
multimodal social media. In Proceedings of the 56th Annual Meeting of the Association for
Computational Linguistics (Volume 1: Long Papers) (pp. 1990–1999). Association for
Computational Linguistics.
- Machimbarrena, J. M., Calvete, E., Fernández-González, L., Álvarez-Bardón, A., Álvarez- Fernández,
L., & González-Cabrera, J. (2018). Internet risks: An overview of victimization in cyberbullying,
cyber dating abuse, sexting, online grooming and problematic internet use. International Journal
of Environmental Research and Public Health. 15(11), 2471.
https://doi.org/10.3390/ijerph15112471
- Matthews, A., Grasso, I., Mahoney, C., Chen, Y., Wali, E., Middleton, T., Njie, M., & Matthews, J.
(2021, June). Gender bias in natural language processing across human languages In (Y.
Pruksachatkun, A. Ramakrishna, K.-W. Chang, S. Krishna, J. Dhamala, T. Guha, & X. Ren, Eds.)
Proceedings of the first workshop on trustworthy natural language processing (pp. 45–54).
Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.trustnlp-1.6
- Motschenbacher, H. (2013). Gentlemen before ladies? A corpus-based study of conjunct order in
personal binomials. Journal of English Linguistics, 41(3), 212-242.
- Pathak, S., Solanki, V. K., & Linh, N. T. D. (2024). Gender biasness – a victim of artificial intelligencebased development (Mishra, D., Ngoc Le, A., & McDowell, Z., Eds.) In Communication
technology and gender violence. Signals and communication technology. Springer, Cham.
https://doi.org/10.1007/978-3-031-45237-6_8
- Poole, M. S., & Folger, J. P. (1981). Modes of observation and the validation of interaction analysis
schemes. Small Group Behavior, 12, 477-493 https://doi.org/10.1177/104649648101200406
- Rodríguez-Rodríguez, I., & Heras-González, P. (2020). How are universities using Information and
Communication Technologies to face sexual harassment and how can they improve? Technology
in Society, 62. https://doi.org/10.1016/j.techsoc.2020.101274
- Savoldi, B., Gaido, M., Bentivogli, L., Negri, M., & Turchi, M. (2022). Under the morphosyntactic lens:
A multifaceted evaluation of gender bias in speech translation. arXiv: Computation and Language
(cs.CL), 1-18. https://doi.org/10.48550/arXiv.2203.09866
- Shah, D. S., Schwartz, H. A., & Hovy, D. (2020). Predictive biases in natural language processing
models: A conceptual framework and overview. In Proceedings of the 58th annual meeting of the
association for computational linguistics (pp. 5248–5264). Association for Computational
Linguistics.
- Marts, S. A. (2004). Interdisciplinary research is key to understanding sex differences: Report from the
Society for women's health research meeting on understanding the biology of sex differences.
Journal of Women's Health & Gender-Based Medicine, 11(6), 245-249.
https://doi.org/10.1089/152460902760277859
- Shields, S. A. (2002). Speaking from the heart: Gender and the social meaning of emotion. Cambridge
University Press.
- Smith, B. G. (2022). Kadın çalışmaları temeller (Özde Çakmak, Trans.). İletişim Yayınları.
- Swift, S., & Stillwell, E. (2015, April, 16-18). Gender disparities in the tech industry: The effects of
gender and stereotypicality on perceived environmental fit [Oral presentation]. National
Conference on Undergraduate Research (NCUR) 2015, Cheney, WA, ABD.
- UN (2015). Sustainable development goals. Retrieved December 24 2024 from https://sdgs.un.org/goals
- UNESCO Report. (2019). Artificial intelligence. Retrieved December 24 2024 from
https://en.unesco.org/ARTIFICIALINTELLIGENCE-and-GE-2020
- Vaismoradi, M., Jones, J., Turunen, H., & Snelgrove, S. (2016). Theme development in qualitative
content analysis and thematic analysis. Journal of Nursing Education and Practice, 6(5).
https://doi.org/10.5430/jnep.v6n5p100
- Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V. Domisch, S., Felländer, A., Langhans, S.
D., Tegmark, M., & Nerini, F. F. (2020). The role of artifcial intelligence in achieving the
sustainable development goals. Nature Communications, 11, 233. https://doi.org/10.1038/s41467-
019-14108-y
- Watkins, H., & Pak, R. (2020). Investigating user perceptions and stereotypic responses to gender and
age of voice assistants. Proceedings of the Human Factors and Ergonomics Society Annual
Meeting, 64(1), 1800–1804. https://doi.org/10.1177/1071181320641434
- Weber, R. P. (1990). Basic content analysis. CA: Sage
- Wood, W., & Ridgeway, C. L. (2010). Gender: An interdisciplinary perspective. Social Psychology
Quarterly, 73(4), 334-339. https://doi.org/10.1177/0190272510389005
- Yaşın-Dökmen, Z. (2016). Toplumsal cinsiyet sosyal psikolojik açıklamalar. Remzi Kitabevi.