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Eksik temaların ortaya çıkarılması: Yapay zeka destekli bibliyometrik analiz yoluyla cinsiyetçi önyargının analizi

Yıl 2024, Cilt: 2 Sayı: 2, 1 - 24, 31.12.2024

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

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.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sosyal Psikoloji
Bölüm Araştırma Makalesi
Yazarlar

Senem Gürkan 0000-0002-2061-6385

Seçil Duran 0000-0003-2996-3786

Volkan Duran 0000-0003-0692-0265

Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 26 Ekim 2024
Kabul Tarihi 11 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 2 Sayı: 2

Kaynak Göster

APA Gürkan, S., Duran, S., & Duran, V. (2024). Unveiling missing themes: An analysis of gender bias through artificial intelligence assisted bibliometric analysis. Kahramanmaraş İstiklal Üniversitesi Psikoloji Dergisi, 2(2), 1-24.