This paper provides a brief overview of the integration of artificial intelligence (AI) into feminist art practices, focusing on how AI technology critiques mainstream cultural narratives and reimagines gender representation. The study is based on a systematic review of peer-reviewed English-language literature from the Scopus and Google Scholar databases, identifying four core themes: technological application, cultural significance, ethical considerations, and creative transformation. Feminist artists utilize machine learning and generative algorithms to question authorship, identity, and systemic biases. While AI has opened new aesthetic possibilities and platforms for marginalized voices, it faces numerous challenges, including algorithmic bias, limited access to technology, and insufficient intersectional representation in datasets. Ongoing debates surrounding authorship, emotional authenticity, and data ethics further complicate the field. Despite growing academic attention since 2020, feminist-centered AI design and equitable collaboration models remain significantly lacking. This review concludes that AI holds significant transformative potential for feminist art, and future efforts should prioritize inclusivity, ethical design, decolonized data, and interdisciplinary collaboration.
Adam, A., & Furnival, C. (1995). Designing intersections—designing subjectivity: Feminist theory and praxis in a sex discrimination legislation system. Information & Communications Technology Law, 4, 161-173. https://doi.org/10.1080/13600834.1995.9965716
Ansari, A., Hoffmann, A., Gürses, S., Sloane, M., Vasquez, M., & Pearl, Z. (2021). Technology, equity and social justice roundtable. 1-2. https://doi.org/10.1109/istas52410.2021.9629208
Berryman, J. (2024). Creativity and Style in GAN and AI Art: Some Art-historical Reflections. Philosophy & Technology. https://doi.org/10.1007/s13347-024-00746-8
Braidotti, R. (2022). Posthuman feminism. Polity Press Cambridge.
Ciston, S. (2019). Intersectional AI Is Essential. Journal of Science and Technology of the Arts, 11, 3-8. https://doi.org/10.7559/citarj.v11i2.665
Deckker, D., & Sumanasekara, S. (2024). Systematic Review on AI in Gender Bias Detection and Mitigation in Education and Workplaces. International Journal of Research in Computing. https://doi.org/10.64701/ijrc/345/8905
Deepwell, K. (2019). Introduction, feminist art activisms and artivisms. https://consensus.app/papers/introduction-feminist-art-activisms-and-artivisms-deepwell/35889a2427d656b597eb788c4262ef66/
El Morr, C. (2024). The Need for a Feminist Approach to Artificial Intelligence. Proceedings of the AAAI Symposium Series, 4(1), 332-333. https://doi.org/10.1609/aaaiss.v4i1.31812
Falvo, F. R., & Cannataro, M. (2024). Ethics of Artificial Intelligence: challenges, opportunities and future prospects. 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 5860-5867. https://doi.org/10.1109/BIBM62325.2024.10822112
Han, Z. (2021). Combined Development of Artificial Intelligence and Art Design. 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), 1023-1026. https://doi.org/10.1109/ICSP51882.2021.9408896
Klein, L., & D’Ignazio, C. (2024). Data Feminism for AI. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3630106.3658543
Kun, P., Freiberger, M., Risi, S., & Løvlie, A. (2024). GenFrame: An Interactive Picture Frame Painting the Portrait of Any Girl. Companion Publication of the 2024 ACM Designing Interactive Systems Conference. https://doi.org/10.1145/3656156.3665437
Leavy, S., Siapera, E., & O’Sullivan, B. (2021). Ethical Data Curation for AI: An Approach based on Feminist Epistemology and Critical Theories of Race. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3461702.3462598
Meelberg, V., & Kurt, D. (2018). Artistic Creativity in Artificial Intelligence. https://consensus.app/papers/artistic-creativity-in-artificial-intelligence-meelberg-kurt/108a5200329851bc92946bc33cc036d7/
Morr, C. E. (2024). The Need for a Feminist Approach to Artificial Intelligence. Proceedings of the AAAI Symposium Series. https://doi.org/10.1609/aaaiss.v4i1.31812
Nejad, A. A., Schiphorst, T., & DiPaola, S. (2023). Art, Activism, and AI: Generating Visual Narratives of Injustice through Research-Creation with a Feminist Lens. 377-381. https://consensus.app/papers/art-activism-and-ai-generating-visual-narratives-of-dipaola-nejad/0827af0ed89751cb856ef210653153f4/
Nino, G., & Lisi, F. (2024). Exploring the Question of Bias in AI through a Gender Performative Approach. Sexuality and Gender Studies Journal. https://doi.org/10.33422/sgsj.v2i2.735
Nunes, R. (2024). AI, a Tool or an Author? A Posthuman Feminist Perspective on the Agency of Gen-AI in Creative Practices. Augmented Human Research. https://doi.org/10.1007/s41133-024-00074-8
Rakhmankulov, B. M. (2025). From Automatons to Neural Networks: A Historical and Cultural Analysis of Generative Art. ??????? ? ????????. https://doi.org/10.25136/2409-8744.2025.1.73146
Roth, J. (2018). Feminism Otherwise. Intersectionality beyond Occidentalism. 8. https://doi.org/10.4119/UNIBI/INDI-V8-I2-178
Sharma, P. (2024). Artificial Intelligence as a Catalyst for Gender Equality: Empowering Women across Healthcare, Education, and Entrepreneurship. Mind and Society. https://doi.org/10.56011/mind-mri-133-20245
Shrestha, S., & Das, S. (2022). Exploring gender biases in ML and AI academic research through systematic literature review. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.976838
Sliwinska, B. (2020). Feminist Visual Activism and the Body. https://doi.org/10.4324/9780429298615
Srinivasan, R., & Uchino, K. (2020). Biases in Generative Art: A Causal Look from the Lens of Art History. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3442188.3445869
Verma, P., & Priya, K. (2025). Feminism in Art: The Role of Women Artists in Addressing Gender Inequality, Body Politics, and Intersectional Issues Through Visual Narratives. Journal of Research in Humanities and Social Science. https://doi.org/https://doi.org/10.35629/9467-13013751
Victorin, K., Johnson, E., Mehrabi, T., Studies, G., & LiU. (2019). PRACTICALLY HUMAN Performing Social Robots and Feminist Aspects on Agency, Body and Gender. https://consensus.app/papers/practically-human-performing-social-robots-and-feminist-studies-victorin/d8c77e1fc18e5272bea8a7baf542499b/
Weinberg, L. (2022). Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches. J. Artif. Intell. Res., 74, 75-109. https://doi.org/10.1613/jair.1.13196
Zhou, E., & Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3. https://doi.org/10.1093/pnasnexus/pgae052
Zhou, M., Abhishek, V., Derdenger, T., Kim, J., & Srinivasan, K. (2024). Bias in Generative AI. ArXiv, abs/2403.02726. https://doi.org/10.48550/arXiv.2403.02726
Mao, Y., & Mokhtar, E. S. (2026). The Gendered Code: Exploring Feminist Art through Artificial Intelligence. International Journal of Academic Research in Business and Social Sciences, 16(2), 570–577.
Copyright: © 2026 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
