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Untitled work

(2001) · Cited by 13,930

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Abramski, K., Citraro, S., Lombardi, L., Rossetti, G., & Stella, M. (2023) · doi:10.31234/osf.io/27u6z · Cited by 4

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Dương, T. L. (2023) · Tập san Khoa học và kỹ thuật trường Đại học Bình Dương · doi:10.56097/binhduonguniversityjournalofscienceandtechnology.v6i2.160 · Cited by 3

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Jeong, S. (2023) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/3611683 · Cited by 3

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Lechner, F., Lahnala, A., WELCH, C. A., & Flek, L. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_067 · Cited by 7

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Chen, Y., Hu, Y., & Lu, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5389144 · Cited by 14

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Muses or Stereotypes? Identifying Historical Patterns of Sexism in a Corpus of Brazilian Lyrics

Lopes, J. N. D. S., Firmino, V. P., & dos Reis, V. Q. (2025) · Journal on Interactive Systems · doi:10.5753/jis.2025.5233 · Cited by 2

20Q: Overlap-Free World Knowledge Benchmark for Language Models

Bruyn, M. D., Lotfi, E., Buhmann, J., & Daelemans, W. M. P. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.46 · Cited by 2

Better Alone than in Bad Company. Addressing the Risks of Companion Chatbots Through Data Protection by DesignPreprint

Dewitte, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4773907 · Cited by 2

Addressing Gender Bias in Generative Large Language ModelsPreprint

Zhou, H., Inkpen, D. Z., & Kantarcı, B. (2024) · Research Square · doi:10.21203/rs.3.rs-4670889/v1 · Cited by 2

Promoción de juegos de azar en transmisiones en directo: Análisis del poder de prescripción de los streamers

Manjón, S. G., & Castillejo-De-Hoces, B. (2023) · methaodos revista de ciencias sociales · doi:10.17502/mrcs.v11i2.721 · Cited by 1

GENDER EQUALITY AND ARTIFICIAL INTELLIGENCE

Бадаревски, Б. (2023) · Годишен зборник на Филозофскиот факултет/The Annual of the Faculty of Philosophy in Skopje · doi:10.37510/godzbo2376805b · Cited by 1

Uso ético do ChatGPT na pesquisa acadêmica

dos Santos, A. A. L., Ramos, A. S. M., & de Andrade, A. P. V. (2025) · Gestão org · doi:10.51359/1679-1827.2025.266173 · Cited by 3

MUSE: A Multimodal, Generative, and Symbolic Framework for Human Experience Modeling

Hasan, M. R. (2025) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3716553.3750735 · Cited by 1

Rewriting Stories with LLMs: Gender Bias in Generated Portuguese-language Narratives

Silva, M. O., Brandão, M. A., & Moro, M. M. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5799 · Cited by 1

Constella: Supporting Storywriters’ Interconnected Character Creation through LLM-Based Multi-Agents

Park, S., Park, S., & Lim, Y.-K. (2026) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3796234 · Cited by 6

Social Biases in Language Models : Gender Stereotypes in GPT-3 Generated Stories

Lorentzen, B. (2022) · Uppsala University Publications (Uppsala University) · Cited by 2

A Close Reading Approach to Gender Narrative Biases in AI-Generated StoriesPreprint

Raffini, D., Macori, A., Angelini, M., & Catarci, T. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2508.09651

Da Propp al Prompt: i pregiudizi di genere nelle storie generate dai Large Language Model

Raffini, D., Macori, A., Angelini, M., & Catarci, T. (2026) · IRIS Research product catalog (Sapienza University of Rome)

Unexpected Gender Stereotypes in AI-generated Stories: Hairdressers Are Female, but so Are Doctors

Spillner, L. (2024) · Media (https://www.suub.uni-bremen.de/) · doi:10.26092/elib/4208 · Cited by 2

Uncovering Implicit Gender Bias in Narratives through Commonsense Inference

Huang, T., Brahman, F., Shwartz, V., & Chaturvedi, S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.326 · Cited by 24