A Feminist Analysis of Gender Bias in Artificially Created and Human News Articles
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Sackl, Stefanie
- dc.date.accessioned 2025-09-29T12:59:32Z
- dc.date.available 2025-09-29T12:59:32Z
- dc.date.issued 2025
- dc.description Treball de fi de màster Estudis Internacionals sobre Mitjans, Poder i Diversitat
- dc.description Supervisora: Pilar Medina-Bravo
- dc.description.abstract This analysis compares the different kinds of gender bias apparent in human-written and artificially created News articles. Focusing on the 2022 FIFA World Cup in Qatar, articles published by EuroNews on that topic get used to generate similar articles produced by ChatGPT, Perplexity, and DeepAI. By applying a feminist qualitative content analysis, the study explores how gendered language, stereotypes or forms of discrimination can manifest in both types of texts. While results show that both human and AI-generated articles reflect some forms of existing gender bias, the AI-generated texts offered the readers more context on how those inequalities could be dangerous for marginalized groups and included more diverse voices. The outputs by the different AI-models were generally less biased, most likely through content moderation and guidelines inside the algorithms. The findings highlight the mutual shaping of technology and society and urges for more transparency and diversity in AI development.
- dc.identifier.uri http://hdl.handle.net/10230/71289
- dc.language.iso eng
- dc.rights Llicència CC Reconeixement - No Comercial-Sense Obra Derivada 4.0 Internacional (CC BY-NC-ND 4.0)
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
- dc.subject.other Intel·ligència artificial
- dc.title A Feminist Analysis of Gender Bias in Artificially Created and Human News Articles
- dc.type info:eu-repo/semantics/masterThesis