Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Emerging leaders or persistent gaps? Generative AI research may foster women in STEM
13
Zitationen
6
Autoren
2024
Jahr
Abstract
The primary aim of this study is to explore the gender dynamics within Generative AI (GAI) research through an analysis of 5092 publications post the inception of ChatGPT in November 2022. This investigation seeks to comprehend how gender distribution varies across different research areas and geographical locations in GAI, assess thematic differences in contributions by male and female authors, identify areas of significant female activity, evaluate the impact of policies on gender dynamics, devise strategies to enhance gender diversity and explore how GAI research can propel women's advancement in STEM. The study reveals a dual narrative : a promising rise in female leadership within highly cited GAI research papers, juxtaposed with ongoing gender disparities in single-authored works and primary authorship positions. These findings underscore the unique position of GAI as distinct from traditional STEM fields, owing to its integration of technology with societal demands and its potential to foster increased female participation in STEM through its interdisciplinary approach and societal impact . By aligning with Sustainable Development Goal 5, this research champions inclusive development practices and policy reforms aimed at bolstering gender diversity and inclusivity within GAI research. The insights derived from addressing the research questions show how GAI can make STEM fields more inclusive, diverse, and attuned to societal needs, thereby maximizing the value of female researchers' contributions. • Generative AI (GAI) has potential to advance women in STEM. • Dual narrative: Rising female authorship vs. gender disparities in GAI. • Women's leadership evident in highly cited Generative AI papers • Gender imbalance in authorship roles, with men outnumbering women • Significant female contributions in ethical and educational AI applications
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.402 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.270 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.702 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.507 Zit.