Exploring the effects of women in Artificial Intelligence networks (WAINs) on women’s careers in Artificial Intelligence.

Abstract

The underrepresentation of women in the AI field is one of the causes of AI application biases needing resolution as part of the growing movement toward more ethical AI. One potential solution is to encourage more women to be involved in developing and deploying AI solutions, precipitating the growth of professional women in AI support networks. This research aimed to determine how women in AI networks contribute to women advancing and persisting in the AI workforce by developing and testing a model that links networking behaviour to women’s career persistence and advancement in AI. The study addressed the need for theoretical and empirical investigation into women in AI, networking behaviour, and the benefits networks offer to members, especially for their careers. Notably, there is a gap in the literature concerning formal women-only networks, with limited previous research on gender bias in the context of women in artificial intelligence networks (WAINs). The study followed a phased mixed method research process. First, a systematic review of AI gender bias was conducted, which supported the premise for increased gender diversity in AI. A conceptual model was then created from an intersection of multiple theories and models on gender in IT and networking, which a panel of women in AI experts reviewed, verified, and refined through in-depth interviews. The final phase tested the model propositions using structured equation modelling. The findings revealed that social support, opportunities and resources provided by professional WAINS contribute to the persistence and advancement of women in AI careers. This research provides an original contribution by suggesting a solution for improving gender diversity in AI development teams through the resources, opportunities and social support provided by WAINs. The research also contributes to a better understanding of women’s careers and networking behaviour specifically in the AI field. With the proliferation of AI-based solutions and the integration of AI into automated decision-making, reducing the gender gap in the AI workforce is more important than ever. Recommendations include active support for WAINs by businesses and policy bodies, while WAIN organisers and women in AI should co-create career enhancing resources and support in WAINs.