Identifying and Removing Barriers for Women to
Pursue Graduate Studies in Data Science and AI
Executive Summary

Women are significantly underrepresented in the booming fields of Data Science (DS) and Artificial Intelligence (AI) in the U.S., as well as in the graduate school programs that prepare students for careers in this field. Increasing the representation of women in DS/AI is critical: DS/AI represent tremendous opportunities for impact on society and wealth creation that should be accessible to all; and DS/AI also benefit greatly from a diversity of perspectives to reduce bias and increase equity in the products and solutions developed, used, and disseminated.

Women in Data Science (WiDS) conducted research to:

• Improve understanding of the current situation and trends related to the representation of women in the DS/AI workforce and in education.
• Improve understanding of the barriers women face when entering and studying in the academic graduate programs that feed the profession.
• Develop a suite of programmatic approaches to reduce barriers and increase women’s participation to a critical threshold.
  
  

Our experience indicates a theory of change where 30% representation of women is the threshold for creating a sense of belonging, full participation, and acceptance. WiDS aims to help increase the representation of women in DS/AI-related graduate programs to at least 30% by 2030 (30x30).

Key findings from our research include:

• Currently, more than 80% of U.S.-based data scientists hold graduate degrees. This seems to imply that successfully completing a DS/AI-focused graduate degree program is helpful for a career in these fields.
• U.S. women represent approximately 7% of master’s students in Computer Science graduate programs, which currently provide a substantial talent pool for DS/AI. Overall, between 55-65% of the women earning DS/AI-related advanced degrees are international students[1]. This indicates a strong vulnerability to fluctuations in international graduate student applications.
• The barriers women face in the pursuit and completion of DS/AI-related advanced degrees are significant, wide-ranging, and interrelated. They include:
  » Low awareness of data science pathways, value of graduate degree, and societal impact
  » Challenges related to self-efficacy, self-identity, and imposter phenomenon
  » Lack of (effective) faculty mentorship to pursue and thrive in graduate study
  » Insufficient skills development to prepare for graduate program admissions
  » Lack of family, peer, and community support
  » Perpetuation of gender bias and significant gender gaps in academia and the workplace
• The interdisciplinary nature of DS/AI provides a great opportunity to reach women across a wide range of undergraduate programs to broaden the DS/AI talent pool.
• The potential student pipeline from a broader range of undergraduate degrees is six times larger than the pipeline from engineering, computing, applied math, and statistics. Our goal is to provide insights and a vision for potential paths forward.
  
  

WiDS welcomes collaborators and partners to further develop and deploy programs and address outstanding research questions to remove barriers that women face to joining, thriving in, and leading in the DS/AI fields. The opportunity is too great, and the stakes too high to not take action.

Download the White paper