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Bridging the Divide: Gender, Diversity, and Inclusion Gaps in Data Science and Artificial Intelligence Across Academia and Industry in the majority and minority worlds

Genoveva Vargas-Solar

TL;DR

This chapter addresses the persistent gender, diversity, and inclusion gaps in AI and DS workforce across academia and industry, with a focus on majority/minority world contexts and the COVID-19-impacted landscape. It proposes a curated, intersectional data collection framework and a quantitative analytics pipeline to map DEI gaps, including metrics like turnover, attrition, skill gaps, and occupational segregation. A use-case demonstrates the application of these metrics (Turnover Rate, Attrition Rate, Topic Shift Rate, Teaching Mobility Rate) to reveal gender disparities in career mobility and leadership access, informing targeted retention and policy interventions. The work emphasizes privacy-preserving data practices, cross-regional analyses, and actionable strategies—mentorship, inclusive hiring, governance, and collaborations between industry and academia—to foster a more representative and equitable AI/DS workforce.

Abstract

As Artificial Intelligence (AI) and Data Science (DS) become pervasive, addressing gender disparities and diversity gaps in their workforce is urgent. These rapidly evolving fields have been further impacted by the COVID-19 pandemic, which disproportionately affected women and minorities, exposing deep-seated inequalities. Both academia and industry shape these disciplines, making it essential to map disparities across sectors, occupations, and skill levels. The dominance of men in AI and DS reinforces gender biases in machine learning systems, creating a feedback loop of inequality. This imbalance is a matter of social and economic justice and an ethical challenge, demanding value-driven diversity. Root causes include unequal access to education, disparities in academic programs, limited government investments, and underrepresented communities' perceptions of elite opportunities. This chapter examines the participation of women and minorities in AI and DS, focusing on their representation in both industry and academia. Analyzing the existing dynamics seeks to uncover the collective and individual impacts on the lives of women and minority groups within these fields. Additionally, the chapter aims to propose actionable strategies to promote equity, diversity, and inclusion (DEI), fostering a more representative and supportive environment for all.

Bridging the Divide: Gender, Diversity, and Inclusion Gaps in Data Science and Artificial Intelligence Across Academia and Industry in the majority and minority worlds

TL;DR

This chapter addresses the persistent gender, diversity, and inclusion gaps in AI and DS workforce across academia and industry, with a focus on majority/minority world contexts and the COVID-19-impacted landscape. It proposes a curated, intersectional data collection framework and a quantitative analytics pipeline to map DEI gaps, including metrics like turnover, attrition, skill gaps, and occupational segregation. A use-case demonstrates the application of these metrics (Turnover Rate, Attrition Rate, Topic Shift Rate, Teaching Mobility Rate) to reveal gender disparities in career mobility and leadership access, informing targeted retention and policy interventions. The work emphasizes privacy-preserving data practices, cross-regional analyses, and actionable strategies—mentorship, inclusive hiring, governance, and collaborations between industry and academia—to foster a more representative and equitable AI/DS workforce.

Abstract

As Artificial Intelligence (AI) and Data Science (DS) become pervasive, addressing gender disparities and diversity gaps in their workforce is urgent. These rapidly evolving fields have been further impacted by the COVID-19 pandemic, which disproportionately affected women and minorities, exposing deep-seated inequalities. Both academia and industry shape these disciplines, making it essential to map disparities across sectors, occupations, and skill levels. The dominance of men in AI and DS reinforces gender biases in machine learning systems, creating a feedback loop of inequality. This imbalance is a matter of social and economic justice and an ethical challenge, demanding value-driven diversity. Root causes include unequal access to education, disparities in academic programs, limited government investments, and underrepresented communities' perceptions of elite opportunities. This chapter examines the participation of women and minorities in AI and DS, focusing on their representation in both industry and academia. Analyzing the existing dynamics seeks to uncover the collective and individual impacts on the lives of women and minority groups within these fields. Additionally, the chapter aims to propose actionable strategies to promote equity, diversity, and inclusion (DEI), fostering a more representative and supportive environment for all.

Paper Structure

This paper contains 27 sections, 16 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Women in economically promising IT domains
  3. AI and DS female workforce
  4. Challenges
  5. Towards a curated and intersectional data collection
  6. Existing Data
  7. Intersectionality and Inclusion in Workforce Data Collection
  8. Principles of Intersectional and Inclusive Data Collection
  9. Curation for designing intersectional women careers on IT dataset
  10. Data curation pipeline
  11. Analytics on AI and DS gender and DEI gap
  12. Quantitative methodology for measuring a DEI gap
  13. Diverging career trajectories
  14. Self-reported skills
  15. Job turnover and attrition rates.
  16. ...and 12 more sections

Figures (1)

  • Figure 1: Data curation pipeline