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 paper examines gender and diversity disparities in AI and Data Science across academia and industry, highlighting inequalities exacerbated by COVID-19 and proposing strategies for promoting equity and inclusion.
Contribution
It provides a comprehensive analysis of participation gaps and offers actionable strategies to enhance diversity and inclusion in AI and Data Science fields.
Findings
Women and minorities are underrepresented in AI and DS sectors.
COVID-19 worsened existing gender and diversity disparities.
Dominance of men reinforces biases in AI systems.
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…
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Taxonomy
TopicsEthics and Social Impacts of AI · Artificial Intelligence in Healthcare and Education · Diversity and Career in Medicine
