Bridging the Divide: Gender, Diversity, and Inclusion Gaps in Data Science and Artificial Intelligence Across Academia and Industry in the majority and minority worlds
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arXiv
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| Format: | Preprint |
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2025
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| author | Vargas-Solar, Genoveva |
| author_facet | Vargas-Solar, Genoveva |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18558 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging the Divide: Gender, Diversity, and Inclusion Gaps in Data Science and Artificial Intelligence Across Academia and Industry in the majority and minority worlds Vargas-Solar, Genoveva Computers and Society Databases 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. |
| title | Bridging the Divide: Gender, Diversity, and Inclusion Gaps in Data Science and Artificial Intelligence Across Academia and Industry in the majority and minority worlds |
| topic | Computers and Society Databases |
| url | https://arxiv.org/abs/2511.18558 |