Bridging the Divide: Gender, Diversity, and Inclusion Gaps in Data Science and Artificial Intelligence Across Academia and Industry in the majority and minority worlds
- URL: http://arxiv.org/abs/2511.18558v1
- Date: Sun, 23 Nov 2025 18:09:31 GMT
- 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
- Authors: Genoveva Vargas-Solar,
- Abstract summary: This chapter examines the participation of women and minorities in AI and DS, focusing on their representation in both industry and academia.<n>The dominance of men in AI and DS reinforces gender biases in machine learning systems, creating a feedback loop of inequality.<n>This imbalance is a matter of social and economic justice and an ethical challenge, demanding value-driven diversity.
- Score: 0.5076419064097732
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Enduring Disparities in the Workplace: A Pilot Study in the AI Community [3.4307685264019256]
We conducted a pilot survey of 1260 AI/ML professionals both in industry and academia across different axes.<n>Results indicate enduring disparities in workplace experiences for underrepresented and/or marginalized subgroups.<n>We highlight that accessibility remains an important challenge for a positive work environment and that disabled employees have a worse workplace experience than their non-disabled colleagues.
arXiv Detail & Related papers (2025-06-04T17:40:36Z) - An Empirical Study on the Impact of Gender Diversity on Code Quality in AI Systems [2.2160604288512324]
Underrepresentation of women in software engineering raises concerns about robustness in AI development.<n>This study examines how gender diversity within AI teams influences project popularity, code quality, and individual contributions.
arXiv Detail & Related papers (2025-05-06T00:37:27Z) - Exploring the Role of Women in Hugging Face Organizations [46.84136061744368]
Women are highly underrepresented in both organizations and commits distribution.<n>Addressing gender disparities is essential to create more equitable, diverse, and inclusive open-source ecosystems.
arXiv Detail & Related papers (2025-03-21T10:06:52Z) - GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases.<n>GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
arXiv Detail & Related papers (2024-08-22T15:35:46Z) - Thinking beyond Bias: Analyzing Multifaceted Impacts and Implications of AI on Gendered Labour [1.5839621757142595]
This paper emphasizes the need to explore AIs broader impacts on gendered labor.
We draw attention to how the AI industry as an integral component of the larger economic structure is transforming the nature of work.
arXiv Detail & Related papers (2024-06-23T20:09:53Z) - Social Diversity Reduces the Complexity and Cost of Fostering Fairness [63.70639083665108]
We investigate the effects of interference mechanisms which assume incomplete information and flexible standards of fairness.
We quantify the role of diversity and show how it reduces the need for information gathering.
Our results indicate that diversity changes and opens up novel mechanisms available to institutions wishing to promote fairness.
arXiv Detail & Related papers (2022-11-18T21:58:35Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z) - Investigating Participation Mechanisms in EU Code Week [68.8204255655161]
Digital competence (DC) is a broad set of skills, attitudes, and knowledge for confident, critical and use of digital technologies.
The aim of the manuscript is to offer a detailed and comprehensive statistical description of Code Week's participation in the EU Member States.
arXiv Detail & Related papers (2022-05-29T19:16:03Z) - How diverse is the ACII community? Analysing gender, geographical and
business diversity of Affective Computing research [0.0]
ACII is the premier international forum for presenting the latest research on affective computing.
We measure diversity in terms of gender, geographic location and academia vs research centres vs industry, and consider three different actors: authors, keynote speakers and organizers.
Results raise awareness on the limited diversity in the field, in all studied facets, and compared to other AI conferences.
arXiv Detail & Related papers (2021-09-12T18:30:36Z) - Implicit Gender Bias in Computer Science -- A Qualitative Study [3.158346511479111]
Gender diversity in the tech sector is sufficient to create a balanced ratio of men and women.
For many women, access to computer science is hampered by socialization-related, social, cultural and structural obstacles.
The lack of contact in areas of computer science makes it difficult to develop or expand potential interests.
arXiv Detail & Related papers (2021-07-04T13:30:26Z) - MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
We study multi-group fairness in machine learning (MultiFair)
We propose a generic end-to-end algorithmic framework to solve it.
Our proposed framework is generalizable to many different settings.
arXiv Detail & Related papers (2021-05-24T02:30:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.