Artificial Intelligence, Employment Inequality, and the Future of Inclusive Work

Introduction

Estimates suggest that artificial intelligence (AI) is cutting approximately 16,000 jobs per month from the U.S. economy (Lichtenberg, 2026). AI’s impact on the labor market is not exclusive to the U.S.; it is rather anticipated to cause major global labor market disruptions in the upcoming years. Using a sample of over 1,000 global employers across 55 countries and 22 industries, the World Economic Forum's (2025) Future of Jobs Report projects that by 2030, 22% of today's total jobs will be the result of job creation and job displacement. And, despite benefits attributable to AI in terms of job creation and productivity, substantial job losses are incurred (Wang & Wong, 2026). Meanwhile, automation, over the past four decades, has led to increased labor market polarization and put pressure on the wages of low- and medium-skilled workers (Lábaj et al., 2025).

Industrial sociologists have long recognized occupational segregation theory as a system of structural barriers that sort women and minority workers into jobs that are routine and require lower skill levels (Bergmann, 1971, 1974) and are most vulnerable to automation. Occupational segregation results from societal norms and stereotypes of “women’s work” combined with the devaluation of such work and systemic biases, such as hiring biases for both women and minority employees. The biases that precipitated occupational segregation have persisted within algorithmic AI hiring processes and in the disproportionate automation of jobs occupied by women and minorities, leading to job displacement (e.g., Johnson et al., 2025) while exacerbating demographic occupational inequalities.

Although training and reskilling initiatives are often proposed as a potential remedy (Malone et al., 2025), their effectiveness may be limited. The effective use of AI frequently requires advanced or specialized skills, making reskilling challenging for workers in low-skill occupations. Given that underrepresented minorities are disproportionately concentrated in these roles, they may face significant barriers to adapting to AI-driven job changes or overcoming displacement, even when training resources are available.

Thus, the primary goals of the proposed special issue are to (a) identify the disproportionately negative effects that AI may have on the job loss of minorities, (b) foster theory development and research on the impact of AI on minority job loss and job displacement, (c) review the existing research the topic, (d) consider strategies that can be used to help minorities overcome these potential problems, (e) present directions for future research and practice on the disparate effect of AI on the minorities' job-related opportunities. Empirical submissions employing rigorous methodologies are encouraged. Theoretical or conceptual submissions are also welcome if they entail novel contributions. In addition, given that research on AI's influence is limited to regions of developed countries or China (Ghosh et al., 2025) and that country-specific differences are expected to affect the extent of AI's impact on job loss, this special issue also invites empirical insights from other countries, beyond Western, educated, industrialized, rich, and democratic (WEIRD) countries.

List of Topic Areas

• Mitigation strategies for the adverse impacts of AI-driven unemployment for minorities. Are specific minorities particularly vulnerable to AI-driven job loss? If so, why and how can this be remedied?
• What occupations and/or job tasks, if automated, may especially penalize minorities?
• How might AI-based screening tools disproportionately filter out minority applicants at each stage in the hiring process?
• How perceptions of unfairness or bias in AI systems affect job-seeking behavior and organizational trust of underrepresented minorities.
• How might explainable AI tools (XAI) affect minorities' ability to contest decisions and reduce perceived discrimination?
• What is the role of reskilling programs in reducing unemployment gaps?
• Modeling how biased AI hiring decisions create feedback loops that reshape labor market data over time, reinforcing systemic exclusion.
• How might Human-AI Decision Hybrid Models reduce discriminatory outcomes in hiring, layoffs, or promotions?
• Psychological consequences of AI-driven job loss.
• Organizational Accountability Structures for AI Decisions.
• AI as a "Trojan Horse" where ostensibly neutral practices lead to disparate impacts.
• Does AI "identify" hidden identities that influence hiring or screening outcomes?
• Intersectionality issues in AI-enabled HR decisions.
• How does the presence of AI in hiring processes affect LGBTQ+ individuals' willingness to disclose their identity?
• Evaluation of AI approaches and their risk of bias toward underrepresented minorities.
• How might AI-enabled systems and data design unintentionally erase gender non-conforming identities?
• AI-Driven Job Displacement in occupations with higher representation of minorities.
• Barriers to reskilling of minorities (e.g., lack of access to resources, discrimination, lack of safe training environments).
• AI auditing approaches to reduce the risk of adverse impact.

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available at: https://mc.manuscriptcentral.com/edi 

Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/edi 

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key Deadlines

Opening date for manuscripts submissions: 01/07/2027

Closing date for manuscripts submission: 15/07/2027

Closing date for abstract submission: 31/01/2027

Email for submissions: [email protected]