Background: Artificial intelligence (AI) and automation are increasingly influencing workplace decision-making, particularly in recruitment, performance evaluations, and career progression.  While AI is often perceived as neutral, research highlights that these systems frequently replicate and amplify historical gender biases, disproportionately disadvantaging women and marginalized groups.  Existing AI fairness models primarily focus on generic algorithmic bias but fail to address gender-specific and intersectional discrimination. Additionally, corporate AI governance frameworks lack structured enforcement mechanisms, leading to reactive rather than proactive bias mitigation. 

Objective: This study aims to develop a structured framework for mitigating gender bias in AI-driven workplace automation.  It seeks to bridge the gap between AI development and ethical workforce practices by integrating fairness, accountability, and inclusivity into algorithmic decision-making. 

Methodology: A conceptual research design is adopted, synthesizing insights from AI fairness literature, gender studies, and corporate governance frameworks.  The study relies on secondary data sources, including peer-reviewed journal articles, industry reports, and case studies on AI-driven workplace discrimination.  Theoretical models such as Gender Role Theory, Algorithmic Bias Theory, and Intersectionality Theory inform the framework’s development. 

Proposed Model: The study introduces the G.E.N.D.E.R.  AI Framework as a structured approach to mitigating gender bias in AI-driven workplace automation.  This framework integrates six core components to ensure fairness, accountability, and inclusivity in algorithmic decision-making.  Governance and regulation serve as the foundation, establishing AI fairness policies and ensuring compliance with ethical and legal standards.  Equitable data training addresses biases embedded in historical datasets by implementing strategies to eliminate discriminatory patterns and promote balanced representation. Neutrality in algorithm design emphasizes fairness-aware programming and model transparency, ensuring that AI-driven systems do not reinforce systemic inequalities. Diversity in AI development teams plays a crucial role in reducing bias by incorporating inclusive perspectives in the design and deployment of AI technologies.  Evaluation and bias audits enable continuous monitoring of AI-driven decisions, facilitating early detection and correction of discriminatory patterns in hiring, performance assessments, and career progression. Lastly, responsible AI usage mandates human oversight in AI-powered employment decisions, ensuring that algorithmic recommendations are critically reviewed and do not replace human judgment in critical workplace determinations.  By integrating these principles, the G.E.N.D.E.R.  AI Framework provides a comprehensive, interdisciplinary model designed to promote gender-equitable AI governance and ethical automation in workforce management. 

Results: The framework provides a structured, interdisciplinary approach to embedding gender equity into AI decision-making.  It highlights key challenges in existing AI fairness models and offers actionable solutions for AI developers, HR professionals, and policymakers. 

Conclusion: As AI continues to shape workforce dynamics, it is critical to ensure that automation fosters inclusivity rather than reinforcing historical inequalities. The G.E.N.D.E.R.  AI Framework serves as a foundation for ethical AI governance, promoting gender fairness in workplace automation. Future research should focus on empirical validation, industry-specific adaptations, and the integration of explainable AI techniques to enhance fairness in AI-driven employment decisions.