Artificial Intelligence and Gender Inequality: Mitigation Strategies and Governance Insights from Qatar

Öz

This research examines the complex relationship between artificial intelligence (AI) systems and gender inequality, focusing on technical and sociological dimensions. This paper analyzes current literature and empirical studies and investigates how AI technologies influence gender disparities in workforce participation, skill acquisition, and algorithmic decision-making. Four key bias mitigation strategies are examined: down-sampling, gender-aware hyperparameter tuning, up-sampling, and probabilistic gender proxy modeling, evaluating their effectiveness in reducing discrimination. AI systems can reinforce gender biases, but if designed and regulated properly, they can also promote equality. Effective bias mitigation requires a combination of technical solutions, regulatory frameworks, and institutional policies. The paper includes a case study on Qatar's initiatives to advance gender equality through economic and technological empowerment, suggesting a national strategy that addresses challenges and leverages opportunities in line with Qatar's National Vision 2030. The research investigates how such a strategy could enhance women's economic and digital participation while addressing structural barriers and cultural factors. By utilizing international standards alongside local context, this study offers a policy development framework that aligns with Qatar's development goals while fostering sustainable and inclusive economic growth. The paper concludes with recommendations for creating AI systems that support gender equality.