Authors - MD Junayed Talukdar, Khosro Salmani Abstract - Artificial Intelligence (AI) systems are widespread across fields such as healthcare, finance, employment, and criminal justice, with a substantial impact on the lives of individuals and society. However, AI systems have been shown to perpetuate existing social inequalities, particularly through biases that are not easily discernible. Such biases are embedded in the technical and social structures of AI systems, posing a direct challenge to the principles of Equity, Diversity, and Inclusion (EDI) understood as the acknowledgment of differences among individuals, fairness and equal access, and the valuation of all participants. This study argues that fairness in AI cannot be achieved by focusing solely on technical aspects, necessitating a holistic approach. To investigate this, computational content analysis was applied to 360 occupational narratives generated by ChatGPT across nine professions and four geographic regions (Canada, Germany, India, and Bangladesh) using explicitly gender-neutral prompts. The analysis examined whether AI-generated narratives associate professions predominantly with one gender, and whether such patterns remain consistent across regions. Findings reveal that gender bias persists despite neutral prompting, with male-coded protagonists dominant in technical and manual labor professions and female-coded protagonists dominant in caregiving roles. Although regional conditions influenced the magnitude of gender imbalance, the direction of occupational gender patterns remained largely consistent across all four regions. This study identifies the empirical foundations necessary for future EDI-AI co-design frameworks, outlining the sociotechnical dimensions that such frameworks must address to be effective.
Open Zoom