INTEGRATING ARTIFICIAL INTELLIGENCE WITH HEALTH, SAFETY, AND ENVIRONMENTAL PRACTICES: A COMPARATIVE ANALYSIS BETWEEN MALE AND FEMALE WORKERS IN PAKISTAN

Abstract

Background: Artificial intelligence (AI) is increasingly used to strengthen health, safety, and environmental (HSE) management through predictive analytics, real-time monitoring, and automated compliance. Evidence on demographic differences in AI uptake—particularly gender differences—in developing countries such as Pakistan is limited. Objectives: To (1) describe demographic characteristics of workers engaged in AI–HSE practices, (2) compare male and female representation across management levels, (3) examine gender differences in mean scores for AI integration in safety, health, and environmental settings, and (4) identify demographic and professional predictors of AI practices in HSE settings. Methods: A cross-sectional survey (purposive sampling) was administered to professionals (n = 350) from construction, manufacturing, healthcare and related industries. Instruments included a demographic sheet, the AI in Workplace Safety Scale (AIWSS) and an HSE Management Practices Scale (5-point Likert). Data were analyzed in SPSS using descriptive statistics, cross-tabulations, independent-samples t-tests, and multiple linear regression. Results: The sample comprised professionals across several industries with predominance of middle management. Women were underrepresented in management roles relative to men. Females reported higher mean scores for AI integration in safety settings (M = 117.32, SD = 23.83) than males (M = 111.51, SD = 18.61); differences in health and environmental settings were negligible. Multiple regression (n = 350) showed education was the only significant predictor of AI practices (B = 7.97, p < .001); gender, age, experience and management level were not significant predictors. Conclusion: Educational attainment is the primary enabler of AI adoption in HSE settings. Although women are underrepresented in managerial positions, they report stronger engagement with AI in safety applications. Policies should prioritize education and targeted training, promote gender-inclusive leadership pathways, and invest in organizational readiness to realize equitable AI-driven safety gains.