Mitigating Western Cultural Bias in Large Language Models via a Role-Based Professional Ethical Framework in Pakistan's Freelance Economy

Large Language Models (LLMs) trained on predominantly Western corpora encode implicit assumptions aboutprofessional ethics, temporal autonomy, and individualistic boundary-setting that conflict with the survival-driven, collectivist realities of Pakistan's freelance workforce. This study introduces a role-based ethical promptingframeworkthatcontextualizes LLM outputs within Pakistan's macroeconomic stressors—currency devaluation, infrastructurefragility, andexclusion from global payment architectures. Through A/B testing of 30 professional dilemmas drawn fromPakistanifreelancer forums, we compare control (standard) LLM advice against role-primed responses. Results demonstrateaWestern Bias Index of 16.7% in control advice (characterized by decontextualized boundary-setting) versus 6.7%withrole-priming. Cultural Awareness of local stressors rose from 6.7% to 30.0%, and Pragmatic Strategy Rateincreased450% (from 13% to 76%), revealing a significant Guidance Gap in conventional AI ethics. We argue that socio-technicalmitigation—embedding cultural identity into prompt engineering—offers a scalable, low-cost interventionagainstWEIRD (Western, Educated, Industrialized, Rich, and Democratic) bias in AI alignment. Implications for HCI andAI ethicsinclude rethinking context-agnostic fairness metrics and advocating for geographically-aware model fine-tuning.