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Your organization is deploying an AI-powered loan approval engine trained on historical application data. During model evaluation, the data science team discovers that loan decisions disproportionately favor applicants from particular ZIP codes, which correlate with socioeconomic factors. You are tasked with refining the model pipeline to mitigate potential ethical risks and regulatory violations related to geographic and demographic bias. Which responsible AI strategy should you prioritize to proactively detect and mitigate geographic bias in the model while aligning with fairness and ethical AI development frameworks?