Toward Trustworthy AI in Industry: An Integrated Framework for Interpretability, Fairness, and Human Accountability

AI-driven decision systems in high stakes industries leave patients, loan applicants, and workers with no visibility into decisions or meaningful recourse. Existing research treats interpretability, fairness, and accountability separately, leaving practitioners without a unified deployment framework. This paper proposes an integrated framework organizing trustworthy AI evaluation around three human-centered dimensions: interpretability for human oversight, contextual fairness evaluation, and human accountability architecture. Interpretability alone does not guarantee equitable outcomes, and fairness-audited models without explanation deny individuals recourse. Evaluating all three dimensions jointly provides a structured basis for responsible AI governance in healthcare, finance, public administration, and hiring, where the EU AI Act mandates human oversight of high-risk systems.