Artificial Intelligence (AI) systems are increasingly used in high-stakes domains such as healthcare, finance, andlegal systems. Yet, their “black-box” nature poses challenges for accountability, trust, and regulatorycompliance. Explainable AI (XAI) seeks to make AI decisions transparent and interpretable to humans. Thispaper explores XAI as a mechanism to foster trust and ensure compliance with ethical and regulatory standards.It investigates how explanation design, user differences, and error regimes affect human trust and systemaccountability. A literature review of contemporary studies on explainability, trust calibration, and auditability ispresented. A mixed-methods methodology combining laboratory experiments, field deployments, andcompliance case studies is proposed. The results highlight that well-designed explanations can improve trustcalibration and audit confidence but may induce overreliance when poorly aligned with model accuracy. Thediscussion section outlines expected trade-offs and design implications. Ultimately, XAI is not merely atechnical enhancement but a socio-technical bridge connecting transparency, ethics, and compliance inresponsible AI systems.