Explainable AI & Trustworthy AI for Complianc...

Explainable AI & Trustworthy AI for Compliance and Regulatory Models

Author Name : Indra Reddy Mallela

ABSTRACT The increasing integration of Artificial Intelligence (AI) into compliance and regulatory frameworks has introduced unprecedented efficiencies but has also raised concerns about transparency, trust, and accountability. Explainable AI (XAI) and Trustworthy AI emerge as critical paradigms to address these challenges. XAI focuses on ensuring that AI models provide clear, interpretable, and justifiable outcomes, enabling stakeholders to understand decision-making processes. This is especially vital in regulatory environments where opaque "black-box" models can lead to compliance risks, misinterpretations, or legal challenges. Trustworthy AI complements this by emphasizing principles such as fairness, accountability, robustness, and ethical alignment. Together, these approaches enhance AI’s ability to operate within strict regulatory standards while fostering stakeholder confidence. This paper explores how XAI and Trustworthy AI can be effectively implemented in compliance models, discussing methodologies for ensuring interpretability, such as rule-based systems, surrogate models, and feature attribution techniques. It also examines strategies for building trust, including bias mitigation, adversarial testing, and adherence to ethical frameworks. Furthermore, the paper highlights real-world applications in sectors like finance, healthcare, and data privacy, showcasing how these paradigms contribute to transparent auditing, risk management, and decision validation. By integrating XAI and Trustworthy AI, organizations can create AI systems that not only meet regulatory requirements but also maintain ethical integrity and public trust. The convergence of these paradigms is presented as a pathway to achieving AI systems that are both innovative and compliant, setting the foundation for sustainable AI adoption in regulatory landscapes.