Explainable AI for Compliance and Regulatory Models
DOI:
https://doi.org/10.36676/jrps.v11.i4.1584Keywords:
Explainable AI, compliance models, regulatory frameworks, transparency, interpretability, accountabilityAbstract
The increasing complexity of compliance and regulatory frameworks across industries demands innovative solutions for managing and interpreting large volumes of data. Explainable Artificial Intelligence (XAI) offers a promising approach by providing transparent and interpretable AI models that can be utilized for compliance and regulatory decision-making. Traditional AI systems, often viewed as "black boxes," have been met with scepticism due to their opacity, especially in high-stakes domains like finance, healthcare, and legal sectors, where accountability and trust are paramount. XAI addresses these challenges by making the decision-making process more transparent, enabling stakeholders to understand the logic behind AI-driven recommendations and actions.
In regulatory environments, XAI can be used to explain the rationale behind risk assessments, fraud detection, or legal interpretations, thus ensuring compliance with laws and policies. Moreover, the integration of XAI into compliance models enhances auditability and traceability, providing regulators and auditors with the tools to validate and verify the adherence to standards. This transparency is crucial for building trust in AI systems and fostering collaboration between human decision-makers and AI tools.
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