Abstract
The expanding use of artificial intelligence (AI) in decision-making and safety raises questions about its openness, accountability, and reliability. Current artificial intelligence models, especially those based on deep learning architectures, excel at forecasting, but their opaque decision-making processes make them unreliable for autonomous agent systems. Improving AI models through data science-driven methodologies is the focus of this research, which aspires to produce trustworthy, explicable, and auditable autonomous agents. Including interpretability techniques into AI pipelines and improving their performance is the focus of this study. Some examples include feature attribution, surrogate modeling, model-agnostic explanations, and others. Studies have shown that autonomous agents, particularly in dynamic and uncertain environments, need to be able to see their surroundings and make defensible decisions in real time. This work links human comprehension with machine intelligence using a comprehensive framework that integrates interpretability and reliability metrics. The ways in which interpretable representations help autonomous systems with adaptive learning, fault diagnostics, bias detection, and resilience are investigated in this work. Interpretability-enhanced AI systems improve safety, human-AI collaboration, and regulatory compliance, according to conceptual modeling and analysis. The findings indicate that trustworthy autonomous agents must adhere to interpretability as a design criterion. This paper advocates transparent intelligence data science methods for responsible AI system deployment in autonomous real-world applications.