The surging deployment of Artificial Intelligence (AI) systems across safety-critical sectors — namely healthcare diagnostics, autonomous driving, aircraft regulation, and industrial automation — has resulted in a burgeoning need for transparent decision-making frameworks capable of justifiability and accountability. This paper describes a holistic architectural paradigm for constructing Explainable Artificial Intelligence (XAI) systems which provide sufficient reasoning transparency in mission-critical settings, where the failure of a system may have disastrous outcomes. We examine the limitations of black-box AI today and introduce a layered explainability framework that merges post-hoc explanation methods with attention processes and methodologies for synthetic formation of human personified I/O logic rules. It builds on SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations) and counterfactual reasoning to provide fine-grained, context-aware explanations for model predictions. We also align with regulatory compliance needs, including the EU AI Act and FDA guidelines, making explainability an integral part of our design process rather than a standalone consideration. We conduct extensive experimental evaluations over medical imaging, autonomous driving, and fault detection datasets, showcasing that our architecture yields comparable predictive accuracy while substantially improving interpretability scores. In summary, this work addresses the key challenge of the disparity between AI performance and human trust by providing a solid underpinning for responsible deployment of AI in life-critical settings.
Sajjan Choudhuri, A. Agade, Rahul Reddy Gouravaram et al.· 2026 International Conferenc...· 0 citations
In fast-evolving software systems, effective 'natural language requirements parsing' and downstream change effect analysis capability across a multitude of codes represents low-hanging-fruit in this regard. We present a structured framework to deploy Large Language Models (LLMs) for automating two essential software engineering tasks, namely requirement interpretation and change impact analysis Utilizing the inherent understanding of semantics offered by transformer-based LLMs, the novel approach advances by converting vague and unstructured requirement documents into structured but machine-readable specifications to offer a direct traceability mapping from requirements to system components. Additionally, the framework leverages LLM-driven dependency analysis to predict and quantify how change effects percolate through connected modules which can minimize manual effort and human errors. This approach combines prompt engineering and retrieval-augmented generation (RAG) for domain-relevant accuracy plus fine-tuning techniques. On open-source and enterprise-grade software projects, experimental evaluations show that disambiguation accuracy, traceability precision, and change impact coverage of our approach are orders of magnitude better than state-of-the-art rule-based or static analysis tools. Notes: The results illustrate the application of LLMs at scale and demonstrate how these can alter software engineering workflows by removing bottlenecks (at a massive scale) at different stages of the software development lifecycle. In this research, we provide a generalizable pipeline that helps to bridge the gap from NLP advancements into practice for software lifecycle management.
Nithya Krishnan, Kumaran Ramanujam, Suresh Babu Narra et al.· 2026 International Conferenc...· 0 citations
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