Increasing AI Explainability by LLM Driven Standard Processes
This addresses the need for reliable and interpretable AI-supported decision-making, though it appears incremental by integrating existing methods.
The paper tackles the problem of AI explainability by embedding Large Language Models (LLMs) into standardized processes like QOC and Sensitivity Analysis, transforming opaque inference into transparent decision traces. Empirical evaluations show the system can reproduce human-level decision logic in contexts like decentralized governance.
This paper introduces an approach to increasing the explainability of artificial intelligence (AI) systems by embedding Large Language Models (LLMs) within standardized analytical processes. While traditional explainable AI (XAI) methods focus on feature attribution or post-hoc interpretation, the proposed framework integrates LLMs into defined decision models such as Question-Option-Criteria (QOC), Sensitivity Analysis, Game Theory, and Risk Management. By situating LLM reasoning within these formal structures, the approach transforms opaque inference into transparent and auditable decision traces. A layered architecture is presented that separates the reasoning space of the LLM from the explainable process space above it. Empirical evaluations show that the system can reproduce human-level decision logic in decentralized governance, systems analysis, and strategic reasoning contexts. The results suggest that LLM-driven standard processes provide a foundation for reliable, interpretable, and verifiable AI-supported decision making.