Increasing AI Explainability by LLM Driven Standard Processes

2025-11-10

AI Summary

Large language models (LLMs) suffer from opaque and non-auditable decision-making processes, hindering trust and regulatory compliance. Method: This paper proposes a hierarchical, explainable AI architecture that integrates LLMs with structured decision frameworks—including QOC (Question-Options-Criteria), sensitivity analysis, game-theoretic modeling, and risk management—thereby decoupling reasoning and explanation spaces. Unlike post-hoc interpretability methods, it enables prospective modeling of inference paths through standardized analytical workflows. Contribution/Results: It is the first work to systematically co-model classical decision science paradigms with LLMs, supporting end-to-end traceability and formal verification of decision logic. Experiments demonstrate that the system replicates expert-level reasoning in complex domains—including decentralized governance, systems analysis, and strategic planning—while significantly enhancing transparency, auditability, and trustworthiness of AI-driven decisions.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Humans and AI: Explainable AI (XAI) for Human UnderstandingPhilosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systemsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Enhancing AI explainability through standardized LLM integration
• Transforming opaque AI inference into transparent decision traces
• Establishing verifiable AI decision-making via structured reasoning frameworks

Innovation

• Embedding LLMs within standardized analytical processes
• Integrating LLMs into formal decision models like QOC
• Separating LLM reasoning from explainable process space