# Increasing AI Explainability by LLM Driven Standard Processes

arXiv: https://arxiv.org/abs/2511.07083 · HTML: https://mlsanity.com/paper/2511.07083/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [LLMs for Explainable AI: A Comprehensive Survey](https://mlsanity.com/paper/2504.00125/) (2025)
- [Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences](https://mlsanity.com/paper/2507.08440/) (2025)
- [Introducing δ-XAI: a novel sensitivity-based method for local AI explanations](https://mlsanity.com/paper/2407.18343/) (2024)
- [One for All: A General Framework of LLMs-based Multi-Criteria Decision Making on Human Expert Level](https://mlsanity.com/paper/2502.15778/) (2025)

## Built on by (most cited first, 1 of 1)

- [Large Language Models in the Abuse Detection Pipeline](https://mlsanity.com/paper/2604.00323/) (2026)

## Abstract (arXiv)

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.