Building AI You Can Understand

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Author: Denis Avetisyan

Moving beyond explaining AI decisions, this review explores how to create inherently interpretable systems.

Despite the promise of Large Language Models to standardize processes through layered architectures, a fundamental barrier to explainability persists, inevitably obscuring the rationale behind automated decisions and creating a new form of technical debt.
Despite the promise of Large Language Models to standardize processes through layered architectures, a fundamental barrier to explainability persists, inevitably obscuring the rationale behind automated decisions and creating a new form of technical debt.

This paper proposes embedding Large Language Models within standardized analytical processes to enhance AI transparency, reasoning, and decision support.

Despite advances in artificial intelligence, a persistent challenge remains in understanding why AI systems make specific decisions. This paper, ‘Increasing AI Explainability by LLM Driven Standard Processes’, introduces a framework that addresses this by embedding Large Language Models within formalized analytical processes—such as decision modeling and risk assessment—to generate transparent, auditable reasoning traces. Empirical results demonstrate this approach yields AI systems capable of reproducing human-level logic in complex scenarios. Could this integration of LLMs and structured processes unlock a new era of reliable and verifiable AI-supported decision-making?

The Illusion of Intelligence: Why We Need to Look Under the Hood

Modern Deep Learning, particularly with Large Language Models, achieves impressive results. However, these models often operate as ‘black boxes’, obscuring their reasoning. This lack of transparency hinders trust and adoption, especially in critical applications requiring accountability. Fields like healthcare and finance demand explainable intelligence; validation and bias identification become exceedingly difficult without insight into a model’s decision-making.

An agent-based implementation demonstrates the QOC process.
An agent-based implementation demonstrates the QOC process.

Attempts to address this limitation, like attention mechanisms, offer superficial understanding and fail to capture complex interactions. The pursuit of truly interpretable AI remains a central, and likely futile, challenge.

Standard Processes: Herding Cats with LLMs

A proposed solution embeds Large Language Models (LLMs) within formalized analytical frameworks – ‘LLM-Driven Standard Processes’. This integration harnesses LLM reasoning while retaining control and ensuring transparency. This methodology moves beyond simple prompting, structuring LLM interactions around pre-defined analytical procedures.

This approach addresses the ‘Explainability Barrier’ by framing LLM responses within established methodologies, making the reasoning auditable and interpretable. Key frameworks include Question–Option–Criteria (QOC) analysis, Game Theory, and established Risk Management protocols. Implementation involves defining clear input structures, translating outputs into quantifiable data, and validating against benchmarks. This prioritizes methodological rigor over generative capabilities, offering a pathway towards incorporating LLMs into regulated analytical processes.

Validation: Can We Trust the Machine?

This research validates a novel approach to complex reasoning by applying Large Language Models (LLMs) to a Decentralized Autonomous Organization (DAO) evaluated with Quality of Consensus (QOC), and the Cuban Missile Crisis analyzed through Game Theory. The methodology employs LLM-driven standard processes to replicate human reasoning, subsequently assessing alignment with established ground truth benchmarks.

Quantitative results demonstrate significant alignment. QOC alignment increased from 0.529 to 1.000 over four steps, with 62.9% alignment in sensitivity analysis. Game Theory simulations indicated the LLM identified a viable de-escalation pathway in 93.3-95.3% of scenarios. Chain-of-Thought (CoT) prompting enhances reasoning transparency and facilitates validation, reproducing human-level performance across both case studies.

Explainable AI: A Thin Veneer of Understanding

Recent investigations demonstrate a viable pathway to reconcile LLM capabilities with the demand for explainability. This research establishes the feasibility of creating AI systems that are not only powerful but also transparent and accountable, addressing a significant limitation of contemporary models.

The integration of post-hoc explainable AI (XAI) techniques, like LIME and SHAP, further enhances interpretability. These techniques provide supplementary insights into model behavior, allowing for a more comprehensive understanding beyond the inherent transparency offered by the LLM. This layered approach enables detailed analysis and validation.

This hybrid methodology overcomes the constraints of opaque ‘black box’ models or labor-intensive rule-based systems. It represents a pragmatic step toward building AI that functions effectively and whose reasoning can be understood – a fleeting victory, perhaps, but a victory nonetheless.

The pursuit of inherently interpretable AI, as detailed in the paper, feels predictably optimistic. It’s a noble goal to embed Large Language Models within formalized processes, striving for transparency rather than post-hoc explanations. However, one suspects these ‘standard processes’ will inevitably accrue complexity. As G.H. Hardy observed, “A mathematician, like a painter or a poet, is a maker of patterns.” These patterns, when translated into production systems, rarely remain pristine. The elegance of a theoretically interpretable framework will, without fail, be compromised by the realities of scale and unforeseen edge cases. The drive for ‘Explainable AI’ often forgets that the most elaborate explanations become meaningless when applied to systems no one truly understands anymore.

What’s Next?

The ambition to embed Large Language Models within formalized processes is, predictably, a search for control. The assumption—that structure will tame the stochastic parrot—feels…familiar. It recalls every attempt to impose order on systems fundamentally resistant to it. The bug tracker will, inevitably, become a repository of edge cases where the ‘inherently interpretable AI’ reveals itself to be anything but. The real challenge isn’t producing explanations, but acknowledging the limits of what can be explained, and building systems that gracefully degrade when those limits are reached.

Future work will undoubtedly focus on scaling these formalized processes, on handling increasingly complex reasoning chains. This is, however, merely rearranging the deck chairs. The true test lies in deployment—in letting these systems operate in genuinely unpredictable environments. Multi-agent systems, in particular, present a fascinating, and likely frustrating, arena. The elegant reasoning of one agent will collide with the pragmatic compromises of another, and the resulting explanations will be…negotiated, at best.

The field chases ‘explainability’ as a feature. It should be viewed as a cost. Every line of explanation is a line of code not spent on solving the actual problem. The question isn’t whether an AI can explain itself, but whether it needs to, and whether the effort is worth the inevitable, and exquisitely detailed, postmortem. The systems don’t deploy—they let go.

Original article: https://arxiv.org/pdf/2511.07083.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-12 00:11