Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent

LLMs can make complex causal reasoning more transparent: Unlike traditional "black box" AI systems, this approach uses a large language model to convert causal relationships into human-readable text that experts and stakeholders can actually read and verify, rather than hidden numerical patterns.

Explainability comes with trade-offs: The system intentionally removes weaker causal connections to make explanations more natural and understandable to humans, meaning some details are lost in the process of making the system more interpretable—a deliberate choice between perfect accuracy and practical usability.

AI systems can explain their own reasoning steps: Both the encoding (converting causal maps to text) and decoding (reconstructing from text) processes include explanations of how decisions were made, moving away from opaque decision-making toward systems where AI agents justify their choices.

Identity mapping without direct comparison is possible: The system reconstructs causal relationships through a sequence of instructions rather than by directly comparing outputs to inputs, suggesting new approaches to how AI systems can learn and validate their own reasoning without traditional supervised learning methods.

Responsible AI requires preserving important relationships: The system's design prioritizes maintaining strong causal edges while simplifying weaker ones, reflecting a principle that explainable AI should focus on the most consequential relationships rather than attempting to capture every detail.