Abstract
Causal Echo Binding, as proposed herein, is a next-generation explainable AI technique that traces "echoes" of causal influence backward through a model’s decision process, binding them to evolving, human-understandable concepts and validating each link with built-in counterfactual tests. Unlike static feature attribution methods, the technique produces a verifiable, time-aware narrative of why and when an AI system acted, enabling deeper trust and auditability. For a network equipment provider, this capability can enhance AI-driven networking, security, and observability products by providing transparent, regulator-ready explanations for automated decisions - from pinpointing the root cause of a network change to justifying a security alert.
Publication Date
2026-01-04
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Lanov, Dennis, "CAUSAL ECHO BINDING FOR EXPLAINABLE AI", Technical Disclosure Commons, (January 04, 2026)
https://www.tdcommons.org/dpubs_series/9121