Layer-Wise Explainability Graphs for Neural Architecture Self-Auditing

Abstract

Nowadays, enterprise AI functions within intricate governance scenarios in which business requirements, regulatory limitations, and operating interests need to intersect favorably. The Neural nets excel in discerning patterns and making correct predictions; yet they are failing to grasp organizational intent in overall terms often means that decisions may be correct in tactical terms, yet misaligned with desired holistic contexts. Current governance paradigms usually feature post-hoc auditing and rule override mechanisms that cannot cope efficiently in today's rapidly changing world. This work also innovates and proposes a framework known as Intent-Aware Neural Decision Systems (IANDS), which integrates explicit notions of business and governance intent in learning and decision-making by neural systems in a fundamental way. This innovation involves intent that is represented as high-level goals and priorities to govern decisions in a manner that is distinct from neural network accuracy alone. We conducted a series of experiments with simulated decision scenarios in business-like situations with compliance-critical and high-impact results. The data suggests a significant enhancement in intent awareness training in terms of how well it aligns with governance goals and reduces both violation and intervention burdens. The paper has kept things simple with visualizations that present an understanding of how intent can be aligned, how violation can be decreased, and how precision competes with other factors in an easy-to-understand manner, along with a series of real-time business cases in support of this approach. The study suggests that intent-aware systems provide a governance solution for the enterprise, scaling appropriately and in a proactive manner, to accurately and strategically implement AI. Keywords: Intent-Aware Neural Decision Systems (IANDS), Artificial Intelligence (AI), Neural Network, Intent-Aware Neural Decision Systems