Redundancy as the Shadow of Explainability: A Trade-Off Principle for AI-Intensive Systems
Yan Liu, Jun Huang, Abdelwahab Hamou‐Lhadj, Zheng Li · 2026
Explainability has become a first-class requirement for AI-intensive systems, ensuring transparency, reproducibility, and accountability. However, explainability introduces a hidden burden. Pipelines accumulate redundant artifacts, including embeddings, checkpoints, attribution vectors, and logs. These artifacts support interpretability, but inflate storage, energy, and maintenance costs. Software engineering lacks principles, abstractions, and metrics to systematically quantify this redundancy cost or balance it against sustainability goals. In this paper, we argue that redundancy is not an isolated concern but the operational shadow of explainability. We propose a trade-off principle that frames explainability, redundancy, and sustainability as interdependent software quality attributes. Our methodology defines a redundancy cost function that links explanatory fidelity to system resource consumption, and uses this function to guide a reference architecture for redundancy-aware explainable systems. The design is illustrated through a typed context memory framework, where artifacts are annotated with explanatory value and redundancy contribution, and managed through lifecycle controls that promote, demote, or retire artifacts. This work establishes new software engineering methods for building AI systems that remain interpretable, reproducible, and sustainable at scale.