layerhow

Layerhow is a conceptual framework used to describe and organize explanations that emerge from layered systems. The approach emphasizes incremental explanations, where each layer adds a level of abstraction and reveals how inputs are transformed toward outputs. The term blends “layer” and “how” and is used in discussions of interpretability, modular design, and instructional modeling.

In practice, layerhow involves identifying the layers in a system, characterizing the function of each layer, and describing how the transitions between layers produce the observable result. This can include mapping inputs to intermediate representations, tracing influence across layers, and generating visual or textual summaries that aggregate layer contributions.

Applications include artificial intelligence interpretability, where layerhow helps explain decisions by attributing effects to specific neural-network layers or algorithmic stages; software architecture and debugging, where complex pipelines are broken into layers for easier maintenance; and education, where layered explanations help learners build understanding step by step.

Advantages include modularity, scalability of explanations, and potential for clearer accountability. Limitations involve potential oversimplification when layers interact nonlinearly, the risk of misattribution if layer boundaries are arbitrary, and added effort to construct and maintain layer-based explanations.

See also: explainable AI, modular design, hierarchical modeling.