#Model Design

Articles about Model Design — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with model design. ← All posts

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Pratik Dhanave · ·7 min read

Intrinsic vs Post-Hoc — Two Paths to an Explainable Model

There are exactly two ways to get an explainable model: build one that's transparent from the start, or build a black box and explain it afterward. These two paths — intrinsic interpretability and post-hoc explanation — define the entire field, and choosing between them is the first real decision in any project where understanding matters. This post lays out both, their trade-offs, and why the choice is less obvious than "just use the accurate model and explain it later."

Two paths to an explainable model: build one transparent by design (intrinsic — faithful, free explanations) or explain a black box afterward (post-hoc — keeps accuracy but explanations can be unfaithful). The real debate: for high-stakes problems where an interpretable model is competitive, prefer intrinsic over explaining a black box.

#Explainable AI#Interpretability#Machine Learning#Model Design

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.