Local Rule-Based Explanations of Black Box Decision Systems

When Algorithms Hide Their Reasons — A Simple Way to Hear Them

Many smart systems make choices but they often keep the reasons secret, and that can feel wrong or unsafe.
A new approach shows how to turn a locked-up decision into a clear, local story about that single case.
The trick is to build a small set of similar, made-up examples around the case and use a simple rule to copy the black box behavior nearby.
From that rule you get a plain explanation of why the decision was made, and also few counterfactuals — easy changes that would flip the outcome.
This method does not need to know how the original system works, it just learns close by, so it stays faithful to the real answer.
The approach use a smart search to craft the neighborhood, then reads off human-friendly rules, so people can ask better questions.
It helps spotting unfair or risky choices, and makes complex systems feel less mysterious, more transparent, and useful for everyday decisions.

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Local Rule-Based Explanations of Black Box Decision Systems

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