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* feat(skill): add interpretability workflow * fix(data-scientist): make the causal route clickable * docs(data-scientist): list all interpretation resources
34 lines
2.1 KiB
Markdown
34 lines
2.1 KiB
Markdown
# Interpretability workflow
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Interpretability is evidence about model behavior under a method and reference
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distribution. It is not automatically a causal explanation or a user-facing
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justification.
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1. State the decision, audience, stakes, and explanation target: global model
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behavior, local prediction, error diagnosis, fairness investigation, or a
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contrastive question. Define whether the audience needs a diagnostic view or
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an actionable explanation.
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2. Choose scope and background deliberately. Record the model, data version,
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reference population, feature preprocessing, missingness, and perturbation or
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intervention semantics. For local explanations, state the neighborhood and
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baseline; for global explanations, state the population and aggregation.
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3. Check correlated features, proxies, extrapolation, and distribution shift.
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A feature attribution may be shared among correlated variables or reflect a
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model shortcut. A perturbation may create impossible records and should be
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marked invalid rather than interpreted.
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4. Validate the explanation: rerun with seeds, nearby backgrounds, and relevant
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perturbations; use label or feature randomization sanity checks where
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applicable; compare at least one materially different method or model. Record
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disagreement instead of averaging it into false certainty.
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5. Inspect slices, especially protected groups and high-impact cases. An
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aggregate explanation or fairness result can hide a subgroup failure. Report
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sample sizes, uncertainty, missingness, and the limits of slice comparisons.
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6. Write the conclusion in predictive language unless a causal design identifies
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an intervention effect. Never turn “the model relied on X” into “X caused Y.”
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Route causal claims to [the causal-inference framework](causal-inference-framework.md).
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Explanation outputs belong in a versioned report with method, target, audience,
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background, stability results, method disagreement, slice results, and known
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failure modes. Do not expose sensitive feature values or internal reasoning just
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to make an explanation look complete.
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