Analytical Explainability
Outliers and Null Semantics: When ‘Missing’ Means Something
Outliers and nulls can be meaningful; AI must interpret them correctly.
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Null can mean unknown, not applicable, or missing.
TL;DR
- • Missing data is not always zero.
- • Outliers can distort explanations.
The problem (layman)
- • Null values are treated as zeros or ignored.
- • Outliers skew explanations and trends.
Why it matters
- • Misinterpreting nulls leads to wrong conclusions.
- • Outliers can hide the real story.
Symptoms
- • AI says “no change” when data is missing.
- • Explanations are driven by a few extreme points.
Root causes
- • No null semantics documented.
- • No outlier handling in measures.
What good looks like
- • Null semantics defined (unknown vs not applicable).
- • Outlier handling rules documented.
How to fix (steps)
- • Document null meaning in metadata.
- • Add measures that exclude or flag outliers.
- • Explain when data is missing.
Pitfalls
- • Treating null as zero by default.
- • Silently excluding outliers.
Checklist
- • Null meaning documented.
- • Outlier handling implemented.
- • AI responses mention missing data.