Context Stability
Bidirectional Filtering: Convenience vs Predictability
Bidirectional filters can make models easier to use, but less predictable for AI.
Rendering diagram...
A directional relationship with a backflow arrow showing ambiguity.
TL;DR
- • Bidirectional filters increase ambiguity.
- • Predictability matters more than convenience for AI.
The problem (layman)
- • Bidirectional filtering allows context to flow in both directions.
- • AI queries become harder to reason about.
Why it matters
- • Unexpected filter paths create inconsistent answers.
- • Explainability suffers when context is unclear.
Symptoms
- • Slicer changes affect unrelated metrics.
- • AI answers shift when extra dimensions are included.
Root causes
- • Bidirectional filtering used to “fix” reports quickly.
- • Lack of model governance.
What good looks like
- • Directional relationships with documented filter paths.
- • Bidirectional filters used only when necessary.
How to fix (steps)
- • Audit all bidirectional relationships.
- • Replace with bridge tables or single‑direction filters.
- • Document any remaining bidirectional cases.
Pitfalls
- • Leaving bidirectional filters in place because they seem harmless.
- • Assuming AI can infer intent.
Checklist
- • Bidirectional filters minimized.
- • Filter paths documented.
- • Context tests pass.