This release adds 3 notable features for engineering teams evaluating rollout.
✓ No known CVEs patched in this version
Topics
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Summary
AI summaryOpen Dataset Profiler adds six enhancements including representation drift comparison, manual column mapping, reference‑population baselines, choosable intersections, proxy hints, and tunable thresholds.
Changes in this release
| Type | Severity | Summary | CVE |
|---|---|---|---|
| Feature | Medium |
Adds compare command to detect representation drift between datasets Adds compare command to detect representation drift between datasets Source: llm_adapter@2026-07-13 Confidence: high |
— |
| Feature | Medium |
Adds manual column mapping option for custom demographic columns Adds manual column mapping option for custom demographic columns Source: llm_adapter@2026-07-13 Confidence: high |
— |
| Feature | Medium |
Adds reference‑population baseline scoring against external data Adds reference‑population baseline scoring against external data Source: llm_adapter@2026-07-13 Confidence: high |
— |
| Feature | Medium |
Adds choosable intersection pair option for cross‑demographic analysis Adds choosable intersection pair option for cross‑demographic analysis Source: llm_adapter@2026-07-13 Confidence: high |
— |
| Feature | Medium |
Adds chi‑squared proxy hints with p‑values and Cramér's V (optional scipy extra) Adds chi‑squared proxy hints with p‑values and Cramér's V (optional scipy extra) Source: llm_adapter@2026-07-13 Confidence: high |
— |
| Feature | Medium |
Adds tunable threshold flags for share, intersection floor, imbalance, and missing data Adds tunable threshold flags for share, intersection floor, imbalance, and missing data Source: llm_adapter@2026-07-13 Confidence: high |
— |
Full changelog
Open Dataset Profiler — a major capability wave
This release rounds out the Open Dataset Profiler with six enhancements. All of
them run through one shared spec (faircode/SPEC.md), so the faircode CLI and
the in-browser profiler produce identical numbers.
⚖ Compare two datasets for representation drift (#60)
faircode compare train.csv prod.csv — or drop an A/B pair in the web profiler —
to see how each demographic dimension shifted. Reports the Population Stability
Index (PSI), Total Variation Distance, per-group share shifts, and
appeared/disappeared groups, flagging significant drift and score drops.
🗺 Manual column mapping (#62)
When auto-detection misses an oddly-named column (gndr, patient_region_code),
override it: faircode profile data.csv --map gndr=sex, or editable per-column
dropdowns on the web that re-run the audit instantly.
📊 Reference-population baseline (#56)
Score a dataset against an external population (e.g. Census age×sex), not just
internal balance: faircode profile data.csv --reference census.csv or a web
upload. Surfaces per-group expected-vs-actual deltas and flags
under-representation relative to who the model will actually serve.
✕ Choosable intersection pair (#58)
Cross any two demographic columns, not just the first two detected:
faircode profile data.csv --cross race,age or two dropdowns in the web UI.
🔗 Chi-squared proxy hints (#61)
faircode profile data.csv --proxy-hints flags strongly-associated column pairs
— a "this may be a proxy for a protected attribute" signal — with p-values and
Cramér's V. Opt-in via the optional scipy extra; informational, never scored.
🎚 Tunable thresholds (#63)
--min-share, --intersection-floor, --imbalance-flag, and --missing-flag
let auditors set their own bars without editing source.
Install: pip install -e ".[proxy]" for the chi-squared extra.
Full changelog: see CHANGELOG.md → [1.2.5].
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