rai-check-ml
Fairness, robustness, data quality, and production drift audits for tabular ML models.
from rai_audit.ml import ClassificationAudit, DriftAudit, RegressionAudit
DriftAudit compares a reference window with a current batch. It checks numeric
feature distributions, prediction distributions, sensitive-feature subgroup
composition, and classification error-rate changes per sensitive group. Numeric
drift evidence includes corrected KS p-values, population stability index, and
Jensen-Shannon divergence.
Classification fairness checks include equalized odds, calibration by group when probabilities are available, Wilson confidence intervals, and explicit warnings for undersized groups. Data-quality checks include common PII patterns, numeric outliers, and target-deterministic features.
Use split_data_quality_findings to catch entity overlap and exact duplicate rows
across train and test datasets:
from rai_audit.ml import split_data_quality_findings
findings = split_data_quality_findings(train, test, id_columns=["patient_id"])
See examples/ml_drift_monitoring/batch_monitor.py
and examples/mlops_integrations/ for monitoring examples.
1 maintainer
Metadata
1 maintainer
Release files for rai-check-ml 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| rai_check_ml-0.0.1.tar.gz | 26.8 kB | With provenance | Details for rai_check_ml-0.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| rai_check_ml-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for rai_check_ml-0.0.1-py3-none-any.whl |
Total release size: 54.4 kB
Release files / rai_check_ml-0.0.1.tar.gz
| Download URL | rai_check_ml-0.0.1.tar.gz |
|---|---|
| Size | 26.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
b31cbbb595d43bfdbe7d6960305b807e034a842d6bf2cba5cad342b6dc4f243f
|
|
BLAKE2b-256 checksum How to use checksums |
f4b2ced1a590e58d12fd448e50cc42524dec039f82f2a0943423f5a3cffe2b0b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 21, 2026.
Transparency logRelease files / rai_check_ml-0.0.1-py3-none-any.whl
| Download URL | rai_check_ml-0.0.1-py3-none-any.whl |
|---|---|
| Size | 27.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b447414759db4ff7be6f08cb843b532c0a72aba73ee87e24b04ca0b5dd48c83a
|
|
BLAKE2b-256 checksum How to use checksums |
99100b9c302a45099dc1edcd943d64a8efb953fbbc266340547f5aadf5f85dd4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 21, 2026.
Transparency logSecurity for rai-check-ml 0.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 2 files in this release have been uploaded with provenance metadata.
Attestation for rai_check_ml-0.0.1.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 21, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
