fairpipe 0.9.1

Production-ready toolkit for fairness measurement, mitigation, and monitoring.

fairpipe

Fairness measurement, mitigation, monitoring, and pipeline tooling for ML workflows.
PyPI package: fairpipe · License: Apache-2.0 · Python 3.10+

	Fairlearn	AIF360	fairpipe
Metrics library	✅	✅	✅
Mitigation algorithms	✅	✅	✅
DataFrame I/O	✅	✅	✅
Parquet I/O	❌	❌	✅
Orchestrated end-to-end pipeline	⚠️ Partial	⚠️ Partial	✅
CI/CD integration	❌	❌	✅
GitHub Action	❌	❌	✅
Production monitoring	❌	❌	✅
REST API	❌	❌	✅

Fairlearn and AIF360 provide individual pre/in/post-processing components; fairpipe provides a YAML-configured baseline→transform→validate workflow with CI/CD exit codes.

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Install

pip install fairpipe

Optional extras: pip install 'fairpipe[api]' · 'fairpipe[training]' · 'fairpipe[monitoring]' · 'fairpipe[adapters]'
(REST API, PyTorch training helpers, dashboards/drift, Fairlearn/Aequitas backends.) Full detail is in the documentation below—not duplicated here.

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Documentation

Start here (hosted): Documentation — SvrusIO.github.io/fAIr
Built from this repo’s Sphinx sources; includes getting started, user guide, API reference, integration, performance, and security links.

In-repo references (for browsing on GitHub or a checkout):

Topic	Location
Getting started	docs/getting_started.md
User guide (long-form)	DOCS.md
API reference	docs/api.md
Playbook · fairpipe (as implemented)	docs/playbook-part-five-fairpipe.md
Integration guide	docs/integration_guide.md
Architecture / ADR	docs/ADR-001-architecture.md
Versioning	docs/VERSIONING.md
Release checklist (mirror / PyPI)	docs/RELEASE.md
Changelog	CHANGELOG.md

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Quick start

CLI

fairpipe validate \
  --csv data.csv \
  --y-true y_true \
  --y-pred y_pred \
  --sensitive gender \
  --with-ci

fairpipe run-pipeline --config config.yml --csv data.csv --output-dir artifacts/

Python

from fairpipe import load_data
from fairpipe.metrics import FairnessAnalyzer

df = load_data("data.csv")
analyzer = FairnessAnalyzer(min_group_size=30)
result = analyzer.demographic_parity_difference(
    y_pred=df["y_pred"],
    sensitive=df["gender"],
    with_ci=True,
)
print(result.value, result.ci)

CLI commands, YAML configuration, workflow orchestration, training, monitoring, and the optional REST API are documented on the docs site and in docs/api.md.

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CI/CD Integration

Add fairness validation to every pull request with the companion GitHub Action:

# .github/workflows/fairness-check.yml
name: Fairness Check
on: [pull_request]

jobs:
  fairness:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: SvrusIO/fairpipe-action@v1
        with:
          csv: data/predictions.csv
          y-true: y_true
          y-pred: y_pred
          sensitive: gender
          threshold: "0.05"
          metric: "equalized_odds_difference"
          fail-on-violation: "true"

Point csv at your predictions file. If equalized odds difference exceeds 0.05, the PR is blocked. A full fairness report is written to the Actions job summary — metric values, confidence intervals, group breakdowns — permanently attached to the commit.

→ SvrusIO/fairpipe-action

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Development

git clone https://github.com/SvrusIO/fAIr.git
cd fAIr
pip install -e ".[dev]"
pytest -q

See CONTRIBUTING.md and SECURITY.md.

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Case Studies

COMPAS Recidivism Bias Analysis — Reproduces ProPublica's 2016 Machine Bias finding. Measures an Equalized Odds Difference of 0.2116 on the COMPAS dataset and demonstrates a 53.9% reduction via Instance Reweighting.

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Project links

Homepage / docs	SvrusIO.github.io/fAIr
Repository	github.com/SvrusIO/fAIr
Issues	github.com/SvrusIO/fAIr/issues

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License

Apache License 2.0 — see LICENSE.