fAIr

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

Real-world bias audits demonstrating fairpipe’s full pipeline — from measurement and detection through mitigation and CI/CD integration.

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COMPAS Recidivism Bias Analysis

Reproduces ProPublica’s 2016 Machine Bias investigation on the COMPAS recidivism algorithm used in US courtrooms.

• DPD = 0.2451 — Black defendants 24.5 percentage points more likely to be flagged high-risk than white defendants
• EOD = 0.2116 — among defendants who will not reoffend, Black defendants are 21 percentage points more likely to be incorrectly labelled high-risk
• 53.9% reduction in EOD via Instance Reweighting
• 28 features with statistically significant racial disparities detected
• 23 proxy variables identified — removing the race column alone would not fix this model

AI Hiring Bias — ACS Employment Analysis

Demonstrates the type of bias audit now required under NYC Local Law 144 and the EU AI Act, framed around Mobley v. Workday — the 2025 class action alleging AI hiring tools discriminated against millions of applicants by age, race, and disability.

• DPD = 0.1046 — white candidates selected at 32.3% vs Black candidates at 21.8%, a 10.5 percentage point gap with no race feature in the model
• EOD = 0.1022 — 76.8% of qualified Black candidates incorrectly rejected vs 66.6% of white candidates
• All 5 prediction features show statistically significant racial disparity — removing the race column alone would not fix this model
• 47.6% reduction in EOD via Instance Reweighting, closing to within 0.0036 of the 0.05 compliance threshold
• Dataset: ACS 2018 1-Year California (196,604 individuals, folktables)

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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.

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