fAIr

ADR-001: Measurement Module Architecture

Context:
The Measurement Module unifies fairness measurement across ML workflows, providing a defensible, reproducible, and extensible layer for evaluating fairness metrics.
It standardizes how statistical validation and reporting are handled across multiple teams, libraries, and pipelines.

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Decision

1. Single Unified Entry Point
   • All fairness computations are exposed through the FairnessAnalyzer class, located in the measurement module.
   • The analyzer supports multiple backends via adapters:
     • native (built-in reference implementation)
     • FairlearnAdapter
     • AequitasAdapter
   • This abstraction allows seamless switching between fairness libraries without changing user code.
2. Metrics Engine
   • Implements parity metrics for both classification and regression tasks:
     • demographic_parity_difference
     • equalized_odds_difference
     • mae_parity_difference
   • Supports intersectional analysis with min_group_size filtering to ensure statistical reliability.
3. Statistical Validation Layer
   • Provides bootstrap-based confidence intervals (bootstrap_ci, bca_ci) and Bayesian small-n corrections (beta_binomial_interval).
   • Reports standardized effect sizes (risk_ratio, cohen_d) for practical interpretability.
   • Each metric returns a structured result object:

     {
       "metric": "equalized_odds_difference",
       "value": 0.12,
       "ci": [0.09, 0.17],
       "effect_size": 1.42,
       "n_per_group": {"A": 120, "B": 100}
     }
     

4. Workflow Integration
   • MLflow logger: Records fairness metrics, confidence intervals, and metadata as structured artifacts.
   • Pytest plugin: Adds assert_fairness() for automated threshold checks within CI/CD pipelines.
   • CLI tool (fairpipe validate): Enables one-command validation using a CSV input.
   • Markdown reporting: Generates readable fairness summaries for audits and documentation.
5. Development Workflow
   • Pre-commit hooks ensure linting (Black, Ruff, isort) and smoke fairness checks.
   • CI (GitHub Actions) runs matrix tests across Python 3.10–3.12 and multiple OS environments.
   • System tests confirm end-to-end flow: metric → CI → MLflow → report.

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Consequences

• Developers now use a single, stable interface instead of juggling multiple fairness libraries.
• The measurement process is statistically robust, traceable, and CI-ready.
• The modular architecture supports future extensions (e.g., group fairness dashboards, temporal bias tracking).

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Status:

✅ Accepted (v0.1.0) — Measurement Module released and validated through demo and CI integration.

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ADR-002: fairpipe Shim Namespace

Date: 2026-05-07

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Context

The project is published to PyPI under the name fairpipe, but the internal Python package was named fairness_pipeline_dev_toolkit. This mismatch meant that pip install fairpipe succeeded but import fairpipe raised ModuleNotFoundError, forcing users to discover and use the internal package name. This violated the principle of least surprise and created friction in onboarding, documentation, and third-party integrations.

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Decision

A fairpipe/ top-level shim package was added to the repository. Each submodule is a thin re-export that imports all public symbols from the corresponding fairness_pipeline_dev_toolkit submodule using from fairness_pipeline_dev_toolkit.X import * and forwards __all__. Object identity is fully preserved — no wrappers, no copies.

The shim covers all public submodules: metrics, pipeline, training, monitoring, integration, and exceptions. The package-level __version__ is re-exported from fairness_pipeline_dev_toolkit.__version__.

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Consequences

• Users can now write import fairpipe or from fairpipe.metrics import FairnessAnalyzer as naturally as expected from the PyPI package name.
• All existing code using fairness_pipeline_dev_toolkit.* continues to work without any changes (full backward compatibility).
• Both namespaces are documented as equally valid and maintained in lockstep.
• The shim adds no runtime overhead beyond a single extra import indirection per submodule on first import.

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Status:

✅ Accepted (v0.6.5) — fairpipe shim namespace shipped, tested (4 namespace parity tests), and documented.

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