kryptologyst/Fairness-Evaluation-Toolkit

The Fairness Evaluation Toolkit is an open-source Python library (MIT licensed) for assessing fairness and bias in machine learning classification models. It implements a set of standard fairness metrics (e.g. demographic parity difference, equalized odds difference, disparate impact ratio, statistical parity difference, equal opportunity difference, and calibration difference), enabling developers and researchers to quantify how a model's predictions vary across sensitive/protected subgroups.

The toolkit includes baseline model implementations (logistic regression, random forest, decision tree), synthetic dataset generators for creating controlled biased and unbiased test scenarios, and an interactive Streamlit dashboard for exploring fairness results in real time. It supports reproducible evaluation through deterministic seeding and provides a programmatic API alongside command-line scripts for running evaluation suites.

Notably, the toolkit incorporates explicit accountability and human-oversight safeguards. It includes a non-reliance disclaimer stating results should not be the sole basis for fairness decisions in production systems, and calls for continuous monitoring, multi-metric validation, documentation of evaluation processes, and regular fairness audits. It acknowledges known limitations, including metric instability, context-dependency of fairness definitions, and sensitivity to model complexity. 

Auto-discovered on 2026-08-13 by OECD Catalogue Automation