AIF360

As AI systems are increasingly deployed in high-stakes domainshiring, lending, healthcare, criminal justicethe need to measure and address discriminatory model behavior has become a regulatory and ethical imperative.

AIF360 operationalizes fairness research into accessible tools that data scientists can apply without requiring deep expertise in the fairness literature.

The toolkit includes over 70 fairness metrics covering group fairness (equal opportunity, demographic parity, equalized odds), individual fairness, and causality-based fairness concepts.

Bias mitigation algorithms are organized across three intervention points in the ML pipeline: pre-processing (modifying training data to reduce bias), in-processing (incorporating fairness constraints into model training), and post-processing (adjusting model outputs to satisfy fairness criteria).

Users can apply multiple mitigation strategies and compare their effect on both model fairness and predictive accuracy using the toolkit's built-in evaluation framework.

Data scientists and ML engineers working on models subject to anti-discrimination regulations (ECOA for credit, EEOC for employment, the EU AI Act for high-risk systems) use AIF360 as a structured framework for conducting and documenting fairness assessments.

Academic researchers studying algorithmic fairness use it as a shared implementation baseline, enabling fair comparison of new mitigation approaches against the algorithms already in the toolkit.