AI Fairness 360

AI Fairness 360 (AIF360) is a comprehensive open-source toolkit developed by IBM Research for detecting and mitigating bias in machine learning models and datasets. Released in 2018 and maintained as part of IBM's Trusted AI initiative, AIF360 has become one of the most widely referenced and adopted bias detection toolkits in both academic research and industry practice, establishing itself as a foundational tool in the responsible AI ecosystem. The toolkit provides an extensible library of over 70 fairness metrics and more than 10 bias mitigation algorithms spanning three critical stages of the ML pipeline: pre-processing (techniques applied to training data before model building), in-processing (constraints applied during model training), and post-processing (adjustments applied to model predictions). This comprehensive coverage allows practitioners to address bias at whatever stage is most appropriate for their use case and constraints. Key algorithms included in AIF360 range from well-established techniques like reweighing and disparate impact remover in pre-processing, to adversarial debiasing and prejudice remover in in-processing, to calibrated equalized odds and reject option classification in post-processing. The toolkit supports multiple fairness definitions including statistical parity, equal opportunity, equalized odds, and predictive equality, reflecting the academic consensus that fairness is context-dependent and no single metric is universally appropriate. AIF360 is implemented in Python with an R port available, making it accessible to the broad data science community. The library integrates with standard Python ML tools including scikit-learn, TensorFlow, and PyTorch. IBM provides extensive tutorials, Jupyter notebooks, and documentation that walk users through common bias detection and mitigation workflows, significantly lowering the barrier to entry for teams new to fairness testing. As an open-source project licensed under Apache 2.0, AIF360 can be freely used, modified, and distributed by any organization. This makes it an excellent choice for organizations that want to build internal bias testing capabilities without vendor lock-in. However, the trade-off is that AIF360 is a developer library rather than a turnkey product, meaning it requires programming expertise to use effectively and lacks a graphical user interface, enterprise access controls, and commercial support.