AIF360

Why we included this project

When a model is headed into finance, healthcare, hiring, or education, accuracy alone rarely settles whether it is safe to release. AIF360 tackles the fairness side of that question with group and individual fairness metrics computed directly from datasets and model predictions, paired with a library of research-grade bias mitigation algorithms. The mitigation work spans the whole pipeline: you can clean the training data beforehand, constrain the learner itself, or adjust predictions after the fact, depending on where the bias actually shows up. The package is built to be extended, so teams can fold fairness checks into their own evaluation routines instead of treating them as a one-off audit. Python and R bindings, worked example notebooks, and guidance on picking the right metric or algorithm mean a data scientist can move from measurement to remediation without leaving their usual workflow.