Improving ML Fairness: IBM, UMich & ShanghaiTech Papers Focus on Statistical Inference and Gradient-Boosting
A team from University of Michigan, MIT-IBM Watson AI Lab and ShanghaiTech University publishes two papers on individual fairness for ML models, introducing a scale-free and interpretable statistically principled approach for assessing individual fairness and a method for enforcing individual fairness in gradient boosting suitable for non-smooth ML models.
