Improving Prediction Fairness via Model Ensemble Fair machine learning is a topical problem. It studies how to mitigate unethical bias against minority people in model prediction. A promising solution is ensemble learning  Nina et al 1 first argue that one can obtain a fair model by bagging a set of standard models. However, they do not present any empirical evidence or discuss effective ensemble strategy for fair learning. In this paper, we propose a new ensemble strategy for fair learning. It adopts the AdaBoost framework, but unlike AdaBoost that upweights mispredicted instances, it upweights unfairly predicted instances which we identify using a variant of Luongu0027s kNN based situation testing method 2. Through experiments on two realworld data sets, we show our proposed strategy achieves higher fairness than the bagging strategy discussed by Nina et al and several baseline methods. Our results also suggest standard ensemble strategies may not be sufficient for improving fairness.

FAE: A FairnessAware Ensemble Framework. Automated decision making based on big data and machine learning (ML) algorithms can result in discriminatory decisions against certain protected groups defined upon personal data like gender, race, sexual orientation etc. Such algorithms designed to discover patterns in big data might not only pick up any encoded societal biases in the training data, but even worse, they might reinforce such biases resulting in more severe discrimination. The majority of thus far proposed fairnessaware machine learning approaches focus solely on the pre, in or postprocessing steps of the machine learning process, that is, input data, learning algorithms or derived models, respectively. However, the fairness problem cannot be isolated to a single step of the ML process. Rather, discrimination is often a result of complex interactions between big data and algorithms, and therefore, a more holistic approach is required. The proposed FAE (FairnessAware Ensemble) framework combines fairnessrelated interventions at both pre and postprocessing steps of the data analysis process. In the preprocessing step, we tackle the problems of underrepresentation of the protected group (group imbalance) and of classimbalance by generating balanced training samples. In the postprocessing step, we tackle the problem of class overlapping by shifting the decision boundary in the direction of fairness.