Diversity and Inclusion Metrics in Subset Selection The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives. When considering the relevance of ethical concepts to subset selection problems, the concepts of diversity and inclusion are additionally applicable in order to create outputs that account for social power and access differentials. We introduce metrics based on these concepts, which can be applied together, separately, and in tandem with additional fairness constraints. Results from human subject experiments lend support to the proposed criteria. Social choice methods can additionally be leveraged to aggregate and choose preferable sets, and we detail how these may be applied.

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.