With growing awareness of societal impact of artificial intelligence, fairness has become an important aspect of machine learning algorithms. The issue is that human biases towards certain groups of population, defined by sensitive features like race and gender, are introduced to the training data through data collection and labeling. Two important directions of fairness ensuring research have focused on (i) instance weighting in order to decrease the impact of more biased instances and (ii) adversarial training in order to construct data representations informative of the target variable, but uninformative of the sensitive attributes. In this paper we propose a Fair Adversarial Instance Re-weighting (FAIR) method, which uses adversarial training to learn instance weighting function that ensures fair predictions. Merging the two paradigms, it inherits desirable properties from both – interpretability of reweighting and end-to-end trainability of adversarial training. We propose four different variants of the method and, among other things, demonstrate how the method can be cast in a fully probabilistic framework. Additionally, theoretical analysis of FAIR models' properties have been studied extensively. We compare FAIR models to 7 other related and state-of-the-art models and demonstrate that FAIR is able to achieve a better trade-off between accuracy and unfairness. To the best of our knowledge, this is the first model that merges reweighting and adversarial approaches by means of a weighting function that can provide interpretable information about fairness of individual instances.

Achieving fairness in algorithmic decision-making tools is an issue constantly gaining in need and popularity. Today, unfair decisions made by such tools can even be subject to legal consequences. We propose a new constraint that integrates fairness into data envelopment analysis (DEA). This allows calculating relative eficiency scores of decision-making units (DMUs) with fairness included. The proposed fairness constraint does not allow disparate impact to occur in eficiency scores, and enables the creation of a single data envelopment analysis for both privileged and discriminated groups of DMUs simultaneously. We show that the proposed method (FairDEA) produces an interpretable model that was tested on a synthetic dataset and on a real-world example, namely, the ranking between hybrid and conventional car designs. We provide the interpretation of the FairDEA method by comparing it to the basic DEA and the balanced fairness and eficiency method (BFE DEA). Along with calculating the disparate impact of the method, we performed a Wilcoxon rank-sum test to inspect for fairness in rankings. The results show that the FairDEA method achieves similar eficiency scores as other methods, but does not have disparate impact. Statistical analysis indicates that differences in ranking between groups are not statistically different, which means that ranking is also fair. This method contributes both to development of data envelopment analysis, and the inclusion of fairness in eficiency analysis.