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Most datasets of interest to the analytics industry are impacted by variousforms of human bias. The outcomes of Data Analytics [DA] or Machine Learning[ML] on such data are therefore prone to replicating the bias. As a result, alarge number of biased decision-making systems based on DA/ML have recentlyattracted attention. In this paper we introduce Rosa, a free, web-based tool toeasily de-bias datasets with respect to a chosen characteristic. Rosa is basedon the principles of Fair Adversarial Networks, developed by illumr Ltd., andcan therefore remove interactive, non-linear, and non-binary bias. Rosa isstand-alone pre-processing step / API, meaning it can be used easily with anyDA/ML pipeline. We test the efficacy of Rosa in removing bias from data-drivendecision making systems by performing standard DA tasks on five real-worlddatasets, selected for their relevance to current DA problems, and also theirhigh potential for bias. We use simple ML models to model a characteristic ofanalytical interest, and compare the level of bias in the model output bothwith and without Rosa as a pre-processing step. We find that in all cases thereis a substantial decrease in bias of the data-driven decision making systemswhen the data is pre-processed with Rosa.

Demonstrating Rosa: the fairness solution for any Data Analytic pipeline