Demonstrating Rosa: the fairness solution for any Data Analytic pipeline
This addresses fairness issues in data-driven decision-making systems for industries relying on analytics, though it appears incremental as it builds on existing Fair Adversarial Networks principles.
The paper tackles the problem of human bias in datasets used for data analytics and machine learning by introducing Rosa, a web-based tool that de-biases datasets with respect to a chosen characteristic, resulting in a substantial decrease in bias across five real-world datasets.
Most datasets of interest to the analytics industry are impacted by various forms 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, a large number of biased decision-making systems based on DA/ML have recently attracted attention. In this paper we introduce Rosa, a free, web-based tool to easily de-bias datasets with respect to a chosen characteristic. Rosa is based on the principles of Fair Adversarial Networks, developed by illumr Ltd., and can therefore remove interactive, non-linear, and non-binary bias. Rosa is stand-alone pre-processing step / API, meaning it can be used easily with any DA/ML pipeline. We test the efficacy of Rosa in removing bias from data-driven decision making systems by performing standard DA tasks on five real-world datasets, selected for their relevance to current DA problems, and also their high potential for bias. We use simple ML models to model a characteristic of analytical interest, and compare the level of bias in the model output both with and without Rosa as a pre-processing step. We find that in all cases there is a substantial decrease in bias of the data-driven decision making systems when the data is pre-processed with Rosa.