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Machine learning algorithms are being widely used in different fields such as image recognition, speech recognition, traffic and weather prediction, recommendations, spam filtering, self-driving cars, stock market prediction, medical diagnosis, and more. The ability of machines to feed in years of data and predict the outcome has helped humans in unimaginable ways. Machine learning has automated half of human work requiring very little human intervention and saving time and energy. However, sometimes individuals end up paying the price and falling victim to the unfair and biased outcome of machine learning algorithms. Machines learn through data what they are provided with, but the data that machines learn from does not come free from human biases. Human biases based on race, sex, ethnicity, skin color, and other sensitive attributes are reflected in the dataset which, when fed to the machine, results in a similar biased prediction. The years of data represent the bias that has been present in society, and the machine learning model simply mimics the pattern. There has been constant research and experiments being done on how to prevent these biases from reflecting on the prediction. In this paper, we will investigate if there is any bias present in the benchmark Statlog “Australian Credit Approval” dataset and take necessary measures to mitigate the bias present in the data. The paper shows how the AIF360 tool can identify and mitigate bias in the data and eventually in the learning algorithms.
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