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Need of Mitigating Bias in the Datasets using Machine Learning Algorithms

Need of mitigating the bias that is present in the data is an emerging area in any field of research. In order to improve the efficiency of the system that was designed to analyze which will help in decision making from the results, data play an important role. This made the bias to be a critical factor to consider and mitigating it to ensure that AI systems perform well in making decisions without any discrimination. Various datasets are considered for feeding the machine learning algorithms which holds bias as well as other noises which are not accurate in case of supervised learning. While considering the features to identify the bias in the databases, geography-based, gender-based and object-based are few metrics that are been considered in computer vision is object-based metrics This study helps us to understand the need of mitigating the bias to the maximum possible using ensemble learning algorithm which has the ability to train the data and extract the new set of data which are never seen by applying learning algorithms. This paper also proposes the need of eliminating the skewness present in the data.