Attenuation of Human Bias in Artificial Intelligence: An Exploratory Approach

Abstract

As the world embraces Industry 4.0 with open-hands, Artificial Intelligence has taken centre-stage. AI systems are driving decision making and impacting stakeholders' viewpoints through data. While these systems pamper companies with these new-found efficiencies, they are quite vulnerable to the `garbage in, garbage out' syndrome. In the case of such intelligent systems, the type of `garbage' is biased data. One cannot hope of eliminating bias in machine learning and Artificial Intelligence without addressing the pressing concerns of bias in humans. Although it is deemed as an uphill task by intellectuals in the academia and industry, gradual yet significant steps have been made. This paper intends to measure and mitigate bias in US Employment Demographics. Different algorithms will be applied and a comparison shall be carried out. The social implications of bias in Artificial Intelligence will also be discussed extensively.