An Empirical Study on Algorithmic Bias

In all goal-oriented selection activities, an existence of certain level of bias is unavoidable and may be desired for efficient artificial intelligence based decision support systems. However, a fair independent comparison of all eligible entities is essential to alleviate explicit bias in competitive marketplace. For example, searching online for a good or service, it is expected that the underlying algorithm will provide fair results by searching all available entities in the category mentioned. However, a biased search can make a narrow or collaborative query, ignoring competitive outcomes, resulting customers in costing more or getting lower quality products or services for the money they spend. This paper describes algorithmic bias in different contexts with examples and scenarios, best practices to detect bias, and two case studies to identify algorithmic bias.