An empirical study of algorithmic bias
Abstract
In all goal-oriented selection activities, the existence of a certain level of bias is unavoidable and may be desired for efficient AI-based decision support systems. However, a fair, independent comparison of all eligible entities is essential to alleviate explicit biasness in a competitive marketplace. For example, searching online for a good or a service, it is expected that the underlying algorithm will provide fair results by searching all available entities in the search category mentioned. However, a biased search can make a narrow or collaborative query, ignoring competitive outcomes, resulting in it costing the customers more or getting lower-quality products or services for the resources (money) they spend. This chapter describes algorithmic biases in different contexts with real-life case studies, examples, and scenarios; it provides best practices to detect and remove algorithmic bias.
Publication Title
Handbook On Computer Learning And Intelligence
Recommended Citation
Dasgupta, D., & Sen, S. (2022). An empirical study of algorithmic bias. Handbook On Computer Learning And Intelligence, 2-2, 895-922. https://doi.org/10.1142/9789811247323_0023
