2020/07/01 by Sajib Sen, Dipankar Dasgupta, Kishor Datta Gupta · 1 citation
Business, Management and Accounting · Decision Sciences · #Consumer Market Behavior and Pricing #Auction Theory and Applications #Multi-Criteria Decision Making
paper · doi:10.1109/compsac48688.2020.00-95
openalex publication_date 2020/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.