Algorithm justice – moving towards equitable artificial intelligence

Abstract

Research methodology

The case is presented as descriptive in nature and primarily involves exploratory research.

Case overview/synopsis

Ashraf, a young graduate from Bangalore, India, started a chain of lifestyle shops, his family business in Khartoum, Sudan. To modernize the shops, Ashraf approached a small finance bank for financial assistance. However, after submitting the required documents and with a good credit score, he was denied a loan. The bank officials had mentioned that the loan automation software did not approve the application. Hence, the bank personnel said that they could not do anything further. Disappointed, Ashraf sought the help of his professor, John, to understand why the software rejected his application. Professor John explained to Ashraf the advantages and disadvantages of automation. In the process, Ashraf understood the significance and compelling need to address “Algorithm Bias,” a situation in which specific attributes of an algorithm cause unfair outcomes. The case place students in Ashraf’s position to help them understand the advantages and issues of applying automation through artificial intelligence.

Complexity academic level

The case suits graduate-level courses like business analytics, financial analytics and business intelligence.

Learning objectives

Through the case, the students will be able to: Understand the role of algorithms in business and society. Understand the causes, effects and methods of reducing algorithm bias. Demonstrate the ability to detect algorithm bias. Define policies to mitigate algorithm bias.

Citation

K., R. (2023), "Algorithm justice – moving towards equitable artificial intelligence", , Vol. 19 No. 6, pp. 788-799. https://doi.org/10.1108/TCJ-11-2021-0211