In this new post-corona era, there is a rising interest in technology’s impact on biases in human decisions. Do few or no human interactions reduce and even eliminate culture, racial, and other taste-based biases? In this series of blogs, I hope to provide more understanding on this question by discussing several recent research papers.
The first paper to discuss is D'Acunto et. al. (2020) where they find that automated robo-advising lending tools can reduce culture biases in the peer-to-peer lending process. Moreover, the reduction of cultural biases actually provides sizable economic returns to investors! Discriminating lenders face 32% higher default rates and about 11% lower returns on the loans they issued.
As their study uses a leading P2P platform in India, Faircent, let's first have a brief idea of the cultural bias in India.
https://www.culturalsurvival.org/publications/cultural-survival-quarterly/ethnic-and-religious-conflicts-india
https://www.pewresearch.org/fact-tank/2021/06/29/key-findings-about-religion-in-india/
https://en.wikipedia.org/wiki/Shudra
Their main findings are two folds.
Before investors use robo-advising, they detect two spiking patterns that are indications of culture-based discrimination in the lending process.
- Both Hindu and Muslim lenders tend to favor borrowers of their same religious relative to borrowers of the other group.
- Lenders are less likely to lend to borrowers who tend to be considered as Shudra borrowers.
