Song, Ziheng; PingNg, Chun; Zhou, Yuan; Washington, Patick - In: Journal of Applied Economics 27 (2024) 1, pp. 1-18
The potential of FinTech algorithms to decrease gender bias in credit decisions is limited by the impartiality of the data used to train them. If the data is partial or biased, the algorithmic decision-making process may also be discriminatory, exacerbating existing inequalities. In this study,...