Racial and gender biases exist in algorithmic lending
Race and ethnicity is one of the most concerning fairness areas in bank loan applications, and many earlier studies also provide evidence of racial biases in loan markets and housing markets. Barlett et al. found evidence of racial discrimination by studying more than 13 million mortgage applications and refinance applications in 2019. They found that the racial bias against black borrowers can come from both face-to-face lending and algorithmic lending (Bartlett et al., 2019).
Gender differences in bank loan access have been studied thoroughly too. Accessing bank credit is more difficult or requires higher costs for female entrepreneurs (Calcagnini et al. 2015, Fay, 1993, Ongena et al. 2015). Given the same level of income, female loan applicants encounter higher rejection rates and lower loan approval amounts.
Garbage in, garbage out
While many people might think that machine learning and AI-driven systems can bring more objectiveness to the decision making process in loan applications, it is unfortunately not true. AI-driven systems rely on algorithms and models for making predictions. Algorithms do what they are taught. The biased societal patterns hidden in the data are unintentionally learnt by the algorithms and reproduce biased predictions. Also, the demographics of applicants are used for machine to predict default risks. The algorithms could correlate gender or race with default unsubstantially.
"Unfair algorithms" are not designed to be evilly unfair by data scientists. Instead, these algorithms are usually selected based on their performance in prediction accuracy. Accuracy, however, could be a trade-off of ruling out irrelevant factors. For instance, a machine learned that for 10 raining days, in 6 days I wore a hat; if I wore a hat today, it naturally predicted that there's a large chance it's going to rain.
Ruling out the possibilities that race and gender are mistakenly taken by algorithms as causes of default, is what fair algorithms address (Equalized odds). Data scientists in our group decides to employ post-processing as our fairness algorithm.
To the detailed reasoning on data science reasoning, please refer to our class slides: Project brief slides