Based on my research topic, I chose to elaborate my methodology through a quantitative study with a positivist philosophical foundation. My primary data was collected through an online questionnaire that selected practitioners of digital algorithms as a sample. My secondary data was collected by searching historical data for cases of gender risk propensity embodied in 170,817 orders from a particular technology algorithmic financial services industry platform in the last two years.
Based on the data collected, I conducted the following analysis. 53 people were in the research sample, with a response rate of 88%, and only 20% of the AI industry was female. Although the team preferred female AI workers, there were few female job seekers. Borrowing default rates for women in the secondary research were 38% lower than for men. The absence of a significant effect between gender differences and borrowing accessibility suggests that female borrowers are discriminated against by irrational preferences in the internet lending market, with single women having significantly lower borrowing success rates than men and being discriminated against more severely.