A decade has passed since the subprime mortgage crisis and the Fintech industry is starting to embracing alternative data and machine learning to improve predictive models that have the capability to determine the creditworthiness of consumers with low credit scores. Compared to traditional credit-risk and lending practices, these new financial products and services–buttressed by fledgling data sets and enigmatic algorithms–pose a new set of challenges for regulatory oversight and risks to consumer welfare.
We will apply the AI Blindspot discovery process created at the Assembly program at the Berkman Klein Center and MIT Media Lab to investigate the potential benefits and shortcomings of new financial service offerings from startups such as Upstart, with emphasis on their compliance with three legal mandates: 1. Equal Access to Credit Act’s call for guarding against algorithmic bias; 2. Consumer Credit Protection Act’s stipulation for transparency and explainability; 3. Dodd-Frank Act’s prohibitions against unfair, deceptive, or abusive acts or practices.
This project seeks to engage with the relevant social groups in the consumer loan marketplace to identify blind spots that might arise from unconscious bias and historical inequalities over the course of developing, deploying and operating AI systems that automate consumer lending processes. We will delve into issues such as discrimination by proxy, disparate impact, transparency and explainability, and control over personal data.
Sample questions we plan to address are:
This proposed project will: