12 Interpretability and explainability
Hilo is a (fictional) startup company trying to shake up the online second home mortgage market. A type of second mortgage known as a home equity line of credit (HELOC) allows customers to borrow intermittently using their house as collateral. Hilo is creating several unique propositions to differentiate itself from other companies in the space. The first is that it integrates the different functions involved in executing a second mortgage, including a credit check of the borrower and an appraisal of the value of the home, in one system. Second, its use of machine learning throughout these human decision-making processes is coupled with a maniacal focus on robustness to distribution shift, fairness, and adversarial robustness. Third, it has promised to be scrutable to anyone who would like to examine the machine learning models it will use and to provide avenues for recourse if the machine’s decisions are problematic in any respect. Imagine that you are on the data science team assembled by Hilo and have been tasked with addressing the third proposition by making the machine learning models interpretable and explainable. The platform’s launch date is only a few months away, so you had better get cracking.