Applied Artificial Intelligence: An Introduction For Business Leaders

by Mariya Yao

Machine learning debt can be divided into three main types: code debt, data debt, and math debt.(86) Code debt arises from the need to revisit and repurpose older code that may no longer suit the project. Data debt focuses on the data that was used to train the algorithm, which may have been incorrect or is no longer relevant. For example, a model predicting consumer healthcare coverage may flounder when healthcare regulations change, as new regulatory mandates decrease the importance of historical data, or when historical data must be purged for reasons of compliance. Finally, math debt stems from the complexity of the model’s algorithms. Most machine learning algorithms will require ongoing customizations that can make them harder to configure, maintain, and understand.