Input Data Transparency

From The Foundation for Best Practices in Machine Learning

Input Data Transparency


Ensure that Product Subjects have the ability to observe attributes relied on in the modeling decision and correct inaccuracy. Collect data around this process and use it to identify issues in the data sourcing/aggregation pipeline.


To (a) ensure that the Model is making decisions on accurate data; (b) learn whether there are problems with Model's data assets; and (c) highlight associated risks that might occur in the Product Lifecycle.

Additional Information