Thanks for writing this great primer! Here are a few random notes,
- I'm not sure we can say that our feature injection is "unique", it seems closely related to existing counterfactual methods for generating explanations. I imagine that what's unique is that most ML systems will either take an identifier and extract the features opaquely and internally, or will accept the features as direct inputs. We're doing something interesting by providing ID-based scoring but with access to features, and it goes beyond feature injection as you point out, by providing transparent access to the extracted features. Maybe we should cite writing about counterfactuals to help put our capabilities in context?
- This might be a good opportunity to add one more stage to the image, showing how the model outputs floating-point predictions which are then compared against a threshold. Also fine if you want to avoid the complexity.
- "if half of those paragraphs had exactly one templated reference"—but in the example features it looks like we're assuming every paragraph has a reference, rather than half of the paragraphs.