
of machine teaching

My sense making process often involves making simple sketches and showing them to the subject matter expert. Often my understanding isn't 100% correct, but making a visual provides an excellent artifact for them to respond to.

In a classifier the user assigns a single category to each document, as the model improves it makes suggestions for which category each document belongs to.

Entity extractors add a layer of complexity, users label words, phrases, and sentences with nested categories. A fully trained model is able to find these entities in previously unseen data.

The first of a series of unmoderated user studies conducted on Userlytics

Annotated wireframes help ensure front-end engineers have a clear idea of the design intentions. The model initialization flow helps a user get a new project started without getting overwhelmed with prediction mismatches which are common early in the development of the model.

Before: site tour locked the user from interacting with any elements in the interface

After: contextual help panel provides the same information while maintaining interactivity with the interface.



While most of my designs for Duet avoid using modals, adding or editing a feature is a workflow that needs to be completed before continuing to teach the model.
where it matters

The entity selection tool is an advanced highlighting interaction which allows for selecting words, phrases, or sentences to label them. The interaction model is nuanced and unique to Duet, so using an existing design pattern would have led to confusion about how to use it.
In addition to annotated wireframes, I created simple animations the engineers could refer to.