Can Users Control and Understand a UI Driven by Machine Learning?

I argue that pie menus are much easier for users to operate, understand how they work (selection base purely on the direction between clicks, not the path), and predict how they will respond, and thus trust, than general gesture recognition (often implemented with a black box based on machine learning). Because the pie menu gesture recognition algorithm is obvious, transparent, and Fitts' Law friendly.

Pie menus completely saturate all of "gesture space" with valid easy to distinguish gestures of uniform and high area. But gesture recognition systems like Palm's Graffiti only uses a small subset of all possible gestures, each with different areas and distances from each other (i.e. how easy is it to make each gesture and how distinct each gesture is varies), and most gestures are invalid. There are no possible invalid pie menus gestures -- they all map to well defined selections, so no gesture space is wasted.

Ideally each gesture should have a large area in "gesture space" so it's easy to make, and be as distinct (well separated) from other gestures as possible, so it's harder to make mistakes (like confusing an "n" with a "u"). Pie menus optimize for those ideals. It's also extremely useful that you can correct mistakes in-flight (i.e. reselection, browsing, moving out further to get more "leverage"), so no matter how much you mess up the gesture, you can always correct your mistake and refine your selection.

Once you start writing a sloppy "n" and realize that it may think you're writing a "u", there's no way to correct your mistake, and all you can do is scribble to make sure you input an invalid gesture that's ignored (and hope it isn't mistaken for the "delete all" gesture!), then try again.

https://medium.com/@donhopkins/gesture-space-842e3cdc7102