Using ML wizardry without losing the magic of habituation
It’s easy to feel like new ML technologies cause us to rethink everything about UX design, but that’s not quite true. The emergence of ML doesn’t change the fact that the most usable, delightful UIs are those that embody principles of good design—like habituation—that many designers and researchers (Don Norman, Jakob Nielsen, Steve Krug, and Jeff Johnson to name a few) have been writing about for years. To help you get started, here are four principles to consider when introducing ML features into a UI:
1. Count decisions as navigation steps
If your ML designs aim to remove navigation steps for users, but then require them to stop and evaluate all of the ML-generated suggestions, you haven’t really saved the user any steps (or time). Evaluating recommendations or visually searching the interface for content counts as a navigation step, just like a tap or click.
2. A predictable UI is necessary when the stakes are high
If the user is coming to your product to perform important, time-sensitive tasks, like quickly updating a spreadsheet before a client presentation, don’t include anything in your UI that puts habituation at risk. No ML-based suggestion will be “helpful” enough to offset breaking your user’s flow state and muscle memory. But if you’re confident that the user has a more open-ended goal like exploration, you have more leeway to put dynamic, ML-based features at the forefront of your UI.
Let’s look at an example. Google Play Music strikes a good balance between habituation-friendly UI and algorithmically-generated recommendations. A music app UI could present users with an alphabetical library of hundreds of thousands of options (arduous, yet habituate-able), but because the user’s goal is often to browse until they find something they feel like listening to, Google Play Music dedicates the vast majority of its UI to surfacing music recommendations that change based on your listening habits and factors like the time of day. There’s also a navigation sidebar that never changes, so the user can still habituate to performing basic tasks like finding a saved playlist.
3. Be predictably unpredictable
If your ML algorithm is going to make recommendations or try to personalize the interface for users, consider dedicating a specific place in the UI for this to happen, rather than building the entire UI around it.
For example, Google Drive created a feature called Quick Access, which uses machine learning to surface a few documents you're likely to need at a given moment. Rather than reordering all your content based on ML predictions, the design team created a constrained, dedicated space for Quick Access at the top of the screen. The rest of the UI remains unchanged, and you can turn the feature off if your prefer to search or navigate files without ML assistance.
4. Make failure your baseline
ML algorithms will make bad predictions. Try to imagine what a user’s process for completing the action without ML assistance would be, as well as the user’s process to correct a potential ML failure. If it’s more work for the user to correct a failure than it is for them to complete the process without having the assistance of ML in the first place, then machine learning is not actually creating a better experience.
Gmail’s Smart Reply, for example, uses machine learning to suggest short replies to your email messages. The UI makes these suggestions unobtrusive and easy to ignore if you prefer to write your own. Imagine an alternative design in which the reply is instead inserted into the message text field, forcing you to erase or edit if it's not helpful. This would be far more work than manually writing a reply without ML assistance, and it would be impossible for you to habituate to the reply-writing process.