How to make AI understandable
This blog is part of a series on intelligent system design.
In our previous blog Explaining System Intelligence we looked at why it’s vital to explain the underlying models and reasoning behind AI algorithms to the user, and outlined what needs to be explained and when. Now we want to take this a step further and think about “how”.
Words or visuals?
To communicate explanations effectively, you need to decide on the right elements: some aspects are best explained in a text, while other information is best conveyed visually, for example in a chart or list. Often, a combination of text and visuals offers the best experience.
Example: Combination of text, visuals, and interactive elements to explain the supplier ranking
The right information in the right place
We recommend using progressive disclosure to control the level of detail you expose to users. With this design technique, the main screen contains just enough information to give the user a basic understanding. From there, users who require more information can navigate to secondary screens that reveal increasing levels of detail. The advantage: users don’t have to concern themselves with the specifics unless they need them.
It takes a lot of thought to distribute the relevant information across the different levels in a reasonable way. These principles may help:
- Make sure that information value is added with each disclosure level.
- Do not just repeat information from the previous level.
- Make sure that the information on each level is self-contained and that all elements form a natural flow.
- Think about how to achieve smooth transitions between levels.
Example: Progressive disclosure to explain a supplier ranking
Relevant information at a glance
The first-level explanation on the main screen should be as concise as possible. Often, a simple icon or a few words are sufficient. If the next level shows longer texts, help the user to scan the information by emphasizing the important parts. Bear in mind that formatting should not be used for decoration only. As rule of thumb, try to restrict textual explanations to no more than 3 short sentences.
It’s also worth investing some time on the wording. Make your explanations easy to follow by using clear and simple language and addressing users directly. Aim for a conversational style (that is, write as you would speak) and stick to familiar words.
Example 1: Adding value at each disclosure level
Example 2: Self-contained information
Example 3: Just enough formatting
In a nutshell
How you structure and write explanations is key to making system intelligence feel intuitive. Good communication means finding the sweet spot for each disclosure level: What’s the right level of detail? What combination of text and visuals works best? And how can I use clear, conversational language and conscious formatting to put my message across? Taking these questions on board will help you to craft effective explanations that make AI understandable to users.
What are your experiences? Feel free to add your thoughts in the comments.
Special thanks to Susanne Wilding for reviewing and editing this article.