A UX Lens on AI Model Accuracy and Precision
By: Michael Zuliani
Tags: Artificial Intelligence, Machine Learning, Design, UX, AI

Evaluating options

Which mobile phone should I buy? Should I sell my investment now or later? These examples illustrate another common application of AI — evaluating options to identify the best one.

This type of AI differs from classification and regression models in several ways. First, there is no single right answer. Which option is considered best is a matter of judgement — a conclusion reached by weighing multiple conflicting factors. And what is considered best depends on the person and situation.

Second, options can be evaluated at multiple levels of detail. The evaluation could:

Identify the best option only, e.g., Option B is the best.

Rank order all the options, e.g., Option B, then C, then A.

Quantify the quality of each option, e.g., Option B is 2.4 times better than Option C.

With this in mind, we can specialize our definitions for the context of evaluating options:

Accuracy: How close the evaluation from the AI is to the correct evaluation.

For any model, we need to state whether the accuracy pertains to: identifying the best option only, rank ordering all the options, or quantifying the quality of each option.

Precision: How specific the value from the AI is.

Like classification models, high precision means being able to evaluate a narrow, specific option.

With this type of AI, the usefulness of the information depends the contextual information provided. Simply presenting the answer, no matter how accurate and precise, is of limited usefulness, as we’ll see in our case study.

Evaluating options case study

When treating a patient, physicians often need to choose among multiple treatment options. An AI could help select the best option for each patient.

Imagine an AI that could read a patient record and evaluate four treatment options. Sample raw output is shown below. The treatments are sorted by score with .99 being the best possible score.

From this raw output, a physician could discern that the AI considers Treatment C the best option because it received the highest score. However, this output is not as useful as it could be because it does not address some things the physician wants to know:

While Treatment C is the “best”, is it “highly recommended” or is it “the best of a bad lot” or is it something in between?

Treatment A also seems pretty good. In practical terms, how much better is Treatment C?

How might this output be presented in a more useful manner in terms of our focus on accuracy and precision? To borrow a phrase, the problem here is that there is too much data and not enough information. That is, the treatment scores (e.g., .79) are more precise than needed but important information is not provided.

One solution is grouping the options into meaningful levels of advisability. That is, a small set of rating categories could be defined such as Recommended and Not Recommended; then options could be assigned one of the ratings.

This coarser-grained (less accurate) rating scheme is more aligned with the reality that treatments C and A are about equally advisable, and either would be effective for this patient.

We’ll end with some further UX considerations for this type of AI: