In the fast-moving and highly competitive artificial intelligence sector, developers’ claims that their AI tools can make critical predictions with a high degree of accuracy are key to selling prospective customers on their value. Because it can be daunting for people who are not AI experts to evaluate these tools, leaders may be tempted to rely on the high-level performance metrics published in sales materials. But doing so often leads to disappointing or even risky implementations.

Over the course of an 11-month investigation, we observed managers in a leading health care organization as they conducted internal pilot studies of five AI tools. Impressive performance results had been promised for each, but several of the tools did extremely poorly in their pilots. Analyzing the evaluation process, we found that an effective way to determine an AI tool’s quality is understanding and examining its ground truth.1 In this article, we’ll explain what that is and how managers can dig into it to better assess whether a particular AI tool may enhance or diminish decision-making in their organization.

The quality of an AI tool — and the value it can bring your organization — is enabled by the quality of the ground truth used to train and validate it. In general, ground truth is defined as information that is known to be true based on objective, empirical evidence. In AI, ground truth refers to the data in training data sets that teaches an algorithm how to arrive at a predicted output; ground truth is considered to be the “correct” answer to the prediction problem that the tool is learning to solve. This data set then becomes the standard against which developers measure the accuracy of the system’s predictions. For instance, teaching a model to identify the best job candidates requires training data sets describing candidates’ features, such as education and years of experience, where each is associated with a classification of either “good candidate” (true) or “not a good candidate” (false). Training a model to flag inappropriate content such as bullying or hate speech requires data sets full of text and images that have been classified “appropriate” (true) or “not appropriate” (false). The aim is that once the model is in production, it has learned the pattern of features that predicts the correct output for a new data point.

In recent years, there has been growing awareness of the risks of using features from the training data sets that are not representative or that contain bias.2 There is surprisingly little discussion, however, about the quality of the labels that serve as the ground truth for model development. It is critical that managers ask, “Is the ground truth really true?”