22 Kellogg Insight qualified as men were sometimes reviewed more poorly, the system logi - cally learned to disfavor the resumes of female applicants. “I love this example because it shows how machine learning and scale expose things,” says Hammond. It also shows why having a functional understanding about various AI technologies can be so critical. Knowing that deep learning has this potential to amplify any biases reflected in the data can help companies decide when and how to use it, as well as design safeguards to mitigate any negative consequences. “The kind of knowledge you need, it’s not deep,” says Hammond. “It really comes down to, for any given technology: What does it do, what are its requirements, what can you expect of it, and where are the places where you can make mistakes?” The Power of Knowing Your Own Constraints Still, just because the level of knowledge required isn’t deep or particu - larly technical, it isn’t easy for leaders to come by. They won’t get it directly from the companies selling AI-powered tools. Individual vendors have no incentive to provide potential customers with a fundamental understanding of what it is they do—especially now, with competition in the space proliferating. From the vendors’ perspec - tive, it is far better to let customers believe their tool works almost as if by magic. Bigger tech companies have even less incentive, as they are likely trying to sell you on not just one tool but a suite of tools: not just Microsoft Office, but also Power BI and Azure, and so on. Based on insights from Kris Hammond