The expanding use of AI is attracting new attention to the importance of workforce diversity. Although tech companies have stepped up efforts to recruit women and minorities, computer and software professionals who write AI programs are still largely white and male, Bureau of Labor Statistics data show. Developers testing their products often rely on data sets that lack adequate representation of women or minority groups. One widely used data set is more than 74% male and 83% white, research shows. Thus, when engineers test algorithms on these databases with high numbers of people like themselves, they may work fine.

Jim Moynes, vice president of risk management at Ford Motor Credit in Dearborn, Mich., first became interested in using machine learning to improve car loan underwriting several years ago. "We were watching what others were working on," he said. "We like to be innovative and try to stay up with what's going on." The company recently ran an experiment to see if machine learning could help its underwriters better understand the loan applications it receives. It was a champion vs. challenger test: Moynes' team took several years of loan data, removed all personally identifiable information from it, and gave it to ZestFinance, a provider of machine-learning-based online lending software, and its own modeling team, which creates logistic regression models to predict potential borrowers' creditworthiness. Each team ran the loan application data through its models and predicted the future performance of the loans.