This discussion has been edited and condensed for clarity. McIlwain: When I testified before Congress last December about the impact of automation and AI in the financial services industry, I cited a recent study that found that unlike human loan officers, automated mortgage lending systems fairly approved home loans, without discriminating based on race. However, the automated systems still charge Black and Hispanic borrowers significantly higher prices for those loans. This makes me skeptical that AI can or will do any better than humans. Did you draw the same conclusions?

Our upcoming magazine issue is devoted to long-term problems. Few problems are longer-term or more intractable than America's systemic racial inequality. And a particularly entrenched form of it is housing discrimination. A long history of policies by banks, insurance companies, and real estate brokers has denied people of color a fair shot at homeownership, concentrated wealth and property in the hands of white people and communities, and perpetuated de facto segregation. Though these policies--with names like redlining, blockbusting, racial zoning, restrictive covenants, and racial steering--are no longer legal, their consequences persist, and they are sometimes still practiced covertly or inadvertently.

In today's digital world, most enterprises are handling huge volumes of enterprise data. Sifting through it to locate the one nugget of information you need can be so onerous, many managers don't even try. They're already busy coordinating hectic employee time-off requests, making last-minute schedules, sorting out performance reviews, and completing hundreds of other tasks to keep the business running day-to-day. They simply don't have time. To help a company's data work for -- rather than against -- them, teams are increasingly turning to artificial intelligence (AI). These systems dive into mountains of data and streamline some of the most time-consuming aspects of workforce management.