| Companies are finding all kinds of tasks for AI technology these days, from summarizing financial documents to aiding in legal briefs. But should they? Part of Kathy Pham's job as VP of AI and machine learning at HR software giant Workday is to evaluate the risk and efficacy of applying AI to a given function across the company's sprawling hiring and HR tools. Love or hate its ubiquitous software, the company is using AI in a host of ways, from expediting expense reports to surfacing skills for a job—a tricky task when a growing number of laws are addressing the use of AI in hiring. Pham is also drawing on experience asking those kinds of questions on a broader scale: She served as executive director of the National Artificial Intelligence Advisory Committee at the Commerce Department's National Institute of Standards and Technology and held previous roles at the White House and the Federal Trade Commission. We talked to Pham about why she made the switch to Workday last fall, how she evaluates AI risk at the company, and where AI regulation is headed next. This conversation has been lightly edited for length and clarity. What brought you to Workday after spending time in the public sector? My time in the public sector taught me a couple of different things. One is the power of infrastructure that works, and also the power of policies that exist when that infrastructure doesn't work. And so for me, with Workday, I saw an opportunity to be part of a company that is the system of record for so many other customers, companies, organizations around the world, and one that has been doing it really, really well. And when you have a system of record for people and money data, you can't mess that up, especially when you think about how to really enhance that work with any kind of AI and machine-learning technologies…What, candidly, was really refreshing was that [Workday] co-president Sayan [Chakraborty] was serving as a member of the National AI Advisory Committee. And he just had this very practical, honest approach to [talking] about AI, the technology itself, what it can and can't do, and the problems that it should or should not solve. And it was so refreshing to have that perspective amongst a sea of the extremes of AI. What do you mean by extremes? If you were to reduce it down to extremes, you have the groups that are [saying], "Let's put an AI in everything, it'll solve all the problems." Then you have the groups that are [saying], "No AI on anything at all, because it will cause so many problems"…And I actually talk to my colleagues a lot about fostering that sense of curiosity, to dive into "What even is this technology?" Keep reading here.—PK | 
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