Can AI Answer Toughest HR Questions?

A Podcast addressing tough questions, research from Wharton:


Can Artificial Intelligence Help Answer HR’s Toughest Questions?
Aug 30, 2019 Human Resources Podcasts Research  North America using AI in human resources

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Wharton's Peter Cappelli and Prasanna Tambe discuss their new research about the challenges of using AI in human resources.

With companies continuing to shrink or outsource their human resources departments, it is tempting to augment that traditional business function with artificial intelligence. Data science holds so much promise for other fields that it makes sense for algorithms to replace imperfect human decision-making for hiring, firing, scheduling and promoting. But new research from Wharton professors Peter Cappelli and Prasanna “Sonny” Tambe flashes a cautionary yellow light on using AI in human resources. In their paper, “Artificial Intelligence in Human Resources Management: Challenges and a Path Forward,” the professors show how limited data, the complexity of HR tasks, fairness and accountability pose problems for digital HR. The study, which was co-authored by Valery Yakubovich, professor at ESSEC Business School and senior fellow at the Wharton Center for Human Resources, also looks at how to remedy those problems. Cappelli and Tambe spoke about their research with Knowledge@Wharton. (Listen to the podcast at the top of this page.)

An edited transcript of the conversation follows.

Knowledge@Wharton: You make the point that while AI is invading many different industries and sectors, there are some special concerns when it comes to using AI in human resources. Can you talk about what some of those challenges are?

Sonny Tambe: When you talk to HR practitioners who see their colleagues in finance and marketing using these technologies with so much success, part of the question they ask is, why does it seem so hard for us? I think part of the point we wanted to make is that there are systemic and structural differences for HR that do make it harder. For example, when you are building an AI-based system, you need to know what the right answer is, what a good employee looks like, and defining that in HR is already a difficult thing to do.  .... "