Why is HR so gullible?

This was the question asked this week by a Princeton comp-sci professor.

Abstract image of an interview

This week saw a great quote from Princeton Associate Professor of Computer Science, Arvind Narayanan: “why are HR departments apparently so gullible?”

He was referring to the use of AI in pre-recruitment, where AI algorithms pre-screen candidates and match “good employee” attributes with data gathered from an AI-based interview. Candidate suitability and personality are assessed from videos, games and other types of algorithmic systems. Many of these systems claim to work by analyzing body language, speech patterns, mouse movements, eye tracking, tonality, emotional engagement and expressiveness. The sheer quantity of data is astonishing (and alien); hundreds of thousands of data points on people are gathered in a half hour interview or an online game-playing exercise.

The prize for AI-recruitment companies is big. And the stakes for companies and candidates are big too. Narayanan points out, “These companies have collectively raised hundreds of millions of dollars and are going after clients aggressively. The phenomenon of job candidates being screened out by bogus AI is about to get much, much worse.” The top two companies (based on funds raised) are HireVue ($93 million) and pymetrics ($56.6 million).

Companies marketing these services promise a lot, including helping companies increase diversity by reducing the impact of human bias, increasing the quantity and quality of candidates, decreasing the time and cost of recruitment and matching people by “fit” or soft skills or cognitive ability or personal attributes such as patience, resiliency and grit rather than on previous experience. Customers say these systems help them prepare for the “workforce of the future,” where a person’s cognitive and personal style will matter more than technical skills and previous experience.

The goals are laudable. But is AI, especially pre-recruitment screening, going to get us there? As Narayanan says, many of these products are “little more than random number generators.”

There are three broad areas where these systems are problematic:

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