Meeting of 10-13-16 Public Meeting on Proposed Reboot of Harassment Prevention Efforts
Good afternoon, Chair Yang, and members of the Commission. Thank you for this opportunity to testify. My name is Michael Housman and I am the Workforce Scientist in Residence at hiQ Labs. At hiQ labs, I translate publicly-available data into insights that allow large employers to identify employees that are potential flight risks and take actions that will help retain them, and improve overall workforce performance. Prior to hiQ, I worked for a company called Evolv that used predictive analytics to help large employers make better hiring decisions. So I've seen how data and algorithms are being used at each end of the employee lifecycle.
The recent trend toward the use of big data in our everyday lives has become its own narrative in the news. Even the White House has weighed in; the Obama administration has released a series of reports on big data and its implications in education, health, advertising, criminal justice, and the economy. The latest report included a particular focus on the civil rights opportunities and challenges of the technology's potential use in employment context, particularly in recruitment.
Yes, it's true that large employers are turning toward computer algorithms to determine who is and is not a good fit for the job. Although the results consistently suggest that these "robot recruiters" are effective at helping hiring managers to employees that stay longer on the job and perform better, there is still some skepticism as to whether computers can replace human judgment when it comes to evaluating talent-and its potential to discriminate.
What's important to recognize is that the current system isn't perfect; recruiters aren't unbiased. In fact, a long line of research documents empirically the existence of a "like me" bias that leads recruiters to hire applicants like themselves. This may benefit job applicants who happened to have gone to the same school as the interviewer, but unfortunately, it tends to hurt anyone who didn't. The inevitable outcome of this bias is that the most talented or skillful individual does not automatically get selected for a job, but rather the applicant the recruiter likes the most. Not only that, but there's the possibility that hiring like-minded individuals tends to reduce diversity in the workplace.
Contrast this with the algorithms that have been built to select the best applicants. These algorithms are designed to make assessment decisions based on the factors that actually matter and have been correlated statistically with on-the-job performance and outcomes. When they're calibrated by data science teams that are monitoring the right metrics, they can be engineered to ensure that they have no "adverse impact" on groups protected by gender, race and age. At Evolv, there was a slate of tests that we would apply to any of our scoring algorithms before we deployed them knowing that one's performance on the assessment was basically uncorrelated with their gender, race, or age. In doing so, we trained the algorithms to select the most qualified applicants and to ignore the fact that he or she went to Harvard and plays squash.
The data support this claim. In fact, a white paper was published in November 2015 by the National Bureau of Economic Research by researchers at the University of Toronto, Yale and Northwestern that analyzes hundreds of thousands of hires and finds that the adoption of job testing is associated with a 20 percent reduction in quitting behavior.
If anything, I believe it's more likely that online assessments reduce bias in the hiring process. Consider the fact that recruiters typically spend approximately seven seconds screening each resume. What do they look for? Among other things, they look for previous work history and job-relevant experience. Evolv, where I previously served as chief analytics officer, has released studies demonstrating conclusively that job hoppers and the long-term unemployed stay just as long and perform just as well as individuals with a more typical work history. In fact, the White House released a report in October 2014 about the long-term unemployed in which they cited Evolv's work with companies like eBay / Paypal, AT&T, and Xerox as helping to get the long-term unemployed back to work.
These are factors that shouldn't play a role in the screening process, yet 2 to 6 percent of all job applicants are dismissed immediately because of a less-than-traditional work history. Pre-hire screening reduces personal biases by allowing job hoppers and long-term unemployed to be considered on the basis of their true knowledge, skills and abilities.
The fact that computers are playing a bigger role in the hiring process causes some trepidation, but it's important to realize that these algorithms aren't meant to replace recruiters. They're simply intended to arm recruiters with more information, which they can use to make a more informed decision. It's an exciting era, not only because the technology is capable of issuing recommendations around something as complicated as hiring, but also because this capability is going to give a fair shot to millions of job applicants who wouldn't have been considered previously.