Noel Anderson, Steinhardt Director of Leadership and Innovation, and Clinical Professor of Educational Leadership and Policy Studies at NYU, discusses how algorithms can promote diverse hiring practices.
Algorithmic hiring is the use of artificial intelligence and machine learning to source, recruit, interview and hire applicants for jobs. Anderson says the technique employs multiple criteria, including the candidate’s experience, education and even code words used in the resume.
Algorithms can also help to assess softer skills, such as a candidate’s propensity for agile learning and teamwork.
The technology is highly sophisticated and in regular use by businesses, including the “vast majority” of Fortune 500 companies, to initially screen thousands or even millions of resumes. “It whittles it down so that you can target the kind of candidate you want,” Anderson says. “More companies are using it every day.”
AI-based systems have been accused of reflecting the biases of their programmers. Anderson says developers are working hard to ensure that the selections made by algorithms are resulting in a diverse field of candidates. They’re looking at the output of current systems, then adjusting the programming and reorganizing criteria toward that end.
Businesses are especially keen on ensuring that such systems are fair as a means of avoiding discrimination lawsuits, especially in a market where labor is tight, Anderson notes. But efforts at ensuring complete fairness are still a work in progress.
Still, the algorithms are not intended to render the final decision on hiring. They’re most valuable in their ability to narrow down a large field of candidates. Then humans come into the picture.
“You can never get rid of the human factor,” Anderson says. The future, he adds, will see “the interaction of human and machines” in the hiring process.
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