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 Photo courtesy of UC Berkeley School of Law.

Experts gathered at UC Berkeley School of Law Friday to celebrate the 50th anniversary of the Age Discrimination in Employment Act with frank discussions on how the problem of ageism has—and hasn’t— changed since the bill passed.

Since 1967, the workplace has seen massive technological advancements. Hundreds or even thousands of people can apply for the same job on sites like Monster. Potential employers can easily access information about applicants’ previous work experience and social life with an online search. Recruiters now screen applicants and generate lists of candidates using complex algorithms.

In a lot of ways, these advancements have life easier. But, according to one panel at Friday’s event, the rise of big data and the internet in hiring has  also hurt older workers, often in unintentional ways.

Speakers on the panel included Ifeoma Ajunwa, an assistant professor at the Cornell University School of Industrial and Labor Relations  and an associate faculty member at Cornell Law School; Toni S. Locklear, the vice president for the Southern Region of Litigation Practice and leader of APTMetrics; Laurie A. McCann, the senior attorney for the AARP Foundation Litigation and P. Casey Pitts an attorney at Altshuler Berzon LLP.

Here are ways that panelists said companies can avoid slipping into ageist hiring practices in an increasingly high tech environment:

  1. Check your algorithm for pattern matching: Hiring algorithms take into account a number of factors when looking for new candidates. Often, they are designed to find hires whose background and resume closely resembles successful people already working at the company. But what algorithms can’t tell is what traits are actually relevant to being a good employee. If most people at a company are under 40, that company’s algorithm may be turning away highly qualified candidates above that age range, simply because they don’t match current demographics. And the more people of a certain demographic get hired, the more likely the algorithm will double down on this group. “If you tell the algorithm, here are the people we’ve already hired, find people who match these, if all the people you’ve hired are under 30 then the algorithm will hire people under 30,” said Ajunwa.
  2. And for unnecessary cut-offs: If the hiring algorithm asks for specific birth date information, graduation information or years of experience, try to avoid cut-off dates. These can cut out qualified candidates above a certain age. “If you say, ‘Accept all the people who have five to ten years’ experience, you’re cutting out people with more than ten years,” Ajunwa said. “If you say find me people who graduated earlier than 2010, that also has an impact on age, even if you may just be thinking I want people with newer skills.”
  3. Watch your wording: Job postings can use language or criteria that isn’t related to job performance but puts older applicants at a disadvantage. Only require physical tests if necessary and focus wording on whether someone has the skills for the job— not when they learned them. “It’s quite common to see ads that say they are looking for digital natives— someone who grew up using tech rather than learning it later in life,” said McCann. “Look up what year you have to be born to be a digital native.”
  4. Be wary of online searches: It’s tempting to do a full background search of every candidate. But older candidates have been in the workforce for a longer time, meaning they may have longer unemployment gaps or more confusing parts to their resumes than those with fewer years of experience. Older workers might have spent significant time at companies that no longer exist or didn’t have a website. “There’s a concern that for older workers whose former workplaces don’t exist anymore or weren’t tech savvy that might be problematic in terms of having previous jobs verified,” Ajunwa said.
  5. Diversify your ads: It’s not uncommon for employers to search for applicants via ads on social media platforms like Facebook. But that method of searching for new employees cuts out many older people, who may not be active social media users. To avoid an ageist job marketing policy, try to diversify where ads are placed. Pitts said this form of ageism can be the hardest to prove. “How do you prove someone has used that technology and how do you find someone who would have been recruited for the job but wasn’t, who was a victim of this technology?” he asked. 

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