Can Ageism and A.I. Coexist?
I recently read an article about ageism, and how companies are struggling to effectively manage it. A short time later I read about Artificial Intelligence (AI), and how it will make Talent Acquisition (TA) more efficient. So, I started thinking…how will AI and ageism coexist?
Bias is practiced every day by everyone, starting with personal preference for morning coffee and what goes in it, to our taste in cars (Tesla), to what to buy on Amazon, and more.
We also have strong biases in technology preference: Mac or PC, iPhone or Galaxy, Samsung, or LG?
When it comes to talent selection practices, however, we are trained to actively ignore bias. This is often easier said than done, particularly for those downstream of TA. Try as they might, hiring managers often hold preconceived ideas of what their ideal candidate will look, walk, talk, and act like, technical qualifications aside.
Our job as TA professionals is to mentor and reinforce proper bias management through continual, sometimes intense, coaching of hiring managers and their team members.
Recent trends are strongly focused on diversity, but the other big elephant in the room is age, and everyone knows it. I was once told by my manager as I was preparing a manpower plan, to reduce the headcount of “some of the older, more experienced engineers.”
Because, he noted, “do you realize that we can hire two new college graduates for what we pay for one of the older, more expensive engineers?”
Nice. That less-than-casual advice had to come from somewhere.
So how do we un-train behavior when it’s part of corporate culture or strategy? While it is difficult to purge this way of thinking, we can do several things to help mitigate it.
One way is through continual training and cultural reinforcement. Another way is through technology. Let’s take a look at both.
Start with your company’s employee handbook and discrimination policy. The EEOC can assist with guidance on federal laws, like the Age Discrimination in Employment Act (ADEA), which forbids age discrimination against people who are age 40 or older, and the Older Workers Benefit Protection Act (OWBPA), which is an amendment to ADEA.
There are also a host of state and local laws and ordinances too. For example, in California, older workers are protected under the California Fair Employment and Housing Act (FEHA), which strictly prohibits discrimination based on age.
Then measure compliance and develop strategies to address deficiencies using information held within your Applicant Tracking System (ATS). This is standard demographic information collected from candidates during the application process. This will not only help ensure that your organization can accurately measure and report compliance information, but also identify potential demographic areas to target for Recruitment Marketing outreach or other TA-related initiatives.
Use this information to regularly remind, reiterate, and reinforce at the management level. This will act to remind them of your company’s compliance and diversity goals on an ongoing basis.
Talk about them at related training or general informational sessions at every given opportunity. Management is more compelled to reinforce positive downstream behavior when this information is promoted regularly and institutionalized into company culture.
While training can help reinforce or realign corporate culture, technology is playing an ever-increasing role in reducing ageism and other biases. Tools like Textio help “de-bias” job descriptions to make postings more inclusive.
Textio uses AI not only to help reduce gender and other biases, but also now has ‘age-inclusive guidance’ that takes bias interruption beyond gender by raising writers’ awareness of the unintended bias that may be excluding people across different age groups, including over-40 applicants.
Additionally, there should be a strong technology alliance between your Recruitment Marketing team and Talent Acquisition. Recruitment Marketing’s use of Brand-Lead/ Relationship-Based methods can tell your company’s story and help connect you with specific talent pools.
In conjunction with TA, RM can articulate your company’s brand, culture, and diversity story to build strong relationships over time, and can focus specifically on the over-40 demographic not only for general branding purposes but in conjunction with specific recruitment campaigns.
RM uses general posting, employee blogging, push notifications, and more to reach the company’s followers and through brand reinforcement and promotion, and can lead to a more predictive, robust, and automated candidate pipeline.
Symphony Talent employs automated programmatic media for company brand, recruitment, and talent pools. Resulting in the ability to promote across demographics, or to specific demographics.
It also can present ‘blind’ automated matches to postings for the recruiter to review, which tends to mitigate initial resume review bias.
These are just a couple of the many ways to help combat ageism in the hiring process. We still have a long way to go, and the technology also still needs plenty of work. People are either optimistic or worried about current or upcoming forms of AI in conjunction with recruitment efforts.
Sometimes they are successful, whereas other well-intentioned efforts have failed. For example, Amazon famously had to scrap its AI recruiting bot when the company discovered it was biased against women. Apparently, the bot had studied previous hiring trends, saw that most hires were men, and concluded that was the norm.
It proceeded on that premise until Amazon noticed its numbers headed in the opposite direction of its intended plan. Lessons are learned from such mistakes, and the tools will get smarter.
Some parting thoughts:
First, if you institutionalize an aggressive blend of both training and technology, you will diminish discrimination and bolster diversity across all fronts.
Second, don’t ‘burn the library’ by abandoning the wealth of knowledge that ‘seasoned talent’ brings to the table. If you think it is wise to “hire two college grad engineers for the price of one senior engineer” go ahead, but kiss your company’s tribal knowledge goodbye. While parting with company practices and knowledge may be desirable in limited cases, most often it is not.
Third, perhaps it’s too early to call what we’re using in our industry artificial intelligence. Perhaps ‘automated intelligence’ is closer to what we have at present?
And finally, Diversity and Inclusion should cut across all spectrums, including and especially, the over-40 crowd. Hopefully current, then newer platforms will get us there. And soon.