Ageism remains one of the last widely accepted and largely overlooked forms of bias in modern workplaces. While discrimination based on race, gender, and other protected characteristics has faced increasing scrutiny and accountability, age-related prejudice – particularly against ‘mature’ candidates – continues to persist, and is often justified or ignored.
And now, as AI tools become integral to applicant tracking systems (ATS), these inherent human biases are being amplified at scale, leading to discriminatory practices that disproportionately affect older job seekers. But judicial and regulatory challenges globally are increasingly bringing these questionable processes into focus, and local companies should take note, a leadership expert says.
Debbie Goodman, CEO at Jack Hammer Global, Africa’s largest executive search firm, says South African firms should heed the warning being signalled by global developments, and urges organisations to proactively address risks to both avoid legal pitfalls, and – on the carrot side – unlock untapped talent.
She says escalating concerns over AI-induced discrimination are highlighted in global cases, including a high-profile ongoing lawsuit in the United States against Workday, where plaintiffs allege that AI-powered applicant tracking systems (ATS) exhibit bias through disparate impact on older candidates. And in Europe, new regulations under the EU AI Act emphasise that operators cannot evade liability by claiming ignorance or solely blaming vendors when using high-risk screening software in recruitment.
“As the end-user, organisations bear full responsibility for understanding how these technologies function and ensuring compliance with anti-discrimination laws. While South Africa has yet to see court cases on this front, alleged discriminatory practices are under scrutiny in the US and Europe, and local challenges may well follow in future,” Goodman says.
HOW DOES AI BECOME BIASED?
She explains that historically, hiring involved greater human oversight, but that now, AI’s role in scaling applications is exacerbating underlying problems.
“With thousands of resumes flooding in for a single role, manual screening is impractical, pushing reliance on ATS’s that prioritise based on past hiring data. This creates a feedback loop: if organisations have favoured younger demographics in the past, AI systems learn and replicate these preferences, ranking candidates under 40 higher while sidelining those with decades of experience.
“AI doesn’t create bias out of thin air, it amplifies existing human ones,” she says.
“If your shortlists consistently feature younger profiles, the system will train itself to favour them, perpetuating ageism at scale. Add to this the unconscious biases of younger screeners who may view 50-year-olds as ‘over the hill,’ or corporate assumptions about retirement ages, and you have a systemic barrier that’s both unfair and shortsighted.”
However the ageism bias extends beyond AI, Goodman says.
BEYOND THE AI BIAS
“Age bias is frequently rationalised with phrases like organisations needing a 10-year runway, or perceptions over higher salaries. Yet, seasoned candidates – particularly women entering their prime in their 50s with freedom from family commitments – bring unparalleled attributes: institutional knowledge, wisdom, maturity, extensive networks, and intuitive leadership.
“Companies are truly missing a beat by dismissing older candidates based on outdated assumptions about energy, tech-savviness, or cost. These individuals have earned their value through hard work and can open doors, fix problems, and drive innovation in ways younger hires simply can’t yet match.”
From a commercial standpoint, overlooking mature talent also severely limits access to a competitive pool, especially as retirement ages rise and longevity shifts.
“Leading companies are recognising this by rehiring retirees as contractors, gaining an edge through retained expertise. Diverse teams, including age diversity, lead to better strategies and decisions, and drive superior outcomes. Ageism isn’t just unethical; it’s bad for business.”
Goodman says organisations recognising that they might have an ageism blind spot should ensure they audit AI systems for bias, train teams on unconscious assumptions, and embrace age-inclusive policies.
“By doing so, companies can mitigate legal risks, comply with emerging regulations, and harness the full spectrum of talent,” she says.
