Age-Inclusive AI Matters

Ageism is real. It’s one of those sneaky biases that does more harm than we realize—especially when it slips into AI systems that are supposed to serve all of us. The way we design AI can either challenge harmful stereotypes or deepen them. And unfortunately, ageism—prejudice or discrimination based on a person’s age—is becoming a growing issue in the tech world. If we’re not careful, those same biases will make their way into the very systems we rely on every day.

Ageism in AI isn’t just a tech problem—it’s a societal one. Older adults make up a growing demographic of the global population, particularly in the global North, yet they’re often left out of the picture. AI systems that overlook or misrepresent them can cause real damage, reinforcing exclusion and marginalization where inclusion should be the goal.

Think about the tech industry: workers over 55 make up only 17% of professionals in the field. And that’s even though nearly half of all tech workers are nervous about losing their jobs because of their age. It’s not just a matter of job security—it’s about how these biases erode confidence and limit opportunities for people who still have so much to offer. A lot of this comes down to tired misconceptions, like assuming older people are resistant to change or struggle with technology. And when that mindset creeps into the design of AI, the results can be devastating.

AI systems learn from the data they’re given. If that data is loaded with ageist assumptions or doesn’t include older adults, the AI will carry those biases forward. Think of voice recognition systems that can’t understand older voices, or healthcare algorithms that prioritize younger patients—these issues only widen the gap. This is a tech fail and a disservice to a large part of the population. Even something as simple as chatbots assuming everyone is young or digital interfaces that don’t account for declining vision or motor skills are clear examples of how ageism sneaks into AI.

The effects go even deeper. Recruitment algorithms, for example, have filtered out older candidates—a problem highlighted in lawsuits against major corporations like IBM and HP, where older workers were laid off in favor of younger ones. Not only is this unethical, but it also robs these companies of the experience and insight that can drive innovation.

However, if we get it right, AI can help break down these barriers. Developing age-inclusive AI means creating technology that respects and benefits everyone. It starts with a few key steps:

- Diverse Datasets: AI needs to be trained on data that includes people of all ages and experiences. This is how we ensure decision-making is fair and doesn’t favor one group over another.

- Inclusive Design: Older adults should be part of the process when designing and testing AI products. Their input can expose hidden ageist assumptions—like complicated interfaces or unintended biases in language.

- Rigorous Testing: It’s essential to test AI systems to make sure they work for everyone, regardless of age. This means going beyond the basics and seeing how well these products perform for older users, then making necessary changes to improve accessibility.

-A Call to Action for Companies-

So, what can companies do? For starters, they need to rethink how they hire and retain talent. Simple changes—like avoiding age-biased language in job descriptions (think "digital native" or "young, dynamic team")—can make a big difference. Blind hiring practices and upskilling opportunities can give older employees the tools to thrive in fast-paced industries. Companies also need to include age in their Diversity, Equity, and Inclusion (DEI) strategies. Research from Boston Consulting Group shows that diverse teams—across gender, race, and yes, age—generate 19% more innovation-driven revenue than less diverse ones. Intergenerational teams clearly offer diverse perspectives that can make AI more inclusive and effective. So, these aren’t just feel-good initiatives—they’re good for business, too.

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Ageism in AI is complex, but not unsolvable. In the same way that representation matters in media and politics, it’s crucial in AI development. The voices and experiences of older adults should be present at every step, from brainstorming to deployment.