The shortage of skilled workers is a reality—as is the growing use of artificial intelligence (AI) in HR processes. What happens when these two trends collide is explosive. Increasingly, algorithms are sorting out applicants—not because they lack qualifications, but because they are “too old.” And this happens fully automatically. Without awareness. What sounds like science fiction has long been reality. ?? A class action lawsuit is currently underway against Workday in the US. The accusation: people over the age of 40 were systematically disadvantaged – through automated selection processes. No human eye, no fair chance. In Germany, Workday is under observation for data protection violations in its test system. The problem lies not only in the technology, but also in the training data: AI is not an objective decision-maker. It learns from what we teach it. And if this historical data contains age discrimination, prejudices become entrenched – and scale up. At a time when companies are desperately searching for skilled workers, it is paradoxical to systematically exclude entire population groups. Discriminating against people because of their age, but also favoring them “without reason” because of their age, is business nonsense. Age should not play a role. We are looking for “the best fit” and not “the right age,” and there is no such thing as “too old” or “too young.”
Stereotypes at all levels – not just in HR
The challenge extends beyond the recruitment process. Stereotypical images of age also have a lasting impact on product development, marketing, design, and even innovation planning. They are in the minds of teams—and often in AI algorithms as well. When these two levels interact, the result is products and services that either fail to reach older people (stigma) or exclude them (overload). The result: discrimination by design.
What companies need now
Skills for demographic change – in all departments. Training for managers, developers, and data scientists: What is age bias? How can you recognize it? And how can you counteract it? Diverse data – in terms of age, life situation, tech experience, and perspectives. One-sided training data produces one-sided results.
A new attitude: Age is not a deficit. It is a resource.
We all have a future self. Age discrimination does not only affect “other people.” It also affects us—sooner or later. Whether you are a 40-year-old IT developer looking for a job or a 60-year-old wanting to take out insurance. Those who allow older people to be ignored or even excluded today are building a world in which they too will eventually be overlooked. It is time to break this cycle. With fair AI, smart data, and the will not only to accept diversity, but to actively shape it.