{"id":76639,"date":"2025-04-23T00:29:15","date_gmt":"2025-04-23T04:29:15","guid":{"rendered":"https:\/\/world.edu\/?p=76639"},"modified":"2025-04-23T00:29:15","modified_gmt":"2025-04-23T04:29:15","slug":"ai-is-inherently-ageist-thats-not-just-unethical-it-can-be-costly-for-workers-and-businesses","status":"publish","type":"post","link":"https:\/\/world.edu\/ai-is-inherently-ageist-thats-not-just-unethical-it-can-be-costly-for-workers-and-businesses\/","title":{"rendered":"AI is inherently ageist. That\u2019s not just unethical \u2013 it can be costly for workers and businesses"},"content":{"rendered":"

\"\"<\/p>\n

The world is facing a \u201csilver tsunami\u201d \u2013 an unprecedented ageing of the global workforce. By 2030, more than half of the labour force in\u00a0many EU countries<\/a>\u00a0will be aged 50 or above. Similar trends are emerging across\u00a0Australia<\/a>,\u00a0the US<\/a>\u00a0and other developed and\u00a0developing economies<\/a>.<\/p>\n

Far from being a burden or representing a crisis, the ageing workforce is a valuable resource \u2013 offering a so-called\u00a0\u201csilver dividend\u201d<\/a>. Older workers often offer experience, stability and institutional memory. Yet, in the rush to embrace artificial intelligence (AI), older workers can be\u00a0left behind<\/a>.<\/p>\n

One common misconception is that older people are\u00a0reluctant<\/a>\u00a0to adopt technology or cannot catch up. But this is far from the truth. It oversimplifies the complexity of their abilities, participation and interests in the digital environments.<\/p>\n

There are much deeper issues and structural barriers at play. These include access and opportunity \u2013 including a\u00a0lack of targeted training<\/a>. Right now, AI training tends to be targeted at early or mid-career workers.<\/p>\n

There are also confidence gaps among older people stemming from workplace cultures that can feel\u00a0exclusionary<\/a>.\u00a0Data<\/a>\u00a0shows that older professionals are more hesitant to use AI \u2013 possibly due to fast-paced work environments that reward speed over judgment or experience.<\/p>\n

There can also be issues with the design of tech systems. They are built primarily by and for younger users.\u00a0Voice assistants<\/a>\u00a0often fail to recognise older voices, and\u00a0fintech apps<\/a>\u00a0assume users are comfortable linking multiple accounts or navigating complex menus. This can alienate workers with legitimate security concerns or cognitive challenges.<\/p>\n

And all these issues are exacerbated by\u00a0socio-demographic factors<\/a>. Older people living alone or in rural areas, with lower education levels or who are employed in manual labour, are significantly less likely to use AI.<\/p>\n

Ageism has long shaped hiring, promotion and career development. Although age has become a\u00a0protected characteristic<\/a>\u00a0in UK law, ageist norms and practices persist in many not-so-subtle forms.<\/p>\n

Ageism can affect both young and old, but when it comes to technology, the impact is overwhelmingly skewed against older people.<\/p>\n

So-called\u00a0algorithmic ageism<\/a>\u00a0in AI systems \u2013 exclusion based on automation rather than human decision-making \u2013 often exacerbates ageist biases.<\/p>\n

Hiring algorithms<\/a>\u00a0often end up favouring younger employees. And digital interfaces that assume tech fluency are another example of exclusionary designs. Graduation dates, employment gaps, and even the language used in CVs can become proxies for age and filter out experienced candidates without any human review.<\/p>\n

Tech industry workers are\u00a0overwhelmingly young<\/a>. Homogenous thinking breeds blind spots, so products work brilliantly for younger people. But they can end up alienating other age groups.<\/p>\n

This creates an artificial\u00a0\u201cgrey digital divide\u201d<\/a>, shaped less by ability and more by gaps in support, training and inclusion. If older workers are not integrated into the AI revolution, there is a risk of creating a divided workforce. One part will be confident with tech, data-driven and AI-enabled, while the other will remain isolated, underutilised and potentially displaced.<\/p>\n

An \u2018age-neutral\u2019 approach<\/strong><\/h4>\n

It\u2019s vital to move beyond the idea of being \u201cage-inclusive\u201d, which frames older people as \u201cothers\u201d who need special adjustments. Instead, the goal should be age-neutral designs.<\/p>\n

AI designers should recognise that while age is relevant in specific contexts \u2013 such as restricted content like pornography \u2013 it should not be used as a proxy in training data, where it can lead to bias in the algorithm. In this way, design would be age-neutral rather than ageless.<\/p>\n

Designers should also ensure that platforms are accessible for users of all ages.<\/p>\n

The stakes are high. It is also not just about economics, but fairness, sustainability and wellbeing.<\/p>\n

At the policy level in the UK, there is still a huge void. Last year,\u00a0House of Commons research<\/a>\u00a0highlighted that workforce strategies rarely distinguish the specific digital and technological training needs of older workers. This underscores how ageing people are treated as an afterthought.<\/p>\n

A few\u00a0forward-thinking companies<\/a>\u00a0have backed mid- and late-career training programmes. In Singapore, the government\u2019s\u00a0Skillsfuture programme<\/a>\u00a0has adopted a more agile, age-flexible approach. However, these are still isolated examples.<\/p>\n

Retraining cannot be generic. Beyond basic digital literacy courses, older people need targeted, job-specific advanced training. The psychological framing of retraining is also critical. Older people need to retrain or reskill not for just career or personal growth but also to be able to participate more fully in the workforce.<\/p>\n

It\u2019s also key for reducing pressure on social welfare systems and mitigating skill shortages. What\u2019s more, involving older workers in this way supports the transfer of knowledge between generations, which should benefit everyone in the economy.<\/p>\n

Yet, currently, the onus is on the older workers and not organisations and governments.<\/p>\n

AI, particularly the generative models that can create text, images and other media, is known for producing outputs that appear plausible but are sometimes\u00a0incorrect or misleading<\/a>. The people best placed to identify these errors are those with deep domain knowledge \u2013 something that is built over decades of experience.<\/p>\n

This is not a counterargument to digital transformation or adoption of AI. Rather, it highlights that integrating older people into digital designs, training and access should be a strategic imperative. AI cannot replace human judgment yet \u2013 it should be designed to\u00a0augment it<\/a>.<\/p>\n

If companies, policies and societies exclude older workers from AI transformation processes, they are essentially removing the critical layer of human oversight that keeps AI outputs reliable, ethical and safe to use. An age-neutral approach will be key to addressing this.<\/p>\n

Piecemeal efforts and slow responses could cause the irreversible loss of a generation of experience, talent and expertise. What workers and businesses need now are systems, policies and tools that are, from the outset, usable and accessible for people of all ages.<\/p>\n

Author Bio:<\/strong> Sajia Ferdous is a Lecturer in Organisational Behaviour, Queen’s Business School at Queen’s University Belfast<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

The world is facing a \u201csilver tsunami\u201d \u2013 an unprecedented ageing of the global workforce. By 2030, more than half of the labour force in\u00a0many EU countries\u00a0will be aged 50 or above. Similar trends are emerging across\u00a0Australia,\u00a0the US\u00a0and other developed and\u00a0developing economies. Far from being a burden or representing a crisis, the ageing workforce is […]\n… learn more→<\/a><\/p>","protected":false},"author":33,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[434804],"tags":[12009],"coauthors":[11733],"class_list":["post-76639","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","tag-ageing"],"acf":[],"yoast_head":"\nAI is inherently ageist. 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