Age bias can have significant ethical, political, and career implications, particularly in the development of fair and unbiased AI systems. If AI models are trained using datasets that primarily represent younger individuals, it can significantly impact the accuracy, fairness, and effectiveness of AI systems in future careers and job roles, hindering progress and potential benefits of AI.

Career implications

Job Discrimination: AI-powered recruitment systems often rely on algorithms to screen and shortlist candidates. If these algorithms are biased towards younger candidates, older job seekers may face discriminatory practices, resulting in exclusion from opportunities and perpetuating ageism in the workplace. Age bias in AI can hamper the hiring process by preventing skilled and experienced individuals from securing jobs based on their abilities rather than age.

“With the age demographic change being described as a global mega trend, multi-generational inclusion is the only fully inclusive approach to eliminate diversity debt.” – Steve Anderson, CEO and Founder of the Age Diversity Forum

Limited Access to Resources: As AI becomes more widespread in society, organisations that rely heavily on these technologies might overlook the needs of older employees due to limited experience with and understanding of the technology being used. Age bias can result in less training being provided to employees who may not be considered ‘digital natives,’ limiting their access and causing them to miss out on opportunities.

“Digital inclusion should – by definition – be for everyone, and step-by-step, learner-by-learner we are determined to make sure it is.” – Professor Rachid Hourizi MBE, Director of the Institute of Coding

The importance of diverse age representation in AI development

It is vital to ensure that different age groups are represented in the development and programming of AI systems, both in terms of data sets used and demographics of coders. The lack of diversity in AI has been described as a “diversity debt,” which could lead to future issues in the use of these technologies. In particular, this diversity should include individuals who are older and can bring their unique perspective to the development of AI algorithms. It is important to overcome age bias in AI development to prevent the perpetuation of ageism in society.

Ethics and Policy

Age bias in AI can have ethical and policy implications, potentially perpetuating ageism in the workplace and systemising values that exclude older people. As AI becomes more prevalent in society, there is a growing need for appropriate policies and regulations to address age discrimination, ensuring that fairness is maintained and potential harm to certain groups is avoided.

The only answer is for educators, employers and government to work together to deliver more digital skills to more people in a growing range of ways.  Through a network of industry partnerships, the Institute of Coding is doing just that for everyone from sixth form leavers to silver surfers. To increase your digital skills, at any stage of life, view our latest courses here.

References:

1. Ross, C. (2019). Addressing the diversity debt in artificial intelligence. The Lancet Digital Health, 1(6), e260-e261.
2. Adolphs, S., Parry, G., & Schaefer, D. (2021). Hiring algorithms: can they help employers make better hiring decisions? Organizational Dynamics, 50(4), 100849.
3. World Economic Forum. (2021). Towards a Reskilling Revolution: Industry-Led Action for the Future of Work. Geneva.

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