A concerning number of young workers feel disadvantaged in hiring processes due to bias, according to recent research.
- Over 37% of workers aged 25-34 and 36% aged 16-24 report experiencing discrimination.
- Accent and ethnicity are leading causes of perceived bias among young adults.
- Older workers report less discrimination but cite age as a primary factor when it occurs.
- Employers are urged to use unbiased AI models to tackle these persistent issues.
Research highlights a significant issue facing younger workers in the UK, with a notable percentage believing they encounter bias in recruitment. Over 37% of individuals aged 25-34, alongside 36% of those aged 16-24, claim to have faced discrimination when seeking employment. This perception of bias underscores critical challenges within modern hiring practices.
The top causes of perceived discrimination diverge between the two younger demographics studied. For those aged 16-24, age, ethnicity, gender, weight, and hair colour were frequently cited. In contrast, the 25-34 age group identified accent and ethnicity as prevalent factors, with gender, class, and height also mentioned. This variance in factors illustrates the multifaceted nature of perceived hiring biases.
In an admittance of bias, over a third of hiring managers acknowledge prejudice against Gen Z candidates, despite the growing demand for qualified personnel in regions like London and the South East. This highlights a juxtaposition of bias with business needs.
Older workers report lower instances of discrimination in hiring processes, with only 12% acknowledging any bias experienced. However, when bias is perceived, it is often attributed to ageism, a concern voiced by half of the older applicants surveyed. This is in contrast to the 26% of all other age groups recognising ageism as a bias factor.
Khyati Sundaram, CEO of Applied, stresses the importance of mitigating bias in recruitment. She advocates for the anonymisation of applications and the implementation of skills assessments using trustworthy AI systems, free from biased training data. Such measures aim to ensure fair evaluation based on merit rather than irrelevant personal attributes.
Tackling bias in hiring is imperative to ensure equal opportunities and meet workforce demands.
