Brains Over Bias

Why Measuring the Brain Helps Reduce Bias in Hiring

In conversations about fair hiring, the word bias comes up quickly, and for good reason.

Research shows that recruitment processes can be influenced by (unconscious) bias. When these biases influence decisions, organizations might miss the highly capable candidates, while talented individuals miss out on opportunities.

At BrainsFirst, our mission is to help organizations discover talent based on how people think. Our game-based assessments measure core cognitive functions such as attention, memory, planning, and information processing speed. These cognitive functions are fundamental building blocks of how people process information, learn, solve problems, and adapt to new situations.

By measuring these cognitive abilities, we assess cognitive potential rather than accumulated knowledge, educational background, or a polished CV. This approach fundamentally shifts the focus in hiring: instead of asking “What has someone done before?”, we ask “What is this person capable of doing?”. In other words, we focus on the underlying thinking abilities that help people perform and grow.

Because our assessments are non-verbal and measure cognitive functions, they minimize the cultural, language, and gender biases that are often present in traditional selection methods. Instead of relying on signals such as language proficiency, educational background, or CV presentation, our assessment focuses on fundamental cognitive abilities. This enables organizations to evaluate candidates based on their cognitive potential to provide a more objective starting point for comparing individuals from different backgrounds.

This results in a fair hiring process that identifies an individual that truly fits a role.

Objective Data for Fair Decisions

A key strength of the BrainsFirst approach is that it relies on objective, measurable data. Several factors help ensure that candidates are evaluated in a consistent and unbiased way:

Relevant norm groups

Individual assessment scores are always interpreted using scientifically validated norm groups. A norm group is a reference population consisting of individuals with similar relevant characteristics, such as age and gender.

Using norm groups ensures that a candidate’s results are interpreted in the right context. Without this comparison, natural differences between demographic groups could lead to misleading conclusions. By comparing individuals with an appropriate reference group, BrainsFirst ensures that performance is evaluated accurately across different populations.

Bias testing and correction

Before a model is used in practice, we test it extensively to check whether it produces different outcomes for specific groups. To do this, we apply the model to large and diverse datasets, typically with more than 14,000 participants from different genders, age groups, educational backgrounds and ethnicities.

This allows us to analyze whether the model unintentionally favors or disadvantages certain groups. If we detect such patterns, we adjust the model to correct them. By testing and refining the models in this way before they are implemented, we actively prevent unintended bias in the assessment outcomes.

Language- and experience-independent assessment

Our assessments are designed in a way that language proficiency and prior experience with games do not influence the results. The games are largely non-verbal and focus on fundamental cognitive processes, rather than language comprehension or learned knowledge.

This means candidates are not advantaged or disadvantaged by their educational background, reading ability, or familiarity with assessments. Instead, the tasks measure stable cognitive abilities such as attention, memory, and information processing. By removing language and experience as determining factors, the assessment creates a more equal starting point for candidates from different backgrounds.

From Bias Reduction to Real Diversity

Organizations that use the BrainsFirst methodology often see a noticeable increase in diversity across educational backgrounds, work experience, and personal profiles.

Several real-world cases illustrate this effect:

Gemeente Amsterdam implemented BrainsFirst's game-based assessments to replace CVs and motivation letters for their Algemeen Traineeship. This resulted in the creation of a more diverse talent pool.

Gemeente Rotterdam hired candidates who had previously been rejected based on their CVs but excelled in practice, contributing to sustainable employability and strategic HR insights.

Luchtverkeersleiding Nederland (LVNL) reduced educational requirements based on cognitive data, resulting in a more diverse intake and a 30% increase in successful training completions.

These examples highlight an important insight: when you focus on how people think, previously hidden talent becomes visible.

A Fair Starting Point for Every Candidate

In conclusion, BrainsFirst believes that every candidate deserves an equal starting point regardless of background, education, or experience.

When organizations focus on potential rather than assumptions, everyone wins. Because sometimes, the best talent is simply waiting to be discovered.