The Most Qualified Candidate Doesn't Always Get the Job

Why more experience doesn't always mean more skills, and how artificial intelligence can help us challenge hiring biases.

What happens when the candidate who demonstrates the strongest skills doesn't get the job?

In a new episode of The New Mainstream Podcast, ThinkNow Co-Founder Mario Carrasco speaks with Trent Cotton, Head of Talent Insights and Analyst Relations at iCIMS, about how data and artificial intelligence are transforming recruitment and, more importantly, how they can help us identify biases that have long been considered a normal part of the hiring process.

When Experience Outweighs Skills

During the conversation, Cotton shares an experience from his career that illustrates how bias can influence hiring decisions even when organizations have objective information about candidates' abilities.

At a previous organization, he implemented an AI-powered technical assessment to evaluate how candidates approached problems related to software development and technology.

For one particular position, the team was looking for a professional with approximately seven or eight years of experience.

Two candidates stood out during the process.

The first had around eight years of experience and scored 68 points on the technical assessment.

The second had approximately two years of experience, had completed a two-year technical education program, and had developed much of his expertise independently through resources such as GitHub, YouTube, and specialized communities. He scored 96 points.

Initially, the team favored the candidate with the higher score. However, after discovering that he had less professional experience, they decided to hire the candidate with eight years of experience.

Three months later, the company had to remove him from the project because he was unable to perform the job effectively.

The problem was that expectations about experience ultimately outweighed the evidence of actual skills.

Hiring Bias Existed Long Before AI

Much of today's conversation about artificial intelligence in human resources focuses on the risks of algorithmic bias. It's a legitimate concern, particularly when systems are trained on historical data that may reflect existing inequalities.

However, Cotton invites us to consider another perspective: traditional hiring processes are not free from bias either.

One example he highlights is recency bias.

Imagine a company posts a job opening and receives 350 applications in less than a day. Although every candidate could potentially be evaluated, the volume of applications often leads recruiters to review only a portion of them.

According to Cotton, a recruiter might review between 20 and 50 applications before selecting the candidates who will move forward.

But what happens if the most qualified person applied shortly after the position was posted and wasn't included in that group? Their application might never be considered, not because they lack the necessary skills, but because of how the review process was organized.

Something similar happens during interviews. An outgoing candidate who communicates confidently and builds an immediate personal connection may leave a stronger impression than someone who is more reserved, even when the latter has greater technical expertise.

In both situations, human perception can influence decisions that should be centered on the abilities required to perform the job.

Can Artificial Intelligence Help Reduce These Biases?

For Cotton, one of AI's greatest opportunities is helping recruiters identify candidates who might otherwise be overlooked in traditional hiring processes.

For example, instead of reviewing applications solely in the order they arrive, an AI-powered tool can analyze which profiles meet the required competencies and present a relevant group of candidates for further evaluation.

Skills assessments can also help challenge assumptions about the relationship between experience, educational background, and job performance.

However, these benefits are not automatic. AI tools can also reproduce inequalities when they rely on biased data, inappropriate criteria, or models that have not been properly evaluated.

Artificial intelligence does not eliminate bias on its own. Its value depends on how it is designed, implemented, and supervised.

Technology Needs Governance, Not Blind Trust

During the conversation, Cotton points out that, according to research on AI adoption in talent acquisition that he references, approximately 50% of the organizations surveyed did not have an AI governance framework in place.

This finding is particularly relevant as companies increasingly incorporate artificial intelligence into processes that can directly affect people's employment opportunities.

We wouldn't expect a newly hired employee to perform their responsibilities effectively without training, guidance, or ongoing support. So why would we expect flawless results from a technology tool without proper oversight?

The Future of Hiring Isn't About Replacing Human Judgment

Artificial intelligence is changing how organizations identify talent, assess skills, and make hiring decisions.

But real progress isn't simply about automating more stages of the recruitment process.

It's about building systems that can identify relevant skills, expand opportunities for evaluation, and help people recognize their own biases.

For professionals in human resources, research, and data analytics, this means adopting a more critical perspective: questioning what information we're using, which criteria we're prioritizing, and which candidates might be getting overlooked.

Discover more about how artificial intelligence, data, and human bias are shaping the future of recruitment in Mario Carrasco's conversation with Trent Cotton, Head of Talent Insights and Analyst Relations at iCIMS, on The New Mainstream Podcast.