The Absurd Robotization Of Humans By Robot Recruiters

A manifestly human recruitment

Automation has invaded everything. In human resources, soon it will only be about automatic screening, algorithmic matching, and remote evaluation. The idea is tempting: save time, reduce bias, optimize sorting. But an uncomfortable question remains: what’s left of the human in this process that is supposed to be about… humans? Detecting humanity through tech?

 

When keywords replace talents

Today, a CV looks less like a life story than an SEO exercise. Human SEO is in full swing. Candidates learn to place the right keywords to pass filters, while training courses teaching how to “beat” ATS filters are everywhere. Personal marketing has overtaken sincerity. Personal branding is on everyone’s lips.

In the end, we get standardized profiles, talents disappearing into the crowd, and a machine that mostly rewards those who know how to “play the game.” In short, the flashiest profile? We end up buying the book for its cover…

In some large companies, the numbers are dizzying. A single role can attract more than 1,000 applications. The AI sorts, keeps 50 profiles, then a recruiter picks 10 to meet. The other 990 are rejected, sometimes without a single human having read a line of their background. On paper, the logic is efficient. But what about hidden potential? And don’t blame the recruiter—he does what he can with the time he has.

 

The promise of objectivity… and its limits

AI advocates remind us that humans are not neutral judges. We all have our biases: preference for prestigious schools, attraction to candidates who look like us, suspicion toward atypical paths. Algorithms, on the other hand, apply the same rule to everyone. In theory, they democratize access and detect invisible patterns. One dreams that the cold algorithmic logic of a trading bot could apply to CV sorting. No emotion, the same rule for all.

But theory quickly hits reality. Because an algorithm learns from data. And if the data reflect a biased system, the algorithm will reproduce and amplify that bias. The now-famous case of a global company proved it: its recruitment AI favored male candidates simply because it had been trained on past recruitment histories where men dominated. Behind the promise of fairness hides an invisible standardization.

 

The illusion of perfect measurement

Another paradox appears: the more we measure, the more we think we understand. The more comfortable we feel. But numbers don’t tell the whole story. A technical skill can be easily validated through a test. But how do you capture the energy someone brings to a team, their ability to inspire trust, or their talent for defusing conflict? These are often the qualities that make the real difference in a company’s daily life.

By quantifying everything, we reduce humans to a grid. We think we’re gaining objectivity, but we’re losing nuance. We standardize. We forget that the greatest talents are not defined by what they write about themselves, but by what others perceive in them.

 

Towards manifestly human algorithms

This is where fairception’s approach shifts. Instead of measuring only what candidates declare, why not listen to what their colleagues, managers, or partners say about them? Collective perception often reveals more than introspection. An employee may be unaware that they are seen as an excellent mediator. Another may not dare call themselves creative, while everyone recognizes their inventiveness.

The algorithm of the future is not meant to replace the human eye but to enrich it. It highlights what individuals don’t always see in themselves. It transforms raw data into human insights. It does not standardize—it singularizes.

In short, technology must stop mimicking humans in order to better reveal humanity.

 

The future of recruitment lies in alchemy

In five years, the most successful companies will not be the ones with the most powerful algorithms, but those that manage to create an alchemy of three forces: technological precision, the truth of collective intelligence, and the nuance of human perception. This alliance will allow the singularity of talents to be recognized instead of flattened into a database.

At the end of the day, a machine does not recognize humanity. It classifies, it calculates, it compares. But it’s up to us to feel what makes someone unique. If AI has a purpose, it is not to decide for us but to show us what we hadn’t seen. That is not the dehumanization of recruitment—it is a way to make it more human.