AI Claims to Judge Russians' Job Suitability From Their Faces — and Developers Are Pitching It to the State

A technology has appeared in Russia that supposedly determines a person's professional suitability from a face photo in a matter of seconds. The developers have announced talks with government bodies, and if those succeed, the tool could become part of state hiring procedures. It sounds like a dystopia, but it is already happening.

What the system is

The product is built on a neural network trained to analyze facial features and draw conclusions about an applicant's professional qualities. According to the developers, the system rates reliability, stress resistance, leadership tendencies and several other parameters. The result comes back as numerical scores within seconds, with no questionnaires, interviews or psychological tests.

The creators say the algorithm was trained on a dataset of tens of thousands of cases involving real employees whose performance indicators had been documented. This, they claim, is what separates the system from physiognomy and makes it "scientific". There is no independent verification of this data in the public domain.

The key argument in negotiations with clients is speed and scalability. Traditional initial candidate screening takes minutes or hours. The algorithm does it in seconds and never gets tired. For organizations that hire in high volumes, this looks attractive, especially if nobody digs into what the algorithm actually measures.

Heading for the state level

According to CNews, the developers are in active talks with HR departments at state enterprises and security agencies. If pilots succeed, the technology risks becoming part of standardized hiring procedures in the public sector.

This changes the scale of the problem fundamentally. Corporate HR with algorithmic screening is one thing. Government hiring, where the decision determines a person's access to service, benefits and social status, is something else entirely. The concrete consequences of such a scenario:

• An algorithm, not an accountable human, decides whether to hire or reject
• The candidate does not know what in their face affected the score
• The employer can point to an "objective AI" without explaining the reasons for a rejection
• Challenging an algorithmic decision in court is extremely difficult without disclosure of the model
• Systemic errors and biases automatically scale across the entire public sector

Why science is against it

The academic community established long ago that facial features do not predict professional performance. This is not a matter of debate. It is a settled consensus, recorded in hundreds of peer-reviewed studies in psychology, organizational behavior and HR analytics.

The link between appearance and competence is a cognitive bias known as the "physical attractiveness effect", also called the halo effect. People tend to attribute greater professionalism to those who look more attractive. When a neural network is trained on hiring data collected by humans, it reproduces exactly this bias, only faster and with the appearance of mathematical objectivity.

"Any algorithm trained on historical hiring data reproduces historical

biases, only faster and at greater scale" — a standard conclusion of research on algorithmic bias in HR.

Notably, in the European Union, biometric systems for automatically assessing candidates are effectively banned in the hiring context under the AI Act. In the US, companies have lost lawsuits over algorithmic screening with a proven discriminatory effect. Russia has no comparable legal barrier yet.

What this means

The "AI that reads faces" story is a telling example of how marketing packaging for a neural-network product makes it possible to sell an unscientific tool to large clients. The technological authority of AI creates an illusion of objectivity where there is none and cannot be.

The main risk is not that the system works, but that it will be adopted precisely because it looks convincing, and nobody is left to examine the details. Government scale multiplies the potential damage: thousands of people could be refused employment because of the shape of their nose or the distance between their eyes, without even knowing that a machine evaluated them at all.