In sunny morning of October 19, 2025Four men allegedly walked into the world's most visited museum and walked out minutes later with 88 million euros ($101 million) worth of crown jewels. The theft from Paris's Louvre Museum, one of the most controlled cultural institutions in the world, took just under eight minutes.
Visitors continued to browse. Security did not react (until the alarm went off). The men disappeared into city traffic before anyone realized what had happened.
Investigators later found out that the thieves were wearing reflective vests, masquerading as builders. They arrived in the furniture elevator, which was common in the narrow streets of Paris, and used it to ascend to a balcony overlooking the Seine. Dressed like workers, they looked like they belonged here.
This strategy worked because we don't see the world objectively. We see it through categories – through what we expect to see. The thieves understood the social categories we perceive as “normal” and used them to avoid suspicion. Many artificial intelligence (AI) systems operate in the same way and, as a result, are vulnerable to the same errors.
Sociologist Erving Goffman would describe what happened at the Louvre using his concept self presentation: People “fulfill” social roles, taking cues that others expect. Here the idea of normalcy became the perfect camouflage.
Sociology of vision
People constantly engage in mental categorization to understand people and places. When something fits into the “ordinary” category, it escapes attention.
Artificial intelligence systems used for tasks such as facial recognition and detecting suspicious activity in public places work in a similar way. For humans, categorization is cultural. For AI, it's math.
But both systems rely on learned patterns, not objective reality. Because AI learns from data about who looks “normal” and who looks “suspicious,” it learns the categories embedded in its training data. And it does it subject to bias.
The Louvre robbers were not considered dangerous because they were in a trusted category. In the case of AI, the same process can have the opposite effect: people who do not conform to statistical norms become more visible and the object of scrutiny.
This could mean that facial recognition disproportionately flags certain racial or gender groups as potential threats, while others go undetected.
A sociological perspective helps us see that these are not isolated problems. AI doesn't invent its own categories; it learns from us. When a computer vision system is trained on CCTV footage where “normal” is defined by specific bodies, clothing or behavior, it replicates these assumptions.
Just as museum guards looked past thieves because they seemed like one of their own, AI can ignore certain patterns while overreacting to others.
Categorization, whether human or algorithmic, is a double-edged sword. It helps us process information quickly, but it also encodes our cultural assumptions. Both humans and machines rely on pattern recognition, which is an effective but imperfect strategy.
A sociological view of AI sees algorithms as mirrors: they reflect our social categories and hierarchies. In the case of the Louvre, the mirror is turned towards us. The robbers succeeded not because they were invisible, but because they were viewed through the lens of normality. From the AI's point of view, they passed the classification test.
From museum halls to machine learning
This connection between perception and categorization reveals something important about our increasingly algorithmic world. Whether it's a security guard deciding who looks suspicious or an AI deciding who looks like a “shoplifter,” the basic process is the same: placing people into categories based on cues that seem objective but are culturally learned.
When an artificial intelligence system is called “biased,” it often means that it reflects these social categories too closely. The Louvre robbery reminds us that these categories don't just shape our attitudes, they shape what we notice.
After the theft, France's Culture Minister promised new cameras and tighter security. But no matter how advanced these systems become, they will still rely on categorization. Someone or something has to decide what constitutes “suspicious behavior.” If this decision is based on assumptions, the same blind spots will remain.
The Louvre heist will be remembered as one of the most spectacular museum thefts in Europe. The thieves succeeded because they mastered the sociology of appearance: they understood the categories of normality and used them as tools.
And in doing so, they showed how both people and machines can mistake compliance for safety. Their success in broad daylight was not only a triumph of planning. It was a triumph of categorical thinking, the same logic that underlies both human perception and artificial intelligence.
The lesson is clear: before we teach machines to see better, we must first learn to question how we see.
Vincent Charlesreader on artificial intelligence for business and management, Queen's University BelfastAnd Tatiana GermanAssociate Professor, Department of Artificial Intelligence for Business and Strategy, University of Northampton. This article has been republished from Talk under Creative Commons license. Read original article.
