The Eye, the Algorithm, and the Fight Over What Counts as a Van Eyck
For roughly a century, two paintings of Saint Francis of Assisi Receiving the Stigmata—one in Turin, the other in Philadelphia—have carried Jan van Eyck's name. That changed this year when Swiss company Art Recognition analyzed both with an AI model and concluded that neither was by Van Eyck, assigning the results as much as 91 percent certainty. Maximiliaan Martens of Ghent University, a leading Van Eyck authority, was unconvinced. Van Eyck's technique is so subtle that even microscopes struggle to reveal his brushwork. What, then, had the algorithm actually learned? More fundamentally, there is no agreed scholarly baseline for these panels from which the system could reliably learn.
The disagreement exposes the central problem. AI can produce an impressive probability, but that number is only as meaningful as the evidence and methodology behind it. Scholars can explain their reasoning; proprietary algorithms often cannot. We have encountered this problem with other opaque systems, but the stakes are different here. The machine is not merely assisting researchers. It is challenging judgments built through centuries of artistic scholarship.
Art Recognition faced a similar dispute last year over Rubens's The Bath of Diana. Its model identified the artist's hand in a painting long regarded as a copy. Nils Büttner, chairman of the Centrum Rubenianum and curator of Rubens's catalogue raisonné, rejected the conclusion. According to him, the painting's condition ruled out an authentic Rubens. He also pointed to a flaw in the training material: the dataset did not include the composition scholars recognize as the relevant studio version. The system may have been highly confident, but confidence cannot compensate for incomplete evidence. A precise percentage can look scientific while still being fundamentally wrong; precision has never guaranteed accuracy.
Human experts, however, hardly have an unblemished record. The Rembrandt Research Project repeatedly revised its opinions about individual works, illustrating how attribution can change as scholarship evolves. The Van Eyck and Rubens disputes therefore should not be reduced to machines versus humans. The unresolved issue is how AI-generated conclusions should be treated as evidence. Are they laboratory findings, expert opinions, or sophisticated guesses? The art market has yet to settle that question, even though the financial consequences can be enormous.
Meanwhile, criminals are using generative AI for a more straightforward purpose: fraud. Traditional counterfeiters had to create a convincing object. Now they can fabricate the paperwork instead. AI can generate appraisals, invoices and provenance documents that appear credible enough to survive an initial review. One insurance investigator examining a claim involving a major collection found dozens of seemingly legitimate certificates before metadata exposed the deception. The collection itself appeared to be fictional. There was no forged canvas to inspect—only an invented history surrounding nonexistent art.
That development reveals the larger vulnerability. The art trade depends heavily on documentation and trust, and AI is now attacking both. On one side, it can manufacture convincing evidence for objects that never existed. On the other, it can generate authoritative-sounding judgments about disputed works. Companies developing provenance systems and knowledge graphs may help strengthen the paper trail. But the fundamental question remains unchanged: when evidence is incomplete or ambiguous, who gets the final say? For now, experts still hold that position—but their authority is increasingly contested. AI has not replaced connoisseurship. Instead, it has exposed just how much authentication has always depended on trust.