Patterns without desires

The art expert is the fulcrum of all value and significance in the museum and auction world. Could AI supplant them?

by Noah Charney  BIO

Details from three versions of Caravaggio’s The Lute Player (full paintings below). Courtesy Wikipedia

is a professor of art history and the founder of the Association for Research into Crimes Against Art (ARCA). His books include The Art of Forgery: The Minds, Motives and Methods of Master Forgers (2015), the Pulitzer-nominated Collector of Lives: Giorgio Vasari and the Invention of Art (2017), co-authored with Ingrid Rowland, and The Museum of Lost Art (2018). He lives in Slovenia.

Edited byMarina Benjamin

The art market likes certainty. It prefers painters’ names printed in bold type, dates fixed neatly to a decade, values pinned confidently to price estimates. Yet behind this appearance of assurance lies an industry structured around risk. Enormous sums of money change hands on the basis of attribution (who created this work), and attribution itself often rests on a fragile, negotiated consensus or the determined opinion of one or more experts (some of them more self-proclaimed than objectively expert) rather than an established fact.

A single name on a museum label can anchor the value of an artwork, determine its place in art history and shape scholarly narratives for generations. Change that name, and the consequences ripple outward. A painting can lose tens of millions in value. Is the world’s most expensive painting, Salvator Mundi (‘Saviour of the World’), indeed by Leonardo da Vinci, as many of the top Leonardo scholars confirm? Its last sale price was $450.3 million in 2017 at Christie’s. But scholars of equal renown are convinced that it is a derivative work, not by Leonardo at all, in which case it might be worth only $450,000. That’s quite a difference for an opinion to make. Enough question marks have been thrown up around Salvator Mundi that the painting has yet to be displayed in public since its sale. It is thought to have been purchased by proxy by the Saudi crown prince Mohammed bin Salman to become the centrepiece of the Louvre Abu Dhabi, but it is not on display and its location is not now publicly known.

Salvator Mundi (c1500), attributed to Leonardo da Vinci. Courtesy Wikipedia

Calling attribution into question can cause a museum’s reputation to wobble. A collector’s confidence can evaporate. Governments, insurers, lenders, heirs and institutions all depend on attribution being right, even though they know, at some level, that it might not be. This tension is not accidental. The art market is an overheated system built on partial information, asymmetries of knowledge, and incentives that quietly encourage optimism. Sellers benefit from the highest plausible attribution. Buyers hope that the name on the label will hold. Auction houses rely on inherited scholarly opinions that may be decades old. Museums, once they’ve committed, are rarely eager to reverse themselves. The result is a system that functions not because certainty is common, but because doubt is carefully managed.

Attribution, in this sense, is not merely a scholarly exercise. It is the keystone of an economic and cultural structure. Without it, prices collapse, catalogues unravel, and historical narratives lose coherence. And yet attribution is also deeply human, shaped by judgment, intuition, training and, inevitably, bias.

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For most of art history, attribution rested almost entirely on connoisseurship: the trained eye of experts who compared brushwork, composition and handling across an artist’s oeuvre. Connoisseurs developed astonishing sensitivity to visual nuance, often identifying hands and workshops with remarkable precision. But connoisseurship is also subjective. Two equally qualified scholars might disagree profoundly, and have done, repeatedly, down the centuries. In some cases, their disagreements have lasted generations.

The 20th century introduced new tools that seemed to promise firmer ground. Scientific and forensic analysis allowed scholars to test materials, identify anachronistic pigments, examine underdrawings, and date supports – the panel or canvas on which a painting is made. These techniques revolutionised the detection of forgeries and exposed many celebrated fakes. But they did not solve attribution. Forensic tests are excellent at telling us when something cannot be what it claims to be. They are far less effective at telling us who painted it. Forensic testing is also expensive, time-consuming, and sometimes invasive. Paintings must often be transported to specialist laboratories. Microscopic samples may be removed. Insurance premiums soar. As a result, such analysis is applied sparingly, usually only to works considered important or problematic. The vast majority of artworks entering the global market each year are never tested at this level. And so the art market continues to rely heavily on expert opinion, supported by provenance research and, sometimes, selective scientific evidence. It is a system that works well enough to sustain a multi-billion-dollar industry, but poorly enough to allow persistent uncertainty to thrive beneath the surface.

This uncertainty helps explain why the arrival of artificial intelligence in the realm of attribution has provoked such strong reactions. For some, AI looks like a long-awaited corrective: a way to introduce consistency, scale and empirical rigour into a system long governed by authority and trust. For others, it represents a profound misunderstanding of what art is and how it should be judged. Bendor Grosvenor, a British art historian and television presenter whose work has done much to popularise close looking and traditional connoisseurship, and who has himself identified a number of lost Old Master works, is sceptical. Writing in the Financial Times in November 2025, Grosvenor expressed deep reservations about the use of AI in attribution. His concern is not simply that machines might get things wrong, but that the very premise of algorithmic judgment risks flattening the complexity of artistic creation.

These fears misunderstand what AI image analysis actually does, and what it does not do

Painting, in Grosvenor’s view, is not reducible to pattern recognition. It involves intention, context, decision-making and deviation. Artists break their own habits. They experiment. They collaborate. They respond to commissions, materials and circumstances. To treat attribution as an exercise in statistical matching is to misunderstand the nature of artistic practice itself. There is a danger, he says, that once AI is granted authority, it could marginalise human expertise rather than supplement it. Art history has long resisted rigid systems. Its greatest insights often emerge from anomalies, from works that do not fit neatly into existing categories. An algorithm trained on an artist’s ‘typical’ works might struggle with the atypical, the experimental, or the unfinished. Worse, it might discourage scholars from trusting their own eyes when........

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