New book chapter argues AI image analysis echoes 2000s digital art history's promises and pitfalls
ArtificialOther · x · 2026-11-07
Emily Leifer's chapter in Reimagining Art History with Artificial Intelligence (Springer) argues that LLMs and computer vision in art history aren't unprecedented: the digital art history literature of the 2000s–2010s shows both continuity and rupture with today's AI.
- Shared optimism: both promise to process vast data, spot patterns (especially in image analysis), democratize access, and reveal unexpected connections.
- Shared risks: substituting pattern recognition for interpretation, and neither method automatically mitigates biases in the underlying corpus.
- She proposes comparing both eras' expectations of democratic access and canon-breaking diversity to better evaluate AI's utility in the field.
The chapter itself is paywalled.
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