This review has now been published in Critical AI at this link https://doi.org/10.1215/2834703X-11700291; an extract is pasted in below. If your institution lacks access to Critical AI please encourage them to subscribe. If you are an independent scholar please write to criticalai@sas.rutgers.edu.
EXTRACT:
Fei-Fei Li’s memoir The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI is not exactly the kind of book one would expect to be reviewed in an academic journal like Critical AI. But Li’s memoir does address one particular problem that the critical AI community has been struggling with: how to tell the complete story of a massive training data set like ImageNet.
Critical AI readers will already be familiar with the attempts to write a critical genealogy of the machine learning data set. One key early academic article was published in Big Data and Society in 2021: “On the Genealogy of Machine Learning Datasets: A Critical History of ImageNet” by Emily Denton et al. Regarding ImageNet specifically, academic work has been complemented by creative experimentation with database aesthetics like Kate Crawford and Trevor Paglen’s excavating.ai. Furthermore, Nicolas Malevé and Katrina Sulis’s critical analysis…