@article{excavatingai2019,
  title = {Excavating AI (the politics of image training data)},
  author = {Crawford, K. and Paglen, T.},
  year = {2019},
  journal = {Excavating AI (essay; AI and Society, 2021)},
  url = {https://excavating.ai/},
  abstract = {Crawford and Paglen's "Excavating AI" examines the ImageNet large-scale image dataset, documenting the demeaning, biased, and harmful categories used to label photographs of people. First published as an essay in 2019, with a peer-reviewed version appearing in the journal AI and Society in 2021, it established the politics of image training data as a central problem in AI ethics for anyone working with visual media. The essay is widely assigned in AI-ethics and critical media courses and is the founding text for discussing how training datasets reproduce social bias.},
  keywords = {training-data, ip-and-copyright, ai-literacy},
  note = {AI \& Animation Education Knowledge Base}
}