Documenting Computer Vision Datasets

Abstract

In industrial computer vision, discretionary decisions surrounding the production of image training data remain widely undocumented. Recent research taking issue with such opacity has proposed standardized processes for dataset documentation. In this paper, we expand this space of inquiry through fieldwork at two data processing companies and thirty interviews with data workers and computer vision practitioners. We identify four key issues that hinder the documentation of image datasets and the effective retrieval of production contexts. Finally, we propose reflexivity, understood as a collective consideration of social and intellectual factors that lead to praxis, as a necessary precondition for documentation. Reflexive documentation can help to expose the contexts, relations, routines, and power structures that shape data.

Cite this

Milagros Miceli, Tianling Yang, Laurens Naudts, Martin Schuessler, Diana Alina Serbanescu, Alex Hanna (2021). Documenting Computer Vision Datasets. ACM Conference on Fairness, Accountability, and Transparency (FAccT). https://doi.org/10.1145/3442188.3445880

@article{milagros2021documenting,
  title  = {Documenting Computer Vision Datasets},
  author = {Miceli, Milagros and Yang, Tianling and Naudts, Laurens and Schuessler, Martin and Serbanescu, Diana Alina and Hanna, Alex},
  year   = {2021},
  journal = {ACM Conference on Fairness, Accountability, and Transparency (FAccT)},
  doi    = {10.1145/3442188.3445880}
}

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