• DOI:10.48550/arXiv.2211.13709
• Corpus ID: 254017505

Undesirable biases in NLP: Averting a crisis of measurement

@article{Wal2022UndesirableBI,
  title={Undesirable biases in NLP: Averting a crisis of measurement},
  author={Oskar van der Wal and Dominik Bachmann and Alina Leidinger and Leendert van Maanen and Willem Zuidema and Katrin Schulz},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.13709}
}

• Oskar van der Wal, Dominik Bachmann, +3 authors K. Schulz
• Published 24 November 2022
• Psychology
• ArXiv

As Natural Language Processing (NLP) technology rapidly develops and spreads into daily life, it becomes crucial to anticipate how its use could harm people. However, our ways of assessing the biases of NLP models have not kept up. While especially the detection of English gender bias in such models has enjoyed increasing research attention, many of the measures face serious problems, as it is often unclear what they actually measure and how much they are subject to measurement error. In this paper, we provide an interdisciplinary approach to discussing the issue of NLP model bias by adopting the lens of psychometrics — a field specialized in the measurement of concepts like bias that are not directly observable. We pair an introduction of relevant psychometric concepts with a discussion of how they could be used to evaluate and improve bias measures. We also argue that adopting psychometric vocabulary and methodology can make NLP bias research more efficient and transparent.