Theories of “Gender” in NLP Bias Research
Hannah Devinney, Jenny Björklund, Henrik Björklund
Abstract
The rise of concern around Natural Language Processing (NLP) technologies containing and perpetuating social biases has led to a rich and rapidly growing area of research. Gender bias is one of the central biases being analyzed, but to date there is no comprehensive analysis of how “gender” is theorized in the field. We survey nearly 200 articles concerning gender bias in NLP to discover how the field conceptualizes gender both explicitly (e.g. through definitions of terms) and implicitly (e.g. through how gender is operationalized in practice). In order to get a better idea of emerging trajectories of thought, we split these articles into two sections by time.
Topics & Concepts
OperationalizationGender biasField (mathematics)Natural language processingComputer scienceArtificial intelligencePsychologyData scienceCognitive psychologyEpistemologySocial psychologyMathematicsPure mathematicsPhilosophyHate Speech and Cyberbullying DetectionTopic ModelingWikis in Education and Collaboration