Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP

By Anna Rogers, Timothy Baldwin, Kobi Leins. 2021

78 citations recorded by the source corpus.

A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core legal and ethical principles for collection and sharing of textual data, and the tensions between them. We propose a potential checklist for responsible data (re-)use that could both standardise the peer review of conference submissions, as well as enable a more in-depth view of published research across the community. Our proposal aims to contribute to the development of a consistent standard for data (re-)use, embraced across NLP conferences.

Subfields: Artificial Intelligence, Public Health, Environmental and Occupational Health, Safety Research, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, methodology): “In the future, we also suggest creating model evaluation checklists like those by Rogers et al. (2021) for responsible data use or Dodge et al. (2019) for reporting hyperparameters and compute infrastructure.”
Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022, background): “PREAMBLE BigScience is an open research collaboration involving over 1000 participants from 60 countries, focusing its collaborative research efforts in the study and development of natural language processing systems (hereinafter NLP).”