Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science

Bender, E. M., & Friedman, B. (2018). Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science.

Type

Journal article

Intellectual base

Primary research

Year

2018

Link

https://doi.org/10.1162/tacl_a_00041verified

Cited from

linguistics_semiotics

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Variation and Sociolect
  • … that stigmatize vernacular sociolects produce identities of shame; programs that validate vernacular alongside standard create code-meshing opportunities. AI/NLP fairness confronts sociolect bias in language models; data statements and demographic annotation are emerging frameworks (Bender and Friedman 2018)

    This sourceProposes data statements for transparency in NLP training data; addresses sociolectal bias and demographic variation.

Verification

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