方法证据记录
Gender Bias Detection
Gender bias detection in NLP is a family of statistical and embedding-based methods used to measure stereotyping, representational imbalance, and occupational bias in text corpora and language models. Grounded in benchmarks established by Caliskan et al. (2017) with the Word Embedding Association Test (WEAT) and Zhao et al. (2018) with the WinoBias dataset, these methods produce quantitative evidence of gender bias rather than qualitative impressions. They are widely applied in ethical AI research, media analysis, and fairness auditing of machine-learning systems.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Gender Bias Detection in NLP — Statistical and Embedding-Based Methods
分类方法记录 · process-pipeline / text-mining
- Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. · DOI 10.1126/science.aal4230
- Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2018). Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. Proceedings of NAACL-HLT 2018. · URL
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