Evaluating the Underlying Gender Bias in Contextualized Word Embeddings

By Christine Basta, Marta Ruiz Costa-jussà, Noe Casas. 2019

226 citations recorded by the source corpus.

Gender bias is highly impacting natural language processing applications. Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources. Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in. In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias. Our analysis includes different measures previously applied in the literature to standard word embeddings. Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased.

Subfields: Artificial Intelligence, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “Surveys, frameworks, and meta-analyses Hovy and Spruit (2016); Larson (2017); McCurdy and Serbetçi (2017); Schnoebelen (2017); Basta et al. (2019); Ethayarajh et al. (2019); Gonen and Goldberg (2019); Lauscher and Glavaš (2019); Loukina et al. (2019); Mayfield et al. (2019); Mirzaev et al. (2019);…”
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation (2019, background): “(2019) and Basta et al. (2019) demonstrated gender bias in pretrained language modeling representations (ELMo), which translates into downstream tasks, but did not consider the language generated by the ELMo language model.”
Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor (2019, background): “5 Recent work also suggests that, compared to word embeddings, contextualised embeddings (Devlin et al. 2018; Peters et al. 2018) might be less biased (Zhao et al. 2019; Basta, Costa-jussà, and Casas 2019).”
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models (2020, background): “A wealth of work has shown that toxicity and social biases in training data are acquired by large pretrained sentence encoders (e.g., gender bias in BERT; May et al., 2019; Zhao et al., 2019; Basta et al., 2019; Kurita et al., 2019).”
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned (2022, background): “Large language models exhibit a wide range of harmful behaviors such as reinforcing social biases (e.g., [47, 28, 1, 33, 7]), generating offensive or toxic outputs [25], leaking personally identiﬁable information from the training data [13], aiding in disinformation campaigns [12], generating…”
Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models (2021, background): “Gender-occupation associations are also discernible from distributional word statistics (Bolukbasi et al., 2016; Caliskan et al., 2017; Basta et al., 2019; Zhao et al., 2019).”
Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “Particular attention has been paid to uncovering, analyzing, and removing gender biases in word embeddings (Basta et al., 2019; Kaneko and Bollegala, 2019; Zhao et al., 2019, 2018b; Bolukbasi et al., 2016).”
Measuring Bias in Contextualized Word Representations (2019, background): “Along similar lines, Basta et al. (2019) noted that contextual word-embeddings are less biased than traditional word-embeddings.”
Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias (2020, background): “Gender bias has surfaced as a major concern in word representations, both static word embeddings (Caliskan et al., 2017; Bolukbasi et al., 2016) and contextualized word representations (Zhao et al., 2019a; Basta et al., 2019; Tan and Celis, 2019).”
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP (2021, background): “…et al., 2016; Caliskan et al., 2017; Zhao et al., 2017; Rudinger et al., 2018; Gonen and Goldberg, 2019; Bordia and Bowman, 2019; Sheng et al., 2019; Basta et al., 2019; Nangia et al., 2020, i.a. ) and are prone to generating toxic texts (Brown et al., 2020; Gehman et al., 2020; Abid et al., 2021).”
Towards Debiasing Sentence Representations (2020, background): “Although there has been some recent work in measuring the presence of bias in sentence representations [14, 4, 33], it is still difficult to debias sentence representations [14].”
Examining Gender Bias in Languages with Grammatical Gender (2019, background): “A few recent studies focus on measuring and reducing gender bias in contextualized word embeddings (Zhao et al., 2019; May et al., 2019; Basta et al., 2019).”
Measuring and Reducing Gendered Correlations in Pre-trained Models (2020, neutral): “Recent work has applied association tests to probe for gender bias in contextualized word embeddings (Basta et al., 2019; Peters et al., 2018; Tan and Celis, 2019) with mixed results.”
Automatic Gender Identification and Reinflection in Arabic (2019, background): “word embeddings in a number of studies (Bolukbasi et al., 2016; Caliskan et al., 2017; Sutton et al., 2018; Basta et al., 2019; Garg et al., 2018; Zhao et al., 2018, 2019).”
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021, background): “…al., 2019) have recently been shown to encode and amplify a range of stereo-typical human biases (e.g., gender or racial biases) (Zhao et al., 2019; Basta et al., 2019; Liang et al., 2020a,b), much like their static embedding pre-decessors (Bolukbasi et al., 2016; Caliskan et al., 2017; Dev and…”
Investigating Gender Bias in Language Models Using Causal Mediation Analysis (2020, background): “Gender bias has surfaced as a major concern in word representations, with strong effects in both static word embeddings (Caliskan et al., 2017; Bolukbasi et al., 2016) and contextualized word representations (Zhao et al., 2019; Basta et al., 2019; Tan and Celis, 2019).”