Conceptor Debiasing of Word Representations Evaluated on WEAT

By S. Karve, Lyle Ungar, João Sedoc. 2019

35 citations recorded by the source corpus.

Bias in word representations, such as Word2Vec, has been widely reported and investigated, and efforts made to debias them. We apply the debiasing conceptor for post-processing both traditional and contextualized word embeddings. Our method can simultaneously remove racial and gender biases from word representations. Unlike standard debiasing methods, the debiasing conceptor can utilize heterogeneous lists of biased words without loss in performance. Finally, our empirical experiments show that the debiasing conceptor diminishes racial and gender bias of word representations as measured using the Word Embedding Association Test (WEAT) of Caliskan et al. (2017).

Subfields: Artificial Intelligence, Representation & Self-Supervised Learning

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “…et al. (2019); Garg et al. (2019); Huang et al. (2019); James-Sorenson and AlvarezMelis (2019); Kaneko and Bollegala (2019); Liu et al. (2019); Karve et al. (2019); Nozza et al. (2019); Prabhakaran et al. (2019); Romanov et al. (2019); Sap et al. (2019); Sedoc and Ungar (2019); Stanovsky et…”
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021, background): “…2019; Ethayarajh et al., 2019), more comprehensive evaluation frameworks (Lauscher et al., 2020a), new debiasing approaches (Dev and Phillips, 2019; Karve et al., 2019) and task-speciﬁc bias measures and resources for tasks like coreference resolution (Zhao et al., 2018), machine translation…”
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation (2020, methodology): “CPT-Debias (Karve et al., 2019) introduces a debiasing mechanism by utilizing the conceptor matrix.”
Theories of “Gender” in NLP Bias Research (2022, background): “[75] Saket Karve, Lyle Ungar, and João Sedoc.”
Joint Multiclass Debiasing of Word Embeddings (2020, methodology): “ ranked lists of word pairs in terms of their vector similarity to human judgement via Spearmans Rank correlation coecient [13], by using the collection taken from the Conceptor Debiasing evaluation [7], i.e., RG65, WS, RW, MEN, MTurk, SimLex, SimVerb. In addition, we also utilize Mikolov Analogy Te”