Bias and Fairness in Natural Language Processing

By Kai-Wei Chang, Vinodkumar Prabhakaran, Vicente Ordonez. 2019

72 citations recorded by the source corpus.

Subfields: Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation (2021, background): “Much recent work focuses on exposing and quantifying NLP model biases that reflect known harmful aspects of human culture, negative stereotyping, and inadvertent group segregation [1, 8, 23].”
Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “…bias in NLP systems (Sun et al., 2019), uncovering many gender disparities and harmful biases in algorithms and text (Cao and Daum´e III 2020; Chang et al. 2019; Chang and McKeown 2019; Costa-juss ` a 2019; Du et al. 2019; Emami et al. 2019; Garimella et al. 2019; Gaut et al. 2020; Habash et…”
Multi-Dimensional Gender Bias Classification (2020, background): “Gender affects myriad aspects of NLP, including corpora, tasks, algorithms, and systems (Chang et al., 2019; Costa-jussà, 2019; Sun et al., 2019).”
Mind the Gap: Assessing Temporal Generalization in Neural Language Models (2021, background): “On the other hand, we should also be careful not to let the model update its knowledge with material that can add or amplify to the bias and prejudice of the model (Chang et al., 2019; Bender et al., 2021).”
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models (2023, unknown): “Besides, Dziri et al. (2022) argue that hallucination is also present in human-generated corpora (can be reflected as out-dated (Liska et al., 2022; Luu et al., 2022), biased (Chang et al., 2019; Garrido-Muñoz et al., 2021), or fabricated (Penedo et al., 2023) expression).”
Gender Bias in Machine Translation (2021, background): “We thus highlight that engineering interventions alone are not a panacea (Chang, 2019) and should be integrated with long-term multidisciplinary commitment and practices (D’Ignazio and Klein, 2020; Gebru, 2020) necessary to address bias in our community, hence in its artifacts, too.”
Perturbation Augmentation for Fairer NLP (2022, background): “, 2020), often replicating or amplifying harmful statistical associations in their training data (Caliskan et al., 2017; Chang et al., 2019).”
Biases in Large Language Models: Origins, Inventory, and Discussion (2023, background): “While there is ample coverage of bias in NLP as a general issue [20, 30, 64, 71], in this paper, we focus particularly on the following:”