A Survey on Gender Bias in Natural Language Processing

• URL: http://arxiv.org/abs/2112.14168v1
• Date: Tue, 28 Dec 2021 14:54:18 GMT
• Title: A Survey on Gender Bias in Natural Language Processing
• Authors: Karolina Stanczak, Isabelle Augenstein
• Abstract summary: We present a survey of 304 papers on gender bias in natural language processing. We compare and contrast approaches to detecting and mitigating gender bias. We find that research on gender bias suffers from four core limitations.
• Score: 22.91475787277623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language can be used as a means of reproducing and enforcing harmful stereotypes and biases and has been analysed as such in numerous research. In this paper, we present a survey of 304 papers on gender bias in natural language processing. We analyse definitions of gender and its categories within social sciences and connect them to formal definitions of gender bias in NLP research. We survey lexica and datasets applied in research on gender bias and then compare and contrast approaches to detecting and mitigating gender bias. We find that research on gender bias suffers from four core limitations. 1) Most research treats gender as a binary variable neglecting its fluidity and continuity. 2) Most of the work has been conducted in monolingual setups for English or other high-resource languages. 3) Despite a myriad of papers on gender bias in NLP methods, we find that most of the newly developed algorithms do not test their models for bias and disregard possible ethical considerations of their work. 4) Finally, methodologies developed in this line of research are fundamentally flawed covering very limited definitions of gender bias and lacking evaluation baselines and pipelines. We suggest recommendations towards overcoming these limitations as a guide for future research.

Related papers

• Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
  Existing machine translation gender bias evaluations are primarily focused on male and female genders. This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words) We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
  arXiv  Detail & Related papers  (2024-07-23T08:13:51Z)
• Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation [47.770531682802314]
  Even simple prompts could cause T2I models to exhibit conspicuous social bias in generated images. We present the first extensive survey on bias in T2I generative models. We discuss how these works define, evaluate, and mitigate different aspects of bias.
  arXiv  Detail & Related papers  (2024-04-01T10:19:05Z)
• Gender Bias in Large Language Models across Multiple Languages [10.068466432117113]
  We examine gender bias in large language models (LLMs) generated for different languages. We use three measurements: 1) gender bias in selecting descriptive words given the gender-related context. 2) gender bias in selecting gender-related pronouns (she/he) given the descriptive words.
  arXiv  Detail & Related papers  (2024-03-01T04:47:16Z)
• Gender Inflected or Bias Inflicted: On Using Grammatical Gender Cues for Bias Evaluation in Machine Translation [0.0]
  We use Hindi as the source language and construct two sets of gender-specific sentences to evaluate different Hindi-English (HI-EN) NMT systems. Our work highlights the importance of considering the nature of language when designing such extrinsic bias evaluation datasets.
  arXiv  Detail & Related papers  (2023-11-07T07:09:59Z)
• Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation [64.79319733514266]
  Large Language Models (LLMs) can generate biased and toxic responses. We propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes.
  arXiv  Detail & Related papers  (2023-11-01T05:31:46Z)
• VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution [80.57383975987676]
  VisoGender is a novel dataset for benchmarking gender bias in vision-language models. We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas. We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
  arXiv  Detail & Related papers  (2023-06-21T17:59:51Z)
• Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation [64.65911758042914]
  We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
  arXiv  Detail & Related papers  (2022-05-19T20:05:02Z)
• Theories of "Gender" in NLP Bias Research [0.0]
  We survey nearly 200 articles concerning gender bias in NLP. We find that the majority of the articles do not make their theorization of gender explicit. Many conflate sex characteristics, social gender, and linguistic gender in ways that disregard the existence and experience of trans, nonbinary, and intersex people.
  arXiv  Detail & Related papers  (2022-05-05T09:20:53Z)
• Sexism in the Judiciary [0.0]
  We analyze 6.7 million case law documents to determine the presence of gender bias within our judicial system. We find that current bias detectino methods in NLP are insufficient to determine gender bias in our case law database.
  arXiv  Detail & Related papers  (2021-06-29T05:38:53Z)
• Evaluating Gender Bias in Natural Language Inference [5.034017602990175]
  We propose an evaluation methodology to measure gender bias in natural language understanding through inference. We use our challenge task to investigate state-of-the-art NLI models on the presence of gender stereotypes using occupations. Our findings suggest that three models trained on MNLI and SNLI datasets are significantly prone to gender-induced prediction errors.
  arXiv  Detail & Related papers  (2021-05-12T09:41:51Z)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.