Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology

• URL: http://arxiv.org/abs/2405.06346v1
• Date: Fri, 10 May 2024 09:26:12 GMT
• Title: Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology
• Authors: Rishav Hada, Safiya Husain, Varun Gumma, Harshita Diddee, Aditya Yadavalli, Agrima Seth, Nidhi Kulkarni, Ujwal Gadiraju, Aditya Vashistha, Vivek Seshadri, Kalika Bali,
• Abstract summary: Existing research in measuring and mitigating gender bias predominantly centers on English. This paper presents the first comprehensive study delving into the nuanced landscape of gender bias in Hindi.
• Score: 22.458957168929487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing research in measuring and mitigating gender bias predominantly centers on English, overlooking the intricate challenges posed by non-English languages and the Global South. This paper presents the first comprehensive study delving into the nuanced landscape of gender bias in Hindi, the third most spoken language globally. Our study employs diverse mining techniques, computational models, field studies and sheds light on the limitations of current methodologies. Given the challenges faced with mining gender biased statements in Hindi using existing methods, we conducted field studies to bootstrap the collection of such sentences. Through field studies involving rural and low-income community women, we uncover diverse perceptions of gender bias, underscoring the necessity for context-specific approaches. This paper advocates for a community-centric research design, amplifying voices often marginalized in previous studies. Our findings not only contribute to the understanding of gender bias in Hindi but also establish a foundation for further exploration of Indic languages. By exploring the intricacies of this understudied context, we call for thoughtful engagement with gender bias, promoting inclusivity and equity in linguistic and cultural contexts beyond the Global North.

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)
• A Multilingual Perspective on Probing Gender Bias [0.0]
  Gender bias is a form of systematic negative treatment that targets individuals based on their gender. This thesis investigates the nuances of how gender bias is expressed through language and within language technologies.
  arXiv  Detail & Related papers  (2024-03-15T21:35:21Z)
• On Evaluating and Mitigating Gender Biases in Multilingual Settings [5.248564173595024]
  We investigate some of the challenges with evaluating and mitigating biases in multilingual settings. We first create a benchmark for evaluating gender biases in pre-trained masked language models. We extend various debiasing methods to work beyond English and evaluate their effectiveness for SOTA massively multilingual models.
  arXiv  Detail & Related papers  (2023-07-04T06:23:04Z)
• "I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation [69.25368160338043]
  Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation. We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
  arXiv  Detail & Related papers  (2023-05-17T04:21:45Z)
• Efficient Gender Debiasing of Pre-trained Indic Language Models [0.0]
  The gender bias present in the data on which language models are pre-trained gets reflected in the systems that use these models. In our paper, we measure gender bias associated with occupations in Hindi language models. Our results reflect that the bias is reduced post-introduction of our proposed mitigation techniques.
  arXiv  Detail & Related papers  (2022-09-08T09:15:58Z)
• 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)
• Mitigating Gender Stereotypes in Hindi and Marathi [1.2891210250935146]
  This paper evaluates the gender stereotypes in Hindi and Marathi languages. We create a dataset of neutral and gendered occupation words, emotion words and measure bias with the help of Embedding Coherence Test (ECT) and Relative Norm Distance (RND) Experiments show that our proposed debiasing techniques reduce gender bias in these languages.
  arXiv  Detail & Related papers  (2022-05-12T06:46:53Z)
• A Survey on Gender Bias in Natural Language Processing [22.91475787277623]
  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.
  arXiv  Detail & Related papers  (2021-12-28T14:54:18Z)
• Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models [104.41668491794974]
  We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians.
  arXiv  Detail & Related papers  (2021-04-15T15:03:26Z)
• They, Them, Theirs: Rewriting with Gender-Neutral English [56.14842450974887]
  We perform a case study on the singular they, a common way to promote gender inclusion in English. We show how a model can be trained to produce gender-neutral English with 1% word error rate with no human-labeled data.
  arXiv  Detail & Related papers  (2021-02-12T21:47:48Z)
• 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.