Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications

• URL: http://arxiv.org/abs/2307.09162v3
• Date: Thu, 31 Aug 2023 20:02:47 GMT
• Title: Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications
• Authors: Vishesh Thakur
• Abstract summary: The study examines existing research on gender bias in AI language models and identifies gaps in the current knowledge. The findings shed light on gendered word associations, language usage, and biased narratives present in the outputs of Large Language Models. The paper presents strategies for reducing gender bias in LLMs, including algorithmic approaches and data augmentation techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Gender bias in artificial intelligence (AI) and natural language processing has garnered significant attention due to its potential impact on societal perceptions and biases. This research paper aims to analyze gender bias in Large Language Models (LLMs) with a focus on multiple comparisons between GPT-2 and GPT-3.5, some prominent language models, to better understand its implications. Through a comprehensive literature review, the study examines existing research on gender bias in AI language models and identifies gaps in the current knowledge. The methodology involves collecting and preprocessing data from GPT-2 and GPT-3.5, and employing in-depth quantitative analysis techniques to evaluate gender bias in the generated text. The findings shed light on gendered word associations, language usage, and biased narratives present in the outputs of these Large Language Models. The discussion explores the ethical implications of gender bias and its potential consequences on social perceptions and marginalized communities. Additionally, the paper presents strategies for reducing gender bias in LLMs, including algorithmic approaches and data augmentation techniques. The research highlights the importance of interdisciplinary collaborations and the role of sociological studies in mitigating gender bias in AI models. By addressing these issues, we can pave the way for more inclusive and unbiased AI systems that have a positive impact on society.

Related papers

• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models [58.130894823145205]
  We center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias. Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning. We conclude with recommendations tailored to DPO and broader alignment practices.
  arXiv  Detail & Related papers  (2024-11-06T06:50:50Z)
• 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)
• Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts [49.97673761305336]
  We evaluate three large language models (LLMs) for their alignment with human narrative styles and potential gender biases. Our findings indicate that, while these models generally produce text closely resembling human authored content, variations in stylistic features suggest significant gender biases.
  arXiv  Detail & Related papers  (2024-06-27T19:26:11Z)
• Leveraging Large Language Models to Measure Gender Bias in Gendered Languages [9.959039325564744]
  This paper introduces a novel methodology that leverages the contextual understanding capabilities of large language models (LLMs) to quantitatively analyze gender representation in Spanish corpora. We empirically validate our method on four widely-used benchmark datasets, uncovering significant gender disparities with a male-to-female ratio ranging from 4:01.
  arXiv  Detail & Related papers  (2024-06-19T16:30:58Z)
• Locating and Mitigating Gender Bias in Large Language Models [40.78150878350479]
  Large language models (LLM) are pre-trained on extensive corpora to learn facts and human cognition which contain human preferences. This process can inadvertently lead to these models acquiring biases and prevalent stereotypes in society. We propose the LSDM (Least Square Debias Method), a knowledge-editing based method for mitigating gender bias in occupational pronouns.
  arXiv  Detail & Related papers  (2024-03-21T13:57:43Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Gender Bias in Transformer Models: A comprehensive survey [1.1011268090482573]
  Gender bias in artificial intelligence (AI) has emerged as a pressing concern with profound implications for individuals' lives. This paper presents a comprehensive survey that explores gender bias in Transformer models from a linguistic perspective.
  arXiv  Detail & Related papers  (2023-06-18T11:40:47Z)
• 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)
• 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)
• Towards Understanding and Mitigating Social Biases in Language Models [107.82654101403264]
  Large-scale pretrained language models (LMs) can be potentially dangerous in manifesting undesirable representational biases. We propose steps towards mitigating social biases during text generation. Our empirical results and human evaluation demonstrate effectiveness in mitigating bias while retaining crucial contextual information.
  arXiv  Detail & Related papers  (2021-06-24T17:52:43Z)
• Mitigating Gender Bias in Machine Learning Data Sets [5.075506385456811]
  Gender bias has been identified in the context of employment advertising and recruitment tools. This paper proposes a framework for the identification of gender bias in training data for machine learning.
  arXiv  Detail & Related papers  (2020-05-14T12:06:02Z)

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.