''Fifty Shades of Bias'': Normative Ratings of Gender Bias in GPT Generated English Text

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   Résumé scientifique

   Concerns about bias in large language models (LLMs) often focus on technical metrics, yet limited research examines how marginalized communities perceive and interpret model behavior. Through 25 interviews with participants across gender identities (non-binary, men, and women), we investigate how ChatGPT responds to gendered versus neutral prompts and how users evaluate bias in these responses. Our findings reveal striking differences: non-binary participants identified condescension, stereotyping, and identity erasure in outputs that cisgender participants often found acceptable, demonstrating that bias perception is not universal, but rather shaped by lived experience and social positionin…

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   Generative artificial intelligence (AI), especially large language models (LLMs), is increasingly deployed in domains such as recruitment, content creation, and education. While these systems accelerate productivity, they also risk reproducing and amplifying societal biases (Ahuchogu et al., 2025). This project addresses the urgent challenge of identifying, quantifying, and mitigating gender bias in text-generative AI outputs, with a focus on job narratives. Building on my independent study of 11,000+ AI-generated job narratives, which we generated using Gemini AI, we introduce a bias quantification framework using mean bias, mean absolute bias, sentiment skew (via TextBlob), and distributio…

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   Large Language Models (LLMs) such as OpenAI's GPT -4 have demonstrated remarkable capabilities in generating human-like text across diverse contexts. However, their widespread adoption has raised critical concerns around inherent biases present in the generated content, especially those related to gender, race, and profession. This paper investigates the presence and nature of bias in responses generated by GPT-4 through controlled prompt testing. We introduce a prompt pair generation methodology that varies only on identity-specific variables and assess the outputs of the model on semantic similarity, polarity of sentiment, and embedding-level discrepancy measures. We carry out experiments …

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   Résumé scientifique

   The widespread application of Large Language Models (LLMs) involves ethical risks for users and societies. A prominent ethical risk of LLMs is the generation of unfair language output that reinforces or exacerbates harm for members of disadvantaged social groups through gender biases (Weidinger et al., 2022; Bender et al., 2021; Kotek et al., 2023). Hence, the evaluation of the fairness of LLM outputs with respect to such biases is a topic of rising interest. To advance research in this field, promote discourse on suitable normative bases and evaluation methodologies, and enhance the reproducibility of related studies, we propose a novel approach to database construction. This approach enabl…

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   Résumé scientifique

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   Résumé scientifique

   Language has a profound impact on our thoughts, perceptions, and conceptions of gender roles. Gender-inclusive language is, therefore, a key tool to promote social inclusion and contribute to achieving gender equality. Consequently, detecting and mitigating gender bias in texts is instrumental in halting its propagation and societal implications. However, there is a lack of gender bias datasets and lexicons for automating the detection of gender bias using supervised and unsupervised machine learning (ML) and natural language processing (NLP) techniques. Therefore, the main contribution of this work is to publicly provide labeled datasets and exhaustive lexicons by collecting, annotating, an…

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   Recent works have found evidence of gender bias in models of machine translation and coreference resolution using mostly synthetic diagnostic datasets. While these quantify bias in a controlled experiment, they often do so on a small scale and consist mostly of artificial, out-of-distribution sentences. In this work, we find grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments (e.g., female nurses versus male dancers) in corpora from three domains, resulting in a first large-scale gender bias dataset of 108K diverse real-world English sentences. We manually verify the quality of our corpus and use it to evaluate gender bias in various coreference resolu…

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   Résumé scientifique

   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)…

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