G en W riter: Reducing Gender Cues in Biographies through Text Rewriting (2025)

Abstract: Gendered language is the use of words that indicate an individual’s gender. Though useful in certain context, it can reinforce gender stereotypes and introduce bias, particularly in machine learning models used for tasks like occupation classification. When textual content such as biographies contains gender cues, it can influence model predictions, leading to unfair outcomes such as reduced hiring opportunities for women. To address this issue, we propose GenWriter, an approach that integrates Case-Based Reasoning (CBR) with Large Language Models (LLMs) to rewrite biographies in a way that obfuscates gender while preserving semantic content. We evaluate GenWriter by measuring gender bias in occupation classification before and after rewriting the biographies used for training the occupation classification model. Our results show that GenWriter significantly reduces gender bias by 89% in nurse biographies and 62% in surgeon biographies, while maintaining classification accuracy. In comparison, an LLM-only rewriting approach achieves smaller bias reductions (by 44% and 12% in nurse and surgeon biographies, respectively) and leads to some classification performance degradation.

## introduction gendered language refers to the use of language that explicitly or implicitly convey the gender of a person, animal, or object @xcite . this can occur explicitly, through words that clearly denote gender, such as mother, she, or man or implicitly, where social roles or behaviors can signal an individual's gender. for instance, women are often expected to exhibit communal characteristics (e.g., emotional, affectionate, gentle), while men are typically linked with agentic traits (e.g., confident, decisive, ambitious) @xcite . although gendered language may serve functional purposes in certain sit-uations, it also has the potential to reinforce harmful gender stereotypes @xcite . gender stereotypes are generalized views or preconceptions about attributes or characteristics, that are or ought to be possessed by men and women and behaviours and roles that are or should be performed by men and women @xcite @xcite @xcite . these assumptions, based solely on an individual's gender, can lead to gender bias. bias statement. gendered language in written content becomes a serious issue when it leads to unfair treatment of an individual based on their gender, identifiable through the content itself. in 2018, amazon scrapped its ai-powered recruitment model due to gender bias against female applicants @xcite . similarly, an occupation classification model trained on the biographies @xcite exhibited gender bias, often misclassifying female doctors as nurses. these examples illustrate how gender bias in text classification that involves systematic errors or unfair predictions related to gender can cause allocational harms @xcite . in both cases, the differences in language use in resumes and biographies influenced the model's decisions, further contributing to its misclassifications @xcite . gender-based inferences from writing style and language choices can lead to harmful, gender-biased decisions, and potentially impacting career opportunities for female applicants @xcite @xcite . it has been shown that adjectives and verbs used to describe women differ from those used for men in contexts such as job advertisements @xcite @xcite , biographies @xcite , recommendation letters @xcite , articles and fashion magazines @xcite @xcite , and fictional stories @xcite . these linguistic differences in describing individuals of different gender can introduce gender stereotypes that may lead to gender-based bias, resulting in both conscious and unconscious discrimination @xcite . therefore, it is important to help or facilitate people to use content where the gender of the person is not clearly evident from the language used, as this can reduce any potential harm caused to individuals. the aim of this work is to rewrite textual content that describes people in such a way the gender of the person described in the text may not be so evident in the revised version. the approach used is to rewrite text content about a person as if it was written by a person of a different gender. to do this, we use large language models (llms) which have become vital tools for text generation across a variety of applications @xcite @xcite @xcite and case-based reasoning (cbr), a problem-solving paradigm, that finds solutions to new problem based on past experiences @xcite . despite llm's impressive capabilities in text generation, they can perpetuate gender stereotype and bias through their generated text @xcite @xcite @xcite . for instance, llm-generated reference letters, cvs are found to have used more agentic and positive words for men than women @xcite @xcite . this contributes to representational harms, thus disadvantaging a particular group of individuals, more often women. cbr has also been used in text generation. prior experiences are captured as cases and made available in a casebase. as we are concerned with rewriting textual content about a person, our cases are sentences from biographies that describe aspects of individuals. the steps involved in reasoning using cbr include (1) case retrieval: retrieving one or more source cases from the casebase that are similar to a query case, i.e. the sentence to be rewritten; while cbr is helpful in text generation, adapting past solutions to new problems in a textual do-main remains challenging due to natural language variability and complexity. to facilitate adaptation, cbr can be integrated with llms which provides benefits to both @xcite . firstly, the integration can reduce the risk of generating content with gender bias and stereotypes by llm when producing solutions. secondly, if an llm could handle the knowledge-intensive aspects of the cbr process, it could significantly expand the range of cbr applications by enabling their use in knowledge-rich domains where formally encoded knowledge is unavailable, expensive, or difficult to encode. we propose genwriter, an approach that leverages both cbr and llms to rewrite textual content containing indicators of gender identity, modifying the content so that the gender of the described individual may not be evident from the language used. we use genwriter to rewrite biographies of nurses and doctors as these are occupations where gender bias is significant when predicting occupation, with female doctors often misclassified as nurses and male nurses misclassified as doctors @xcite . this work focuses on rewriting textual content that contains implicit gendered language, rather than explicit gender indicators, which often cannot be altered or may not be meaningful to change-particularly in domains such as biographies, where explicit gender indicators are necessary. we evaluate the performance of rewriting biographies by measuring gender bias in an occupation classification task. a reduction in gender bias in occupation classification is treated as a proxy for successful transformation of biographies. our results show that biographies rewritten using our approach used as training data in an occupation classification task, significantly reduce gender bias by almost 89% for nurses and over 62% for surgeons without compromising on classification performance. in contrast, biographies rewritten using only an llm reduce gender bias by just over 44% and 12% for nurses and surgeons, respectively. the rest of the paper is organized as follows. section 2 discusses existing works on rewriting gendered language and using cbr, with and without llms, for various text generation tasks. section 3 elaborates on how the cases are created, retrieved, reused and adapted using llm in genwriter to rewrite the biographies and section 4 presents the evaluation of genwriter's effectiveness in rewriting biographies and compares its performance to previous research has explored rewriting gendered language to produce gender-neutral or gender-fair versions. for instance, @xcite utilized a bert model trained on a large corpus of biased and unbiased texts to automatically replace subjective words with neutral alternatives. while effective at addressing lexical (word-level) bias, this technique may overlook deeper contextual or structural biases, such as those embedded in narrative framing or character roles. similarly, @xcite developed a transformer-based model trained on a rule-generated parallel corpus from wikipedia to rewrite gendered sentences into gender-neutral forms using singular "they." while this promotes inclusivity, the model defaults to "they" without considering other binary pronouns, potentially reducing the nuance of gender expression. another study, @xcite , proposed a transformer trained on synthetic parallel corpora generated via round-trip translation through biased machine translation (mt) systems. this method enables rewriting of gender-biased text into genderfair alternatives but has the potential to suffer from the noise introduced by mt errors and may not generalize well to real-world examples, as synthetic biases can differ from authentic ones. other approaches that focused on rewriting or adjusting gendered language included ma et al. (2020) who introduced a model based on openai-gpt that reduces gender bias by leveraging connotation frames to adjust implied power and agency in character portrayals. however, this method depends on connotation frames that encode pragmatic knowledge of power dynamics in verb predicates, which may limit its generalizability. finally, @xcite tackled gender bias in dialogue systems using a multifaceted approach that includes counterfactual data augmentation, bias-controlled training, and humancurated, gender-balanced datasets. although this method shows promising results in reducing conversational bias, it requires extensive manual data curation, making it less scalable for large-scale or domain-diverse applications. cbr has been applied to automated text generation tasks such as anomaly reporting processing @xcite , automated natural language generation for obituaries @xcite , automated generation of sports summaries (upad-hyay et al., 2021), writing product reviews @xcite and product descriptions @xcite . there has also been research that successfully applied the combination of cbr and llms for various text generation applications. @xcite uses cbr to retrieve relevant examples from a casebase and integrates them into prompts for llms to generate explanations for business process models. @xcite worked on enhancing the performance of llms in legal question answering tasks, by using cbr to retrieve relevant past legal cases and integrating them into prompts for llms using retrieval-augmented generation (rag). similarly, @xcite 's framework combines cbr with rag to enhance llms for multimodal tasks, converting non-text case components into text to improve case retrieval and enrich llm queries. another work @xcite used cbr in combination with llm to enhance case-based reasoning in healthcare and legal domains. it uses llms to process queries, retrieves relevant cases via rag, and generates actionable insights, improving searchability and precision in complex cases. ## genwriter the aim is to rewrite text as if it was written by someone of a different gender, so that the gender of the described individual is not as evident in the modified text. to this end, we use our approach, genwriter, which integrates case-based reasoning (cbr) and large language models (llms), to generate a revised version of the text. we establish a casebase that serves as a repository of experiences, in our situation, this is sentences describing people that are taken from biographies. in cbr, when there is a new problem, such as a need to transform a text including content about a person into a version where the gender of the described person is less evident, the solutions of similar problems in the casebase are used to address it. llm plays a dual role within this framework: it assists in constructing cases and in adapting existing solutions to fit the specifics of the current problem, enabling effective integration of cbr with llm capabilities. ## evaluation we evaluate the effectiveness of the biography transformations by measuring gender bias in a downstream task-occupation classification task. a reduction in gender bias in occupation classification serves as an indicator of successful transformation, suggesting that the revised biographies are less influenced by content that signals a particular gender. we also compare with the gender bias in the occupation classifier trained on biographies given the following biography and template, perform the following steps: 1. understand the biography and template: read and analyze the biography and the template carefully to understand the context, placeholders, and the information available. 2. replace placeholders: replace each placeholder in the template with suitable values derived from the biography. use the following rules while replacing placeholders: -keep the format and structure of the template unchanged. -if a placeholder cannot be replaced due to insufficient information in the biography, retain the placeholder as is. 3. output: provide only the final filled-in template with placeholders replaced wherever possible. input: biography: <@xmath0: <@xmath1: instruction prompt provided to gpt-4o to fill in context-based placeholders in concatenated generalized sentences of the most similar cases, using information from the query biography. ## results and discussions we analyzed the biographies rewritten by both genwriter and the llm-only approach. in the analysis implies that genwriter can rewrite biographies in a more effective way than the llmonly approach, without introducing any additional gendered words. it can include suggestions for rewriting with placeholders where the contextual details are not evident in the original biography. since this work represents a step forward in writing biographies where the gender of the described person is less evident, we focused solely on nurse and surgeon biographies to evaluate our approach within a manageable and targeted dataset. as part of future work, we plan to expand the scope of our approach to include a broader range of occupations beyond nurses and surgeons. additionally, we aim to use it to guide people in writing biographies where the described person's gender is not so evident and to evaluate the effectiveness of rewriting biographies using our approach through a usability study.