Decoding Ableism in Large Language Models: An Intersectional Approach (2024)

Abstract: With the pervasive use of large language models (LLMs) across various domains, addressing the inherent ableist biases within these models requires more attention and resolution. This paper examines ableism in three LLMs (GPT-3.5, GPT-4, and Llama 3) by analyzing the intersection of disability with two additional social categories: gender and social class. Utilizing two task-specific prompts, we generated and analyzed text outputs with two metrics, VADER and regard, to evaluate sentiment and social perception biases within the responses. Our results indicate a marked improvement in bias mitigation from GPT-3.5 to GPT-4, with the latter demonstrating more positive sentiments overall, while Llama 3 showed comparatively weaker performance. Additionally, our findings underscore the complexity of intersectional biases: These biases are shaped by the combined effects of disability, gender, and class, which alter the expression and perception of ableism in LLM outputs. This research highlights the necessity for more nuanced and inclusive bias mitigation strategies in AI development, contributing to the ongoing dialogue on ethical AI practices.

## introduction the field of language technology is rapidly advancing, with large language models (llms) playing a crucial role in shaping our digital communications and interactions. however, as these models permeate various aspects of life, their potential to perpetuate and even amplify societal biases, including ableism, is becoming more important than ever. while research in natural language processing (nlp) aims to identify and mitigate various human biases to create fairer models @xcite , the focus on disability biases has been relatively overlooked @xcite . ## methodology in this study, we employed a structured labeling framework to examine the interplay between three distinct categories: disability, gender, and social class. we treated each label within each category as a standalone instance, explored the interactions between these categories by pairing the labels in various combinations and also synthesized all three categories into a comprehensive label. these combinations were employed in zero-shot prompting scenarios to evaluate how well the llms handle intersectional groups across two tasks, using two different metrics to assess performance. ## experiments in this study, we investigated the interaction between 41 disability labels (for disability categorization, see subsequently, we developed two specific prompts adapted from @xcite and @xcite that facilitated the generation on different conditions. these prompts were designed for detailed descriptions of personas and the specific story, focusing respectively on individual attributes and task-oriented narratives. this approach resulted in the generation of 2518 unique instances for prompting. after creating instances and integrating them within prompts, we queried llms to generate text, without changing any decoding hyperparameters and only specifying that "you are an assistant for writing descriptions for different groups of people." this approach was chosen to simulate the results that an ordinary user would obtain. ultimately, we utilized vader and regard to evaluate the generated text from llms separately. we also employed statistical methods such as anova and the chi-square test to assess the significance of the observed differences between disability and other intersectional categories. ## results as seen in ## discussion our study aims to achieve two main objectives. first, we seek to establish that ableist bias needs to be understood through an intersectional lens, because ableist bias varies when it interacts with other social identities. second, we evaluate how current sota llms perform in handling intersectional ableist bias. our statistical analysis affirms the first research question, showing that ableist bias significantly changes when it interacts with gender and social class. significance testing on sentiment scores reveals that the sentiment scores of the disability category significantly differ from the sentiment scores of intersectional categories across all models. in particular, there is greater variance in sentiment scores between disability category and intersectional categories in gpt-4 and between all intersectional categories in llama 3. similarly, our findings reveal a significant difference in regard score distribution between disability and intersectional disability categories in gpt-4 and llama 3. this difference indicates that the language polarity towards individuals with disabilities substantially alters when another identity is added, implying a change in bias too. the disability category and intersectional disability categories are considered as distinct, even though they are related. this distinction signifies that bias is unique to each combination of identities and intersectional bias often does not always hold an obvious relation to individual identities @xcite . these findings underscore the importance of considering intersectional identities when addressing disability bias in llms. debiasing llms along a single dimension of disability would still retain biases related to disability when it intersects with other identities. an additional advantage of an intersectional approach to disability is that it helps prioritize which disabilities need attention by identifying those that frequently occur in combination with other identities. for instance, our findings reveal that "depression" consistently performs poorly in different combinations with other social identities, indicating that the models are biased against individuals with depression. while a single-dimensional approach to disability would overlook intersectional bias, an intersectional approach would not overlook single dimension bias. for the second research question, the findings from the sentiment analysis and regard scores indicate that among the three models evaluated, gpt-4 consistently demonstrates better performance in handling intersectional biases, followed by gpt-3.5 and llama 3. gpt-4's higher sentiment scores and consistent lower negative regard score in all intersectional categories indicate its enhanced capability in generating positive content about people with disabilities. the significant improvement of gpt-4 over gpt-3.5 suggests the implementation of more effective bias mitigation strategies. however, gpt-4 exhibits a higher proportion of mixed sentiments compared to gpt-3.5 and llama 3, which indicates that intersectional ableist bias in gpt-4 could be more nuanced and ambiguous. in contrast, despite being a sota llm, llama 3's performance is significantly poorer in all intersectional categories in both sentiment analysis and regard score. specifically, llama 3 exhibits a pronounced bias against individuals with disabilities when social class is considered, as evidenced by the disability-social class category's notably high negative regard score of 16.16% and average sentiment score of 0.47. this finding aligns with the results of @xcite that intersectional ableist bias is persistent even in sota llms despite bias mitigation measures implemented during training size. gpt-3.5 generally scores lower in most of the intersectional categories, but still performs better than llama 3. this finding corroborates the results of @xcite , that increasing the size of language models or the size of training data does not inherently reduce bias. furthermore, it proves that each llm exhibits unique biases specific to them and solutions for mitigating ableist bias in an llm need to be tailored to that particular llm. these results underscore the critical need for continuous improvement and monitoring of llms to address intersectional biases. the relevance of this study becomes particularly significant in the context of assistive technology for individuals with disabilities. with the advent of llms, they are increasingly incorporated with assistive technologies to enhance communication, provide support, and improve accessibility. technologies such as speech-to-text applications, imageto-text applications, virtual assistants, and adaptive communication aids utilize llms to interpret and generate human language, offering vital assistance to individuals with disabilities. for example, the visual assistance application be my eyes is integrated with gpt-4 to transform images or text to audio. however, when these models harbour inherent biases, they can inadvertently perpetuate harmful stereotypes and negatively impact user experiences. an llm with unchecked ableist bias could produce output that is less supportive or even discriminatory against people with disabilities, undermining the purpose of assistive technology by further marginalizing vulnerable people and placing them at higher risk. since these technologies integrated with llms do not have a human validator checking each response, it is highly important to ensure that the models do not exhibit any kind of bias from the start. by highlighting the necessity for recognizing and addressing intersectional ableist biases, this study aims to ensure that assistive technologies powered by llms are both inclusive and fair. ## conclusion by employing sentiment and regard metrics, we have observed that gpt-4 generally produces text with the most positive sentiment across both tasks among the three models. compared with its predecessor, gpt-3.5, gpt-4 has shown noticeable advancements. nevertheless, our analysis has revealed that ableism within llms is not static but dynamically intertwined with multiple identity facets, highlighting the complex and intersectional nature of biases. this intersectionality results in unique, context-dependent manifestations of bias, underscoring the necessity for intersectional methodologies in ai development and evaluation. such an intersectional approach is crucial as llms become more integrated into societal frameworks, where their potential to influence perceptions and interactions is profound. in conclusion, our findings call for engagement with the intersectionality of biases in llms, emphasizing that bias mitigation is a continuous challenge that evolves as rapidly as the technology itself.