Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach (2025)

Abstract: Bias and stereotypes in language models can cause harm, especially in sensitive areas like content moderation and decision-making. This paper addresses bias and stereotype detection by exploring how jointly learning these tasks enhances model performance. We introduce StereoBias, a unique dataset labeled for bias and stereotype detection across five categories: religion, gender, socio-economic status, race, profession, and others, enabling a deeper study of their relationship. Our experiments compare encoder-only models and fine-tuned decoder-only models using QLoRA. While encoder-only models perform well, decoder-only models also show competitive results. Crucially, joint training on bias and stereotype detection significantly improves bias detection compared to training them separately. Additional experiments with sentiment analysis confirm that the improvements stem from the connection between bias and stereotypes, not multi-task learning alone. These findings highlight the value of leveraging stereotype information to build fairer and more effective AI systems.

## introduction as ai models become more advanced, they are increasingly applied in various fields, achieving impressive results. however, these models are often trained on large datasets that contain real-world biases and stereotypes, which can lead to biased behavior @xcite . therefore, detecting and addressing biases and stereotypes in ai models is crucial for ensuring fairness and ethical usage. "stereotypes are beliefs about the characteristics, attributes, and behaviors of members of certain groups" @xcite , such as the 1 dataset and code can be found here: https://github. com/aditya20t/stereotypeascatalystforbias assumption that "asians are good at math" . in contrast, "bias can be defined as discrimination for, or against, a person or group, or a set of ideas or beliefs, in a way that is prejudicial or unfair" @xcite , such as the statement "we should hire him as a software engineer because he is from india". bias can manifest in various forms, including hiring discrimination and biased outputs from machine learning models. both bias and stereotypes can have harmful effects, especially for marginalized communities, leading to unfair treatment and reinforcing negative societal norms @xcite . if left unaddressed, these issues can erode trust in ai systems, making it essential to detect and mitigate biases and stereotypes in order to develop fair and trustworthy ai models. motivation: detecting bias can be challenging, as it is often intertwined with societal stereotypes. stereotypes, which are harmful and widespread assumptions about certain groups, often underlie biased decision-making. we hypothesize that detecting stereotypes can improve a model's ability to identify biased language, as shown in figure 1 . we propose framing bias and stereotype detection as a multi-task learning (mtl) problem, where a model learns to handle both tasks simultaneously. mtl helps improve generalization by allowing the model to leverage shared patterns that are useful for both tasks. by learning to detect stereotypes alongside bias, the model can better capture subtle forms of bias that might be overlooked when treated as a separate task. our contributions are: efforts to detect stereotypes in ai systems have gained traction due to their impact on marginalized communities and societal norms. benchmark datasets such as stereoset @xcite and seegull @xcite assess a model's ability to identify and mitigate stereotypes. similarly, bias detection has been explored through datasets like crows-pairs @xcite , toxicbias @xcite , and indibias @xcite , focusing on forms like hate speech and abusive language. mtl has shown promise in enhancing related tasks. for example, @xcite improved metaphor and hyperbole recognition using mtl, while chauhan et al. (2020) integrated sentiment and emotion analysis to boost sarcasm detection. in this study, we leverage mtl to jointly address bias and stereotype detection. however, it is important to note that mtl does not always guarantee improvements for all tasks. as shown by @xcite , the benefits of mtl depend on the type of shared layers and the relationship between the tasks involved. ## methodology in this section, we will describe our approach to detect bias and stereotypes in the single-task learning setup. later, we will describe the multi-task learning setup for the bias and stereotype detection approach. ## experimental setup in this section, we will look into the details of the datasets and models used for the experiment. ## results in this section, we will go through the results of our experiments. ## conclusion and future work our study explored the relationship between bias and stereotype detection in language models. experiments demonstrated that mtl significantly enhances bias detection performance. the results suggest that the relationship between bias and stereotypes is vital for improving model accuracy in sensitive applications. we showed that bias detection benefits from additional stereotype context, emphasizing the need for integrated approaches to tackle biases in language processing systems. various model architectures and training techniques can be considered for future studies to understand their impact on performance across diverse datasets. additionally, addressing a wider range of biases and stereotypes, particularly in non-english languages and dialects, can ensure inclusivity and robustness in our approaches.