Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach
This work addresses bias in AI for sensitive applications like content moderation, though it is incremental as it builds on existing multi-task learning methods.
The paper tackled bias and stereotype detection in language models by jointly learning these tasks, finding that this approach significantly improves bias detection compared to separate training, with competitive results from decoder-only models using QLoRA.
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.