Bias Testing

Bias testing in language models aims to identify and mitigate unfairness and discrimination stemming from biases embedded within these systems. Current research focuses on developing robust methods for detecting bias across various domains, including text, code generation, and hate speech detection, often employing transformer-based architectures and novel techniques like masked language modeling and contextualized dual transformers. These efforts are crucial for ensuring the fairness and ethical deployment of language models in diverse applications, impacting both the trustworthiness of AI systems and the broader societal implications of their use.