Bias Measurement

Bias measurement in artificial intelligence focuses on quantifying and mitigating unfair biases present in models, particularly large language models (LLMs) and multimodal models like those generating images from text. Current research emphasizes developing robust and reliable bias metrics, often employing techniques like word embedding association tests and counterfactual analysis, while also exploring the influence of factors such as context length and template design on measurement accuracy. This work is crucial for ensuring fairness and trustworthiness in AI systems, impacting both the responsible development of AI technologies and the prevention of discriminatory outcomes in various applications.