Bias Evaluation Across Domain

Bias evaluation across domains in artificial intelligence focuses on identifying and mitigating biases present in large language models and other AI systems, stemming from their training data and inherent algorithmic limitations. Current research emphasizes developing comprehensive benchmark datasets and evaluation frameworks that assess bias across diverse NLP tasks, including classification, generation, and information extraction, often employing convolutional neural networks and knowledge graph embeddings. This work is crucial for ensuring fairness and reliability in AI applications, impacting fields ranging from decision support systems to scientific data analysis by promoting the development of less biased and more robust AI models.