AI Quality

Ensuring the quality of artificial intelligence (AI) systems is a critical research area focusing on developing methods to evaluate and improve AI performance, reliability, and robustness. Current efforts concentrate on developing novel metrics and frameworks for assessing data quality, detecting out-of-distribution data, and evaluating the stability and performance of AI models, including large language models and deep learning architectures. These advancements are crucial for building trustworthy AI systems across various applications, from industrial processes to medical image analysis, ultimately improving the safety and efficacy of AI-driven technologies.