Ensuring the quality of AI systems presents several complex challenges. One of the primary issues is the quality and quantity of data used for training and validation. AI models rely on vast datasets, and if this data is incomplete, biased, or outdated, it can significantly impair the model's performance.
Additionally, many AI systems, particularly those based on deep learning, operate as black boxes, making their decision-making processes difficult to interpret. This lack of transparency can hinder trust and accountability. Addressing these challenges requires robust data management practices, improved model interpretability, rigorous testing for bias, and continuous performance monitoring.