Quality assurance in AI refers to the systematic process of evaluating and validating artificial intelligence systems to ensure they meet predefined standards of performance, accuracy, and reliability. This includes rigorous testing of algorithms, data quality assessment, and the evaluation of model outputs to identify biases, errors, and inconsistencies. Quality assurance is vital for ensuring that AI applications function correctly and ethically, particularly in sensitive areas such as healthcare, finance, and autonomous systems. By implementing robust quality assurance practices, organizations can build trust in AI technologies and mitigate risks associated with their deployment.