Description: The Quality Assurance Framework is a structured approach that seeks to ensure quality in artificial intelligence (AI) systems and processes. This framework focuses on establishing standards and procedures that ensure AI systems operate ethically, safely, and efficiently. Quality in AI refers not only to the accuracy of algorithms but also to transparency, fairness, and accountability in their functioning. An effective framework includes continuous evaluation of models, validation of data, and monitoring of outcomes, allowing for the identification and mitigation of biases or errors that may arise. Additionally, this approach promotes collaboration across different disciplines, integrating technical, ethical, and legal perspectives to address the complex challenges posed by AI today. In the context of technology and users’ data, the framework also extends to data protection and user privacy, ensuring that transactions are secure and personal information is safeguarded. In summary, the Quality Assurance Framework is essential for building trust in AI systems and technology platforms, ensuring they operate responsibly and effectively.
