Which type of AI emphasizes the need for ethical approaches and risk mitigation?

The emphasis on ethical approaches and risk mitigation in artificial intelligence is primarily associated with Responsible AI. This concept refers to the development and deployment of AI systems that are designed to be fair, accountable, transparent, and aligned with societal values. Responsible AI encompasses various practices, including ensuring that data is used ethically, avoiding biases in AI models, and considering the broader impact of AI applications on individuals and communities.

In contrast, the other choices pertain to specific methodologies or paradigms in AI. Supervised Learning focuses on using labeled datasets to train models, while Unsupervised Learning deals with unlabeled data to discover patterns. Reinforcement Learning is a technique where an agent learns to make decisions by receiving rewards or penalties based on its actions within an environment. These options do not inherently include considerations for ethics or risk mitigation, making Responsible AI the correct choice for the question.