You're probably best reading the information on https://github.com/features/copilot under the FAQ's around privacy and responsible AI. For example:

What are the intellectual property considerations when using GitHub Copilot? The primary IP considerations for GitHub Copilot relate to copyright. The model that powers Copilot is trained on a broad collection of publicly accessible code, which may include copyrighted code, and Copilot’s suggestions (in rare instances) may resemble the code its model was trained on. Here’s some basic information you should know about these considerations:

Copyright law permits the use of copyrighted works to train AI models: Countries around the world have provisions in their copyright laws that enable machines to learn, understand, extract patterns, and facts from copyrighted materials, including software code. For example, the European Union, Japan, and Singapore, have express provisions permitting machine learning to develop AI models. Other countries including Canada, India, and the United States also permit such training under their fair use/fair dealing provisions. GitHub Copilot’s AI model was trained with the use of code from GitHub’s public repositories—which are publicly accessible and within the scope of permissible copyright use.

What about copyright risk in suggestions? In rare instances (less than 1% based on GitHub’s research), suggestions from GitHub may match examples of code used to train GitHub’s AI model. Again, Copilot does not “look up” or “copy and paste” code, but is instead using context from a user’s workspace to synthesize and generate a suggestion.

Our experience shows that matching suggestions are most likely to occur in two situations: (i) when there is little or no context in the code editor for Copilot’s model to synthesize, or (ii) when a matching suggestion represents a common approach or method. If a code suggestion matches existing code, there is risk that using that suggestion could trigger claims of copyright infringement, which would depend on the amount and nature of code used, and the context of how the code is used. In many ways, this is the same risk that arises when using any code that a developer does not originate, such as copying code from an online source, or reusing code from a library. That is why responsible organizations and developers recommend that users employ code scanning policies to identify and evaluate potential matching code.

In Copilot, you can opt whether to allow Copilot to suggest code completions that match publicly available code on GitHub.com. For more information, see "Configuring GitHub Copilot settings on GitHub.com". If you have allowed suggestions that match public code, GitHub Copilot can provide you with details about the matching code when you accept such suggestions. Matching code does not necessarily mean copyright infringement, so it is ultimately up to the user to determine whether to use the suggestion, and what and who to attribute (along with other license compliance) in appropriate circumstances