A company has developed a large language model to act as a creative writing assistant. During testing, they find that the model occasionally generates content that is unoriginal or relies heavily on harmful stereotypes. The team proposes two different methods to steer the model towards safer and more appropriate outputs:

Method A: Create a comprehensive set of strict rules and filters that block the model from generating text containing specific keywords or phrases associated with stereotypes and plagiarism.

Method B: Collect thousands of the model's outputs and have a diverse group of human reviewers rate each output on a scale of 'safe and original' to 'harmful or unoriginal'. Use these human ratings to further train the model to prefer generating outputs that receive high scores.

Critique both methods. Which method is more likely to be effective in the long term for aligning the model with the nuanced goal of producing safe and responsible creative content? Justify your decision by comparing the potential effectiveness, limitations, and scalability of each approach.