To find common ground on using machine learning in game design: Understand different perspectives: Identify core concerns and encourage open communication. Focus on shared goals: Define the game's vision and potential ML benefits. Explore specific use cases: Start small with targeted ML implementations. Address concerns directly: Emphasize ML as a tool to enhance, not replace, creativity. Find a middle ground: Consider a hybrid approach and iterative implementation. Essentially, it's about open communication, focusing on shared goals, and approaching ML as a tool to enhance the design process.

Acknowledge that it is controversial. Regulatory risks, ownership of IP, murky provenance of source training material, the threat to careers are all real issues. Define the ethical standards for your studio and what is acceptable before proposing any particular solution - when is it okay to use generative or other tools; for scratch only, for production, never? Listen. Don't allow one loud group to downplay the concerns of another but seek common ground as a studio - you are making a game together that everyone needs to be proud of. Know what your audience thinks and will accept also - they are the final judge of the finished product. Identify key/limited areas to experiment with before over-committing to silver-bullet tools.

When team members clash over machine learning in game design, start by fostering open dialogue to understand each perspective. Encourage everyone to voice their concerns and insights without judgment. Highlight the potential benefits of machine learning, such as enhanced player experiences and streamlined processes, but also acknowledge the challenges and uncertainties it brings. Facilitate collaboration through workshops or training sessions to boost understanding and reduce misconceptions about machine learning. Emphasize teamwork and the importance of diverse skills and perspectives in achieving innovative solutions. Regularly review and adjust strategies based on feedback and results.