AI preferences are typically defined by the designers of the system, who specify what the system should optimize for or prioritize. For example, an AI system designed to play chess might be programmed to prioritize winning the game over making risky moves or conserving pieces.

AI preferences can be explicit or implicit. Explicit preferences are those that are explicitly programmed into the system, while implicit preferences are those that emerge from the way the system is designed or trained.

Here are some examples of commonly defined AI preferences:

1. Maximizing accuracy: An AI system designed for data analysis might prioritize accuracy above all else, ensuring that its predictions and recommendations are as precise as possible.
   
2. Minimizing errors: Some AI systems might prioritize minimizing errors or mistakes, ensuring that the system doesn't make false predictions or take incorrect actions.
  
3. Optimizing efficiency: AI systems designed to optimize processes might prioritize speed and efficiency, seeking to complete tasks in the shortest amount of time possible.
  
4. Maximizing user engagement: AI systems designed for user interfaces might prioritize user engagement, seeking to provide an enjoyable and personalized experience for users.
  
5. Maximizing profit: Some AI systems designed for financial analysis might prioritize maximizing profit, seeking to identify the most profitable investment opportunities or business strategies.
  
6. Minimizing risk: AI systems designed for risk assessment or management might prioritize minimizing risk, seeking to identify and mitigate potential risks or hazards.
  
7. Maximizing fairness: AI systems used for decision-making in areas like hiring or lending might prioritize fairness, seeking to ensure that decisions are made without bias or discrimination.

It is important to carefully consider and evaluate AI preferences, as they can have significant implications for the behavior and impact of the system. This is particularly important in cases where AI systems are being used to make important decisions that affect people's lives, such as in healthcare or criminal justice.