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Fairness in game playing AI: six key dimensions

1. Perceptual fairness
   : Do both competitors perceive the game environment in the same way? This refers to the information they receive about the game (the same input space).
2. Motoric fairness
   : Do both competitors have the same capabilities to take actions within the game (the same output space)? This includes limitations or advantages in movement, available options, or control schemes.
3. Historic fairness
   : Do both AI system have the same amount of time and data for training? This ensures a level playing field by avoiding an advantage for systems with more extensive training data.
4. Knowledge fairness
   : Do both competitors have access to the same in-game knowledge? This refers to understanding the game's rules, objectives, and potentially strategies if applicable.
5. Computational fairness
   : Do both AI systems have the same processing power for decision-making? This ensures neither system has a significant advantage in terms of computational speed or resources.
6. Common-sense fairness
   : Do both AI have access to the same background knowledge beyond the specific game? This includes common-sense reasoning that could influence gameplay decisions.

Isaac Asimov's three laws of robotics:

1. The First Law
   : A robot may not injure a human being or, through inaction, allow a human being to come to harm.
   → This law prioritises human safety above all else.
2. The Second Law
   : A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
   → Robots are programmed to follow human instructions, but not at the expense of harming humans.
3. The Third Law
   : A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
   → Robots are given a basic instinct for self-preservation, but overridden by the higher priorities of protecting humans and following orders.