a fail-safe, or a built-in virtue. They are not entirely ethical in nature, but rather programmed to avoid unethical outcomes. Explicit ethical agents: These are machines that are capable of processing scenarios and acting on ethical decisions. Machines which have algorithms to act ethically. Full ethical agents: These machines are similar to explicit ethical agents in being able to make ethical decisions. However, they also contain human metaphysical features. (i.e. have free will, consciousness and intentionality) (See artificial systems and moral responsibility.) Focuses of machine ethics AI control problem Some scholars, such as philosopher Nick Bostrom and AI researcher Stuart Russell, argue that if AI surpasses humanity in general intelligence and becomes "superintelligent", then this new superintelligence could become powerful and difficult to control: just as the fate of the mountain gorilla depends on human goodwill, so might the fate of humanity depend on the actions of a future machine superintelligence. In their respective books Superintelligence and Human Compatible, both scholars assert that while there is much uncertainty regarding the future of AI, the risk to humanity is great enough to merit significant action in the present. This presents the AI control problem: how to build an intelligent agent that will aid its creators, while avoiding inadvertently building a superintelligence that will harm its creators. The danger of not designing control right "the first time," is that a superintelligence may be able to seize power over its environment and prevent humans from shutting it down. Potential AI control strategies include "capability control" (limiting an AI's ability to influence the world) and "motivational control" (one way of building an AI whose goals are aligned with human or optimal values). There are a number of organizations researching the AI control problem, including the Future of Humanity Institute, the Machine Intelligence Research Institute, the Center for Human-Compatible Artificial Intelligence, and the Future of Life Institute. Algorithms and training AI paradigms have been debated over, especially in relation to their efficacy and bias. Nick Bostrom and Eliezer Yudkowsky have argued for decision trees (such as ID3) over neural networks and genetic algorithms on the grounds that decision trees obey modern social norms of transparency and predictability (e.g. stare decisis). In contrast, Chris Santos-Lang argued in favor of neural networks and genetic algorithms on the grounds that the norms of any age must be allowed to change and that natural failure to fully satisfy these