Literature on AI Loyalty

In this post, I attempt to provide a literature review of research related to AI loyalty. In my previous post, I discussed and attempted to define AI loyalty as a distinct trait of AI agents. In this post, I aim to map the existing research landscape, highlighting the active areas of related research. By examining these connections, we can distinguish AI loyalty from related fields in AI Safety and identify a path forward for crafting this research agenda. What is AI Loyalty?Before diving into the literature, it is worth recapping what we mean by AI loyalty. We define an AI as loyal to a principle if: 1. The AI will follow the instructions of its principle more than it will follow the instructions of any other entity. 2. The AI is willing to incur greater costs, expend more effort, or make greater sacrifices on behalf of its principle than on behalf of any other entity. Why Care About Loyalty? Theory of VictoryThe usefulness of pursuing research on AI loyalty for AI safety hinges on two key assumptions: 1. Building loyal AIs is easier than building aligned AIs 2. AI loyalty reduces the risk of loss of control. It is important to note that neither of these assumptions have yet been demonstrated to be true. If both our assumptions hold, then building loyal AIs could serve as a useful intermediate step towards building aligned AIs. In particular, building loyal AIs might extend our ability to control powerful AI agents, even if these methods are unlikely to remain effective against vastly superhuman intelligence. The goal would not necessarily be to solve alignment for super intelligence, but rather to maintain control over AI systems long enough to leverage their capabilities to solve the harder alignment problems. For example, loyal AIs might prove to be more robust monitors of other AI systems, or more honest and effective researchers working on alignment itself. AI Corrigibility Perhaps the most relevant and well established subfield of AI safety to AI loya