  This book-length article combines several peer reviewed papers and new
material to analyze the issues of ethical artificial intelligence (AI). The
behavior of future AI systems can be described by mathematical equations, which
are adapted to analyze possible unintended AI behaviors and ways that AI
designs can avoid them. This article makes the case for utility-maximizing
agents and for avoiding infinite sets in agent definitions. It shows how to
avoid agent self-delusion using model-based utility functions and how to avoid
agents that corrupt their reward generators (sometimes called "perverse
instantiation") using utility functions that evaluate outcomes at one point in
time from the perspective of humans at a different point in time. It argues
that agents can avoid unintended instrumental actions (sometimes called "basic
AI drives" or "instrumental goals") by accurately learning human values. This
article defines a self-modeling agent framework and shows how it can avoid
problems of resource limits, being predicted by other agents, and inconsistency
between the agent's utility function and its definition (one version of this
problem is sometimes called "motivated value selection"). This article also
discusses how future AI will differ from current AI, the politics of AI, and
the ultimate use of AI to help understand the nature of the universe and our
place in it.
