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The purpose of this paper is to illustrate how game theoretic solution concepts inform what classes of problems will be amenable to artificial intelligence and machine learning (AI/ML), and how to evolve the interaction between human and artificial intelligence.

The approach addresses the development of operational gaming to support planning and decision making. It then provides a succinct summary of game theory for those designing and using games, with an emphasis on information conditions and solution concepts. It addresses how experimentation demonstrates where human decisions differ from game theoretic solution concepts and how games have been used to develop AI/ML. It concludes by suggesting what classes of problems will be amenable to AI/ML, and which will not. It goes on to propose a method for evolving human/artificial intelligence.

Game theoretic solution concepts inform classes of problems where AI/ML 'solutions' will be suspect. The complexity of the subject requires a campaign of learning.

Though games have been essential to the development of AI/ML, practitioners have yet to employ game theory to understand its limitations.

Bystander apathy has been a source of debate for decades. In the past half-century, psychologists developed theoretical frameworks to understand bystander activity, commonly referred to as bystander intervention models (BIMs). More recently, BIMs have been modified to facilitate initiatives to prevent various forms of online victimization. This chapter begins with a review of BIMs and recent applications of bystander intervention research to online environments. We also present several future directions for research along with applications for reducing technology-facilitated violence, including programming recommendations and theoretical development.