Abstract
We present a summary of research that we have conducted employing AI to better understand human morality. This summary adumbrates theoretical fundamentals and considers how to regulate development of powerful new AI technologies. The latter research aim is benevolent AI, with fair distribution of benefits associated with the development of these and related technologies, avoiding disparities of power and wealth due to unregulated competition. Our approach avoids statistical models employed in other approaches to solve moral dilemmas, because these are “blind” to natural constraints on moral agents, and risk perpetuating mistakes. Instead, our approach employs, for instance, psychologically realistic counterfactual reasoning in group dynamics. The present paper reviews studies involving factors fundamental to human moral motivation, including egoism vs. altruism, commitment vs. defaulting, guilt vs. non-guilt, apology plus forgiveness, counterfactual collaboration, among other factors fundamental in the motivation of moral action. These being basic elements in most moral systems, our studies deliver generalizable conclusions that inform efforts to achieve greater sustainability and global benefit, regardless of cultural specificities in constituents.
Highlights
With Evolutionary Game Theory (EGT), our models have demonstrated that different cognitive capacities, including the ability to recognize intentions, to form and to dissolve commitments, to exact revenge, to issue apologies, to forgive, to express guilt, and to act through counterfactual reasoning, whether by themselves or in combination, reinforce the stable group dynamics that depend on achieving cooperation in populations of constituents with diverse strategies, with otherwise potentially exclusive interests
Intention-recognizing strategies result in significantly increased overall cooperation, even with extra cognitive costs associated with detecting a more or less hidden or an explicit intention. By focusing on these elementary though fundamental forms of cognition, plus social learning ability, the EGT approach affords a clearer view about the complex dynamics between the individual and the social collective, than what can be gained by approaches neglecting these fundamental cognitive abilities, and/or that proceed unaided by contemporary computational tools
This work justifies the fundamental status afforded moral capacities in theories of social agency, demonstrating that these capacities are necessary in order to identify false commitments and free-riders, to recognize others’ intentions, to assume and detect guilt, or to creatively compose socially optimal coordination strategies by oneself, through self-reflection, by means of counterfactual thinking
Summary
Methods from statistical physics find applications in studies of human cognition and behaviour at different levels of organizations, from individuals to large groups, e.g., [1,2]. The most promising models should be those respecting the influences of individual moral self-development and represent the dynamics through which individual innovations inform group-level stability by sustainable social strategies With such dynamics in mind, we can begin to formulate an explicit conception of moral autonomy as a capacity made possible by the employment of counterfactual reasoning (knowing what I know today, what would I have done differently?), when exercised in a social-historical learning context over evolutionary time. With computational models built on EGT principles, we observe the combinatorial evolution of individual strategies and their mutations under various conditions They include differences in co-player partners, resulting in diverse offspring frequencies, given the rules of distinct culturally specific social games, or changes in natural contexts and constraints, for example. Either can confess to the crime or else say nothing (cf. the payoff Table 1 below)
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