Employing AI to Better Understand Our Morals

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.

Artificial virtuous agents in a multi-agent tragedy of the commons

“…In turn, this would not only propel human-inspired architectures of artificial moral cognition (Cervantes et al 2016), but also illuminate features of human morality that depend on more general and highly distributed cognitive capacities (FeldmanHall and Mobbs 2015). To that end, regardless of whether sophisticated artificial morality will ever become reality, we might get new insights about the nature of human morality by approaching ethics from the computational perspective (Pereira et al 2021).…”

Emergent behaviours in multi-agent systems with Evolutionary Game Theory

“…Since its inception, EGT has become a powerful mathematical framework for the modelling and analysis of complex, dynamical MAS [5][6][7], in biological, social contexts as well as computerised systems [8][9][10][11][12]. It has been used widely and successfully to study numerous important and challenging questions faced by many disciplines and societies, such as: what are the mechanisms underlying the evolution of cooperative behaviour at various levels of organisation (from genes to human society) [12,13]?…”

Understanding Emergent Behaviours in Multi-Agent Systems with Evolutionary Game Theory

“…Since its inception, EGT has become a powerful mathematical framework for the modelling and analysis of complex, dynamical MAS (Han et al, 2017, Hofbauer and Sigmund, 1998, Paiva et al, 2018, in biological, social contexts as well as computerised systems (Han, 2013, Perc et al, 2017, Pereira et al, 2021, Tuyls and Parsons, 2007. It has been used widely and successfully to study numerous important and challenging questions faced by many disciplines and societies, such as: what are the mechanisms underlying the evolution of cooperative behaviour at various levels of organisation (from genes to human society) (Nowak, 2006b, Perc et al, 2017?…”