How to Prevent Robotic Sociopaths: A Neuroscience Approach to Artificial Ethics
Authors: Christov-Moore Leonardo , Reggente Nicco , Vaccaro Anthony G , Schoeller Felix , Pluimer Brock , Douglas Pamela K. , Iacoboni Marco , Kaplan Jonas 
Published: January 2022

Artificial intelligence (AI) is expanding into every niche of human life, organizing our activity, expanding our agency and interacting with us to an increasing extent. At the same time, AI’s efficiency, complexity and refinement are growing quickly. Justifiably, there is increasing concern with the immediate problem of engineering AI that is aligned with human interests. Computational approaches to the alignment problem attempt to design AI systems to parameterize human values like harm and flourishing, and avoid overly drastic solutions, even if these are seemingly optimal. In parallel, ongoing work in service AI (caregiving, consumer care, etc.) is concerned with developing artificial empathy, teaching AI’s to decode human feelings and behavior, and evince appropriate, empathetic responses. This could be equated to cognitive empathy in humans.We propose that in the absence of affective empathy (which allows us to share in the states of others), existing approaches to artificial empathy may fail to produce the caring, prosocial component of empathy, potentially resulting in superintelligent, sociopath-like AI. We adopt the colloquial usage of “sociopath” to signify an intelligence possessing cognitive empathy (i.e., the ability to infer and model the internal states of others), but crucially lacking harm aversion and empathic concern arising from vulnerability, embodiment, and affective empathy (which permits for shared experience). An expanding, ubiquitous intelligence that does not have a means to care about us poses a species-level risk. It is widely acknowledged that harm aversion is a foundation of moral behavior. However, harm aversion is itself predicated on the experience of harm, within the context of the preservation of physical integrity. Following from this, we argue that a “top-down” rule-based approach to achieving caring, aligned AI may be unable to anticipate and adapt to the inevitable novel moral/logistical dilemmas faced by an expanding AI. It may be more effective to cultivate prosociality from the bottom up, baked into an embodied, vulnerable artificial intelligence with an incentive to preserve its real or simulated physical integrity. This may be achieved via optimization for incentives and contingencies inspired by the development of empathic concern in vivo. We outline the broad prerequisites of this approach and review ongoing work that is consistent with our rationale.If successful, work of this kind could allow for AI that surpasses empathic fatigue and the idiosyncrasies, biases, and computational limits of human empathy. The scaleable complexity of AI may allow it unprecedented capability to deal proportionately and compassionately with complex, large-scale ethical dilemmas. By addressing this problem seriously in the early stages of AI’s integration with society, we might eventually produce an AI that plans and behaves with an ingrained regard for the welfare of others, aided by the scalable cognitive complexity necessary to model and solve extraordinary problems.