We live in an age where robots are increasingly present in the social and moral world. Here, we explore how children and adults think about the mental lives and moral standing of robots. In Experiment 1 (N = 116), we found that children granted humans and robots with more mental life and vulnerability to harm than an anthropomorphized control (i.e., a toy bear). In Experiment 2 (N = 157), we found that, relative to children, adults ascribed less mental life and vulnerability to harm to robots. In Experiment 3 (N = 152), we modified our experiment to be within-subjects and measured beliefs concerning moral standing. Though younger children again appeared willing to assign mental capacities — particularly those related to experience (e.g., being capable of experiencing hunger) — to robots, older children and adults did so to a lesser degree. This diminished attribution of mental life tracked with diminished ratings of robot moral standing. This informs ongoing debates concerning emerging attitudes about artificial life.

An artificial system that successfully performs cognitive tasks may pass tests of 'intelligence' but not yet operate in ways that are morally appropriate. An important step towards developing moral artificial intelligence (AI) is to build robust methods for assessing moral capacities in these systems. Here, we present a framework for analysing and evaluating moral capacities in AI systems, which decomposes moral capacities into tractable analytical targets and produces tools for measuring artificial moral cognition. We show that decomposing moral cognition in this way can shed light on the presence, scaffolding, and interdependencies of amoral and moral capacities in AI systems. Our analysis framework produces a virtuous circle, whereby developmental psychology can enhance how AI systems are built, evaluated, and iterated on as moral agents; and analysis of moral capacities in AI can generate new hypotheses surrounding mechanisms within the human moral mind.

In developing artificial intelligence (AI), researchers often benchmark against human performance as a measure of progress. Is this kind of comparison possible for moral cognition? Given that human moral judgment often hinges on intangible properties like “intention” which may have no natural analog in artificial agents, it may prove difficult to design a “like‐for‐like” comparison between the moral behavior of artificial and human agents. What would a measure of moral behavior for both humans and AI look like? We unravel the complexity of this question by discussing examples within reinforcement learning and generative AI, and we examine how the puzzle of evaluating artificial agents' moral cognition remains open for further investigation within cognitive science.