Guilty Artificial Minds
This research addresses the problem of moral judgment in AI for ethicists and policymakers, but it is incremental as it builds on prior comparisons between human and artificial agents.
The study investigated how humans attribute blame and moral wrongness to artificial intelligence agents by decomposing these judgments into epistemic, conative, and consequential components, and comparing them with human and group agents, finding that group agents serve as an intermediate case between humans and AI.
The concepts of blameworthiness and wrongness are of fundamental importance in human moral life. But to what extent are humans disposed to blame artificially intelligent agents, and to what extent will they judge their actions to be morally wrong? To make progress on these questions, we adopted two novel strategies. First, we break down attributions of blame and wrongness into more basic judgments about the epistemic and conative state of the agent, and the consequences of the agent's actions. In this way, we are able to examine any differences between the way participants treat artificial agents in terms of differences in these more basic judgments. our second strategy is to compare attributions of blame and wrongness across human, artificial, and group agents (corporations). Others have compared attributions of blame and wrongness between human and artificial agents, but the addition of group agents is significant because these agents seem to provide a clear middle-ground between human agents (for whom the notions of blame and wrongness were created) and artificial agents (for whom the question remains open).