Guilty Artificial Minds

TL;DR

This paper investigates how humans attribute blameworthiness and moral wrongness to artificial agents by analyzing basic judgments about their mental states and comparing responses across human, artificial, and group agents.

Contribution

It introduces a novel approach by breaking down blame and wrongness into basic judgments and compares these across human, artificial, and group agents to understand moral attributions.

Findings

Participants differentiate blame and wrongness based on epistemic and conative judgments.

Group agents are perceived as a middle ground between humans and artificial agents.

The study provides insights into moral judgments involving artificial intelligence.

Abstract

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).