Guilty Artificial Minds: Folk Attributions of Mens Rea and Culpability to Artificially Intelligent Agents

Abstract

While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for robot blame, namely the folk's willingness to ascribe inculpating mental states or "mens rea" to robots. In a vignette-based experiment (N=513), we presented participants with a situation in which an agent knowingly runs the risk of bringing about substantial harm. We manipulated agent type (human v. group agent v. AI-driven robot) and outcome (neutral v. bad), and measured both moral judgment (wrongness of the action and blameworthiness of the agent) and mental states attributed to the agent (recklessness and the desire to inflict harm). We found that (i) judgments of wrongness and blame were relatively similar across agent types, possibly because (ii) attributions of mental states were, as suspected, similar across agent types. This raised the question -also explored in the experiment -whether people attribute knowledge and desire to robots in a merely metaphorical way (e.g., the robot "knew" rather than really knew). However, (iii), according to our data people were unwilling to downgrade to mens rea in a merely metaphorical sense when given the chance. Finally, (iv), we report a surprising and novel finding, which we call the inverse outcome effect on robot blame: People were less willing to blame artificial agents for bad outcomes than for neutral outcomes. This suggests that they are implicitly aware of the dangers of overattributing blame to robots when harm comes to pass, such as inappropriately letting the responsible human agent off the moral hook.

Will the use of machines to identify negative activist investments backfire? Experimental evidence

“…The current analysis, which does not document an aversion toward algorithm-mediated investments, is in contrast to earlier research that showed an aversion toward algorithms making investment decisions (Niszczota and Kaszás, 2020). Similar to Kirchkamp and Strobel (2019) and Stuart and Kneer (2021), sharing responsibility with machines led to neither the diffusion nor the exacerbation of responsibility.…”

Intelligence brings responsibility - Even smart AI assistants are held responsible

“…Our most surprising still was that AI assistants were considered more responsible for positive rather than negative outcomes. Our findings align with the inverse outcome effect for blame ascription, 16 where people blamed the AI system less when the outcome was harmful rather than neutral. Stuart & Kneer 16 suggest that the outcome effect arises because people, in case of a harmful outcome, apply a high(er) standard of moral agency—and more demanding standards of intentionality attribution—to identify the person responsible.…”

“…This supports our original hypothesis that instrumental AI-assistants are perceived as agents capable of sharing responsibility with other agents. 13 , 14 , 15 , 16 , 17 …”

“…We call the first the “agentive contribution” hypothesis and the second the “mere tool hypothesis.” If, on the one hand, the mere presence of an AI induces the idea that an independent agent is involved or that the AI assistant could have done something differently, we should expect that it will take a share of responsibility in action carried out by its human user (see Figure 1 , H1)—though not necessarily a 50-50 split. 13 , 14 , 15 , 16 , 17 Relatedly, we can expect that the human driver would be held less responsible when using the AI, as some share of responsibility goes to the AI system for contributing to the decision. 18 , 19

Figure 1 Experimental design and expectations All y axes correspond to 0–100 responsibility ratings.

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Beyond the Courtroom: Agency and the Perception of Free Will

“…What does explain the suprising effect of praise and blame in our study? Stuart and Kneer (2021) have found that people assign more knowledge and blame to an autonomously acting robot when it doesn't commit any harm compared to when it commits harm. This "inverse outcome effect" mirrors the results found in our study.…”