Intuitively, proper referential extensions of psychological and moral terms exclude artifacts. Yet ordinary speakers commonly treat AI robots as moral patients and use psychological terms to explain their behavior. This paper examines whether this referential shift from the human domain to the AI domain entails semantic changes: do ordinary speakers literally consider AI robots to be psychological or moral beings? Three non-literalist accounts for semantic changes concerning psychological and moral terms used in the AI domain will be discussed: the technical (...) view (ordinary speakers express technical senses), the habit view (ordinary speakers subconsciously express ingrained social habits), and the emotion view (ordinary speakers express their own affective empathetic emotional states). I discuss whether these non-literalist accounts accommodate the results of relevant empirical experiments. The non-literalist accounts are shown to be implausible with respect to the ordinary use of agency-terms (e.g., “believe,” “know,” “decide,” etc.), and therefore I conclude that the concepts ordinary speakers express by agency-terms in reference to AI robots are similar to the concepts they express when applying the same terms to humans. When ordinary speakers extend emotion-terms and/or moral-patiency-terms to AI robots, however, I argue that semantic changes have taken place because ordinary speakers are in fact referring to their own affective empathetic emotional states rather than AI robots. This argument suggests that the judgments made by ordinary speakers regarding the proper referential extensions of emotion-terms and moral-patiency-terms are fallacious. (shrink)

The potential capacity for robots to deceive has received considerable attention recently. Many papers explore the technical possibility for a robot to engage in deception for beneficial purposes (e.g., in education or health). In this short experimental paper, I focus on a more paradigmatic case: robot lying (lying being the textbook example of deception) for nonbeneficial purposes as judged from the human point of view. More precisely, I present an empirical experiment that investigates the following three questions: (a) Are ordinary (...) people willing to ascribe deceptive intentions to artificial agents? (b) Are they as willing to judge a robot lie as a lie as they would be when human agents engage in verbal deception? (c) Do people blame a lying artificial agent to the same extent as a lying human agent? The response to all three questions is a resounding yes. This, I argue, implies that robot deception and its normative consequences deserve considerably more attention than they presently receive. (shrink)

As artificial intelligence becomes more sophisticated and robots approach autonomous decision-making, debates about how to assign moral responsibility have gained importance, urgency, and sophistication. Answering Stenseke’s (2022a) call for scaffolds that can help us classify views and commitments, we think the current debate space can be represented hierarchically, as answers to key questions. We use the resulting taxonomy of five stances to differentiate—and defend—what is known as the “blank check” proposal. According to this proposal, a person activating a robot could (...) willingly make themselves answerable for whatever events ensue, even if those events stem from the robot’s autonomous decision(s). This blank check solution was originally proposed in the context of automated warfare (Champagne & Tonkens, 2015), but we extend it to cover all robots. We argue that, because moral answerability in the blank check is accepted voluntarily and before bad outcomes are known, it proves superior to alternative ways of assigning blame. We end by highlighting how, in addition to being just, this self-initiated and prospective moral answerability for robot harm provides deterrence that the four other stances cannot match. (shrink)

In two experiments (total N=693) we explored whether people are willing to consider paintings made by AI-driven robots as art, and robots as artists. Across the two experiments, we manipulated three factors: (i) agent type (AI-driven robot v. human agent), (ii) behavior type (intentional creation of a painting v. accidental creation), and (iii) object type (abstract v. representational painting). We found that people judge robot paintings and human painting as art to roughly the same extent. However, people are much less (...) willing to consider robots as artists than humans, which is partially explained by the fact that they are less disposed to attribute artistic intentions to robots. (shrink)