Joseph Masotti

Many philosophers hold that at least one necessary condition for a picture to depict an object is that the picture’s maker held a relevant intention regarding that object appearing in the picture. This is called the “intentional standard of correctness.” In this paper, I argue that AI-generated synthetic images provide strong counterexamples to the intentional standard of correctness. I then argue that we have three possible routes for a theory of depiction in synthetic images: (1) a non-intentionalist theory of depiction, (2) an alternative intentionalist theory of depiction, or (3) synthetic images do not depict anything. I explore each of these options. First, I argue that non-intentionalist views of depiction still face serious counterexamples from synthetic images in addition to other well-known problems. Second, I consider several potential routes for an alternative intentionalist account of depiction for synthetic images, raising doubts about their plausibility. Finally, I argue that while depictive eliminativism for synthetic images avoids troubling counterexamples, it carries counterintuitive implications, including for our ethical practices. I thus argue that none of these options satisfactorily accounts for depiction in synthetic images. I end by providing a diagnosis of why synthetic images in particular pose challenges for a theory of depiction.

A debate in the philosophy of art regards the necessary and sufficient conditions for a picture to depict something. Many philosophers in this debate hold that at least one necessary condition for a picture to depict an object is that the picture’s maker held a relevant intention regarding that object appearing in the picture. This is called “the intentional standard of correctness.” In this paper, I argue that synthetic images generated by AI provide strong counterexamples to the intentional standard of correctness. Using synthetic image technology, one can create pictures that seemingly depict objects without any relevant intention to depict such objects. I then argue, in light of these counterexamples, that we have three possible routes for a theory of depiction in synthetic images: 1) a non-intentionalist theory of depiction, 2) a non-standard intentionalist theory of depiction, or 3) synthetic images do not depict anything. I explore each of these options. First, I argue that non-intentionalist views of depiction still face serious counterexamples from synthetic images in addition to other well-known problems. Second, I consider several potential routes for an alternative intentionalist account of depiction for synthetic images, raising doubts about their plausibility. Finally, I argue that while depictive eliminativism for synthetic images avoids troubling counterexamples, it carries counterintuitive ethical implications. I thus argue that none of these options, as currently formulated, satisfactorily account for depiction in synthetic images. I end by providing a diagnosis of why synthetic images in particular raise trouble for depiction.

This paper argues that the emotional cues exhibited by AI systems designed for social interaction may lead human users to hold misplaced trust in such AI systems, and this poses a substantial problem for human-AI relationships. It begins by discussing the communicative role of certain emotions relevant to perceived trustworthiness. Since displaying such emotions is a reliable indicator of trustworthiness in humans, we use such emotions to assess agents’ trustworthiness according to certain generalizations of folk psychology. Our tendency to engage in anthropomorphism with artificial agents that display these communicative emotions may mislead us into trusting them, even when such trust is misplaced. This phenomenon is referred to as the Emotions-Trustworthiness Gap. This paper demonstrates this general concept with compassion and guilt. In both cases, we may be tempted to judge that AI systems' mere mimicking of relevant emotions constitutes evidence for their trustworthiness. However, most current AI systems are not trustworthy on any major view of trustworthy AI. Thus, the trust elicited from human users will be misplaced. This tendency of human users to hold misplaced trust in their AI companions leaves them vulnerable to serious harm. Several potential solutions to the Emotions-Trustworthiness Gap are discussed.

Tourette’s Syndrome can involve disruptive “ticcing” behavior. Past work suggests that people sometimes blame those making tics for such disruptions. In the current work, we examined how blame perceptions vary depending on the person’s obligation and capacity to refrain from ticcing. Across two studies, we manipulated whether a person ticced in a formal versus informal social situation (obligation), after a weak versus strong urge to tic (capacity). We assessed perceptions of blame, free will, and moral character. Blame increased with increasing obligation and capacity, and perceptions of free will primarily increased with increasing capacity, with perceived obligation playing a smaller role. Moral character ratings were largely unaffected by the manipulations, but people rated the person higher in moral character when their perceived obligation and capacity were low. Generally, women, younger people, and people who knew someone with Tourette’s rated the person who ticced more positively. Overall, these results suggest that whereas blame is sensitive to a person’s obligation and capacity, these perceptions do not map cleanly onto moral character perceptions, in part perhaps due to reduced free will perceptions. Efforts to normalize tics may reduce the perceived obligation to refrain, thereby avoiding both the pitfalls of blaming people with Tourette’s or viewing them as lower in free will.