Joseph Masotti

In-silico clinical trials (ISCTs) for medical devices and products are computer-based simulations of material clinical trials, often of randomized clinical trials (RCTs) on populations of ‘virtual’ patients. A major aim for developing ISCTs is to use them to complement (or potentially sometimes replace) material trials for providing evidence about the performance of medical products. While these methods are beginning to see use, especially as additional sources of evidence that regulatory agencies allow, they remain relatively under-theorized by philosophers of science. In this paper, we aim to introduce philosophers of science to ISCTs, focusing on ISCTs that generate results using knowledge-based models. Although we identify a number of areas in philosophy of science that we expect can make significant contributions in thinking about ISCTs, from work on measurement and models to that on computer simulations, our own offerings are on issues related to the use of RCTs themselves, like external and internal validity, and on the various regulatory standards for their use. Because of the practical importance of their use as evidence for effectiveness and safety by regulatory agencies, we focus most on how in-silico randomized control trials (ISRCTs) compare with material RCTs done on real patients, which most regulatory agencies see as the gold standard for evidence of intervention effectiveness. Our primary examples are of medical devices since this seems to be the area where suggestions for ISCT use is most developed. Our hope is that this introductory paper will prompt other philosophers of science to delve deeper into this extremely interesting and important area of applied science that can have real practical effects on our lives as ISCT results are increasingly allowed as evidence for medical effectiveness and safety.

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.

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.