Peter Königs recently argued that, while autonomous robots generate responsibility gaps, such gaps need not be considered problematic. I argue that Königs’ compromise dissolves under analysis since, on a proper understanding of what “responsibility” is and what “gap” (metaphorically) means, their joint endorsement must repel an attitude of indifference. So, just as “calamities that happen but don’t bother anyone” makes no sense, the idea of “responsibility gaps that exist but leave citizens and ethicists unmoved” makes no sense. 

“The novel capacities of multimodal generative AI suddenly bring us much closer to realizing the longstanding vision of ubiquitous social robotics” says the opening line of Robophilosophy 2024. However, the normative conceptual space for how we evaluate human treatment of robots is not quite ready for such extension. The majority of discussions are still motivated by mistreatment of robots (like kicking a robot dog) and anthropomorphism, thus resulting in worries that mistreating robots might have undesirable implications or consequences for human moral practices. These approaches thus fall short of accounting for (i) novel and versatile “affordance mixtures” that robots can offer, especially when they’re equipped with specific AI systems; (ii) novel, versatile and dynamic interaction opportunities arising from (i); (iii) that robots can and shall be used in many different areas and ways as products thanks to (i) and (ii), while being part of the social realm as sociable beings. What we need to account for this versatility and novelty is a conceptual framework that can allow us to differentiate an affordance treatment of a robot from a mistreatment, a usage from an abuse. I show that the OASIS framework provides this differentiation.

Social robots, including chatbots and virtual agents, can be fine-tuned on engagement feedback from end users, and their behavior optimized to keep users on the platforms. We refer to strategies induced by social robots in this way as captivation lures, to reflect their manipulative and trap-like nature. We argue that captivation lures differ from phenomena previously studied, including nudging, hypernudging, non-argumentative influence, and subliminal techniques. Researchers have studied nudging - say, in commercials - and hypernudging - say, in recommender systems - for decades, but with recent developments in generative artificial intelligence and social robotics, new ways of manipulating end users have become possible. Captivation lures can be induced from human feedback, and unlike traditional nudging and hypernudging they are not limited to a repository of human-produced content and known nudging strategies. The fact that social robots can induce new, unseen, and hard-to-detect strategies at scale, in the absence of such intentions, has important philosophical and moral implications.