There are a wide variety of potential applications of artificial intelligence (AI) in Defence settings, ranging from the use of autonomous drones to logistical support. However, limited research exists exploring how the public view these, especially in view of the value of public attitudes for influencing policy-making. An accurate understanding of the public’s perceptions is essential for crafting informed policy, developing responsible governance, and building responsive assurance relating to the development and use of AI in military settings. This study is (...) the first to explore public perceptions of and attitudes towards AI in Defence. A series of four focus groups were conducted with 20 members of the UK public, aged between 18 and 70, to explore their perceptions and attitudes towards AI use in general contexts and, more specifically, applications of AI in Defence settings. Thematic analysis revealed four themes and eleven sub-themes, spanning the role of humans in the system, the ethics of AI use in Defence, trust in AI versus trust in the organisation, and gathering information about AI in Defence. Participants demonstrated a variety of misconceptions about the applications of AI in Defence, with many assuming that a variety of different technologies involving AI are already being used. This highlighted a confluence between information from reputable sources combined with narratives from the mass media and conspiracy theories. The study demonstrates gaps in knowledge and misunderstandings that need to be addressed, and offers practical insights for keeping the public reliably, accurately, and adequately informed about the capabilities, limitations, benefits, and risks of AI in Defence. (shrink)

In response to calls for research to improve human-machine teaming, we present a “perspective” paper that explores techniques from computer science that can enhance machine agents for human-machine teams. As part of this paper, we summarize the state of the science on critical team competencies identified for effective HMT, discuss technological gaps preventing machines from fully realizing these competencies, and identify ways that emerging artificial intelligence capabilities may address these gaps and enhance performance in HMT. We extend beyond extant literature (...) by incorporating recent technologies and techniques and describing their potential for contributing to the advancement of HMT. (shrink)

The concept of meaningful work has recently received increased attention in philosophy and other disciplines. However, the impact of the increasing robotization of the workplace on meaningful work has received very little attention so far. Doing work that is meaningful leads to higher job satisfaction and increased worker well-being, and some argue for a right to access to meaningful work. In this paper, we therefore address the impact of robotization on meaningful work. We do so by identifying five key aspects (...) of meaningful work: pursuing a purpose, social relationships, exercising skills and self-development, self-esteem and recognition, and autonomy. For each aspect, we analyze how the introduction of robots into the workplace may diminish or enhance the meaningfulness of work. We also identify a few ethical issues that emerge from our analysis. We conclude that robotization of the workplace can have both significant negative and positive effects on meaningful work. Our findings about ways in which robotization of the workplace can be a threat or opportunity for meaningful work can serve as the basis for ethical arguments for how to—and how not to—implement robots into workplaces. (shrink)

In the past decade, the fields of machine learning and artificial intelligence (AI) have seen unprecedented developments that raise human-machine interactions (HMI) to the next level.Smart machines, i.e., machines endowed with artificially intelligent systems, have lost their character as mere instruments. This, at least, seems to be the case if one considers how humans experience their interactions with them. Smart machines are construed to serve complex functions involving increasing degrees of freedom, and they generate solutions not fully anticipated by humans. (...) Consequently, their performances show a touch of action and even autonomy. HMI is therefore often described as a sort of “cooperation” rather than as a mere application of a tool. Some authors even go as far as subsuming cooperation with smart machines under the label ofpartnership, akin to cooperation between human agents sharing a common goal. In this paper, we explore how far the notion of shared agency and partnership can take us in our understanding of human interaction with smart machines. Discussing different topoi related to partnerships in general, we suggest that different kinds of “partnership” depending on the form of interaction between agents need to be kept apart. Building upon these discussions, we propose a tentative taxonomy of different kinds of HMI distinguishing coordination, collaboration, cooperation, and social partnership. (shrink)

This article defends two claims. First, humans could be in relationships characterised by hate with some robots. Second, it matters that humans could hate robots, as this hate could wrong the robots (by leaving them at risk of mistreatment, exploitation, etc.). In defending this second claim, I will thus be accepting that morally considerable robots either currently exist, or will exist in the near future, and so it can matter (morally speaking) how we treat these robots. The arguments presented in (...) this article make an important original contribution to the robo-philosophy literature, and particularly the literature on human–robot relationships (which typically only consider positive relationship types, e.g., love, friendship, etc.). Additionally, as explained at the end of the article, my discussions of robot hate could also have notable consequences for the emerging robot rights movement. Specifically, I argue that understanding human–robot relationships characterised by hate could actually help theorists argue for the rights of robots. (shrink)

This paper discusses the robotization of the workplace, and particularly the question of whether robots can be good colleagues. This might appear to be a strange question at first glance, but it is worth asking for two reasons. Firstly, some people already treat robots they work alongside as if the robots are valuable colleagues. It is worth reflecting on whether such people are making a mistake. Secondly, having good colleagues is widely regarded as a key aspect of what can make (...) work meaningful. In discussing whether robots can be good colleagues, the paper compares that question to the more widely discussed questions of whether robots can be our friends or romantic partners. The paper argues that the ideal of being a good colleague has many different parts, and that on a behavioral level, robots can live up to many of the criteria typically associated with being a good colleague. Moreover, the paper also argues that in comparison with the more demanding ideals of being a good friend or a good romantic partner, it is comparatively easier for a robot to live up to the ideal of being a good colleague. The reason for this is that the “inner lives” of our friends and lovers are more important to us than the inner lives of our colleagues. (shrink)

The anticipated social capabilities of robots may allow them to serve in authority roles as part of human-machine teams. To date, it is unclear if, and to what extent, human team members will comply with requests from their robotic teammates, and how such compliance compares to requests from human teammates. This research examined how the human-likeness and physical embodiment of a robot affect compliance to a robot's request to perseverate utilizing a novel task paradigm. Across a set of two studies, (...) participants performed a visual search task while receiving ambiguous performance feedback. Compliance was evaluated when the participant requested to stop the task and the coach urged the participant to keep practicing multiple times. In the first study, the coach was either physically co-located with the participant or located remotely via a live-video. Coach type varied in human-likeness and included either a real human, a Nao robot, or a modified Roomba robot. The second study expanded on the first by including a Baxter robot as a coach and replicated the findings in a different sample population with a strict chain of command culture. Results from both studies showed that participants comply with the requests of a robot for up to 11 min. Compliance is less than to a human and embodiment and human-likeness on had weak effects on compliance. (shrink)