Previous works in Human-Robot Interaction have demonstrated the positive potential benefit of designing social robots which express specific personalities. In this work, we focus specifically on the adaptation of language (as the choice of words, their order, etc.) following the extraversion trait. We look to investigate whether current language models could support more autonomous generations of such personality-expressive robot output. We examine the performance of two models with user studies evaluating (i) raw text output and (ii) text output when used within multi-modal speech from the Furhat robot. We find that the ability to successfully manipulate perceived extraversion sometimes varies across different dialogue topics. We were able to achieve correct manipulation of robot personality via our language adaptation, but our results suggest further work is necessary to improve the automation and generalisation abilities of these models.

Recent works have identified both risks and opportunities afforded by robot gendering. Specifically, robot gendering risks the propagation of harmful gender stereotypes, but may positively influence robot acceptance/impact, and/or actually offer a vehicle with which to educate about and challenge traditional gender stereotypes. Our work sits at the intersection of these ideas, to explore whether robot gendering might impact robot credibility and persuasiveness specifically when that robot is being used to try and dispel gender stereotypes and change interactant attitudes. Whilst we demonstrate no universal impact of robot gendering on first impressions of the robot, we demonstrate complex interactions between robot gendering, interactant gender and observer gender which emerge when the robot engages in challenging gender stereotypes. Combined with previous work, our results paint a mixed picture regarding how best to utilise robot gendering when challenging gender stereotypes this way. Specifically, whilst we find some potential evidence in favour of utilising male presenting robots for maximum impact in this context, we question whether this actually reflects the kind of gender biases we actually set out to challenge with this work.

Risk Assessment is a well known and powerful method for discovering and mitigating risks, and hence improving safety. Ethical Risk Assessment uses the same approach, but extends the scope of risk to cover ethical risks in addition to safety risks. In this paper we outline Ethical Risk Assessment (ERA), and set ERA within the broader framework of Responsible Robotics. We then illustrate ERA, first with a hypothetical smart robot teddy bear (RoboTed), and later with an actual smart robot toy (Purrble). Through these two case studies this paper demonstrates the value of ERA and how consideration of ethical risks can prompt design changes, resulting in more ethical and sustainable robots. 

Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and selfesteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such impact by designing robot behaviors solely based on related (human interaction) literature. In this article, we take a usercentered approach to explore how teenagers envisage a social robot <feminine ordinal indicator>group assistant degrees. We engaged 16 teenagers in focus groups, interviews, and robot testing to capture their views and reflections about robots for groups. Over the course of a two-week summer school, participants co-designed the action space for such a robot and experienced working with/wizarding it for 10+ hours. This experience further altered and deepened their insights into using robots as group assistants. We report results regarding teenagers' views on the applicability and use of a robot group assistant, how these expectations evolved throughout the study, and their repeat interactions with the robot. Our results indicate that each group moves on a spectrum of need for the robot, reflected in use of the robot more (or less) for ice-breaking, turn-taking, and fun-making as the situation demanded.

This article presents a cross-cultural replication of recent work on productively violating gender norms; specifically demonstrating that breaking norms can boost robot credibility while avoiding harmful stereotypes. In this work we demonstrate via a 3 (country) x 3 (robot behaviour) between-subject experiment that these findings replicate cross-culturally across the US, Sweden, and Japan, finding evidence that breaking gender norms boosts robot credibility regardless of gender or cultural context, and regardless of pretest gender biases. Our findings further motivate a call for feminist robots that subvert the existing gender norms of robot design.

The Participatory Design and End-User Programming for Human-Robot Interaction (HRI) workshop aims to advance research on how to design systems that can be used by end users to program robots. There tends to be a fracture in HRI between the technical designers of robot programs (often engineers or computer scientists) and the actual users of such robots. Developers have the capabilities to program robots but often lack insights possessed by domain experts, sometimes leading to technically interesting but impractical systems. With this workshop, we aim to bridge two different methods often used individually within the wider HRI community to involve end users in robot program design: Participatory Design (PD) and End-User Programming (EUP). Both methods empower end users to co-produce robots addressing real-world needs. However, there have been limited opportunities to unite researchers who specialize in these areas and engage in mutual learning. We will address this shortcoming with a full-day workshop, which will put the PD and EUP communities in touch, inviting speakers from both sides and welcoming a wide range of publications from describing new end-user programming methods to compiling insights learned from conducting participatory design studies.

In this paper we apply the recent concept of robot Ethical Risk Assessment to an exemplar Socially Assistive Robot (SAR); specif-cally considering ethical risks posed by anthropomorphism in this context. We draw on two complimentary studies to demonstrate that anthropomorphism is important to overall SAR function and overall relatively low ethical risk. As such, rather than avoiding an-thropomoprhism all together (as suggested in a recently published standard on robot ethics), we suggest anthropomorphism in SARs should be a customisable trait that can be adapted to the user. 

Inspired by the recent UNESCO report I'd Blush if I Could, we tackle some of the issues regarding gendered AI through exploring the impact of feminist social robot behaviour on human-robot interaction. Specifically we consider (i) use of a social robot to encourage girls to consider studying robotics (and expression of feminist sentiment in this context), (ii) if/how robots should respond to abusive, and antifeminist sentiment and (iii) how ('female') robots can be designed to challenge current gender-based norms of expected behaviour. We demonstrate that whilst there are complex interactions between robot, user and observer gender, we were able to increase girls' perceptions of robot credibility and reduce gender bias in boys. We suggest our work provides positive evidence for going against current digital assistant/traditional human gender-based norms, and the future role robots might have in reducing our gender biases. 

Participatory design (PD) has been used to good success in human-robot interaction (HRI) but typically remains limited to the early phases of development, with subsequent robot behaviours then being hardcoded by engineers or utilised in Wizard-of-Oz (WoZ) systems that rarely achieve autonomy. In this article, we present LEADOR (Led-by-Experts Automation and Design Of Robots), an end-to-end PD methodology for domain expert co-design, automation, and evaluation of social robot behaviour. This method starts with typical PD, working with the domain expert(s) to co-design the interaction specifications and state and action space of the robot. It then replaces the traditional offline programming or WoZ phase by an in situ and online teaching phase where the domain expert can live-program or teach the robot how to behave whilst being embedded in the interaction context. We point out that this live teaching phase can be best achieved by adding a learning component to a WoZ setup, which captures implicit knowledge of experts, as they intuitively respond to the dynamics of the situation. The robot then progressively learns an appropriate, expert-approved policy, ultimately leading to full autonomy, even in sensitive and/or ill-defined environments. However, LEADOR is agnostic to the exact technical approach used to facilitate this learning process. The extensive inclusion of the domain expert(s) in robot design represents established responsible innovation practice, lending credibility to the system both during the teaching phase and when operating autonomously. The combination of this expert inclusion with the focus on in situ development also means that LEADOR supports a mutual shaping approach to social robotics. We draw on two previously published, foundational works from which this (generalisable) methodology has been derived to demonstrate the feasibility and worth of this approach, provide concrete examples in its application, and identify limitations and opportunities when applying this framework in new environments.

Robot accidents are inevitable. Although rare, they have been happening since assembly line robots were first introduced in the 1960s. But a new generation of social robots is now becoming commonplace. Equipped with sophisticated embedded artificial intelligence (AI), social robots might be deployed as care robots to assist elderly or disabled people to live independently. Smart robot toys offer a compelling interactive play experience for children, and increasingly capable autonomous vehicles (AVs) offer the promise of hands-free personal transport and fully autonomous taxis. Unlike industrial robots, which are deployed in safety cages, social robots are designed to operate in human environments and interact closely with humans; the likelihood of robot accidents is therefore much greater for social robots than industrial robots. This chapter sets out a draft framework for social robot accident investigation, a framework that proposes both the technology and processes that would allow social robot accidents to be investigated with no less rigour than we expect of air or rail accident investigations. The chapter also places accident investigation within the practice of responsible robotics and makes the case that social robotics without accident investigation would be no less irresponsible than aviation without air accident investigation.