Future robots are expected to be autonomous actors, even capable of moral reasoning. Yet how they can provide transparent explanations while being socially intelligent during morally relevant interactions deserves a close examination. Our mixed-methods lab study on a human-robot moral debate on the footbridge dilemma showed that quantitatively, a robot’s perceived competence was significantly higher with transparency cues (additional information presented on a screen). The robot’s perceived warmth and mind were not influenced by transparency cues, but they did significantly change over time (pre- vs. post-debate). The change in the robot’s perceived mind and social attributes after the debate correlated with people’s trust in the robot; transparency cues did not correlate with trust. Qualitatively, the robot was described to be logical, unemotional, and intentional in making moral decisions; participants focused on its gaze and speech. While transparency may help in theory, if people do not observe relevant cues while attributing intentionality to the robot and its gaze, transparency cues may not be useful during critical decision-making though the robot can seem competent. We discuss the implications and call for broadening the notion of transparency to investigate how robots can be transparent communicators by appealing to both cognition and affect in morally sensitive interactions.

As robots may take a greater part in our moral decision-making processes, whether people hold them accountable for moral harm becomes critical to explore. Blame and punishment signify moral accountability, often involving emotions. We quantitatively looked into people’s willingness to blame or punish an emotional vs. non-emotional robot that admits to its wrongdoing. Studies 1 and 2 (online video interaction) showed that people may punish a robot due to its lack of perceived emotional capacity than its perceived agency. Study 3 (in the lab) demonstrated that people were neither willing to blame nor punish the robot. Punishing non-emotional robots seems more likely than blaming them, yet punishment towards robots is more likely to arise online than offline. We reflect on if and why victimized humans (and those who care for them) may seek out retributive justice against robot scapegoats when there are no humans to hold accountable for moral harm.

Abstract Cryptocurrencies are proliferating as instantiations of blockchain, which is a transparent, distributed ledger technology for validating transactions. Blockchain is thus said to embed trust in its technical design. Yet, blockchain’s technical promise of trust is not fulfilled when applied to the cryptocurrency ecosystem due to many social challenges stakeholders experience. By investigating a cryptocurrency chatbot (Brokerbot) that distributed information on cryptocurrency news and investments, we explored social tensions of trust between stakeholders, namely the bot’s developers, users, and the bot itself. We found that trust in Brokerbot and in the cryptocurrency ecosystem are two conjoined, but separate challenges that users and developers approached in different ways. We discuss the challenging, dual-role of a Brokerbot as anobject of trustas a chatbot while simultaneously being amediator of trustin cryptocurrency, which exposes the social-technical gap of trust. Lastly, we elaborate on trust as a negotiated social process that people shape and are shaped by through emerging ecologies of interlinked technologies like blockchain and conversational interfaces.