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Artificial intelligence (AI) agents are increasingly being used as teammates, not just as tools, across many domains, and teammates’ moral behaviour can alter impressions of themselves and the team. How good, powerful, and active is an AI versus human team member engaging in an ethical or unethical behaviour? How good, powerful, and active is their team? To address these questions, we conduct four studies across three domains (chess, esports, and poetry composition) where participants rate their impressions of team members and teams presented in a scenario. In the scenario, a member of a hybrid team of 2 humans and 2 AIs is presented with an opportunity to cheat, and either does or does not. We manipulate which team member (AI vs human) is acting and the morality of that action (non-cheating vs cheating). Across the studies, results show that ethical behaviour significantly increases the goodness of the human more than the AI, and unethical behaviour significantly increases the power of the AI more than the human. However, there were no systematic human versus AI differences on team impressions. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) decision support is increasing in high-stakes contexts, such as healthcare, defense, and finance. Uncertainty information may help users better leverage AI predictions, especially when combined with their domain knowledge. We conducted a human-subject experiment with an online sample to examine the effects of presenting uncertainty information with AI recommendations. The experimental stimuli and task, which included identifying plant and animal images, are from an existing image recognition deep learning model, a popular approach to AI. The uncertainty information was predicted probabilities for whether each label was the true label. This information was presented numerically and visually. In the study, we tested the effect of AI recommendations in a within-subject comparison and uncertainty information in a between-subject comparison. The results suggest that AI recommendations increased both participants' accuracy and confidence. Further, providing uncertainty information significantly increased accuracy but not confidence, suggesting that it may be effective for reducing overconfidence. In this task, participants tended to have higher domain knowledge for animals than plants based on a self-reported measure of domain knowledge. Participants with more domain knowledge were appropriately less confident when uncertainty information was provided. This suggests that people use AI and uncertainty information differently, such as an expert versus second opinion, depending on their level of domain knowledge. These results suggest that if presented appropriately, uncertainty information can potentially decrease overconfidence that is induced by using AI recommendations. [ABSTRACT FROM AUTHOR]

Interest in and ownership of smart home voice assistants like Amazon Alexa and Google Home devices have exponentially increased in recent years. Many people may purchase or be gifted such devices without knowing their potential for connecting with other home technology, listening to private conversations, sharing information with companies, and creating problems due to misunderstanding vocal commands or technological capabilities. Concerns and worries about these devices may be exacerbated over time or by a specific incident. To understand reactions to such situations, we conducted semi-structured in-depth interviews with 10 people who reported different types of worrying incidents with a range of smart home devices and their reactions to reduce that worry. Conducting a thematic coding analysis, we detail how each case study shows a person's worries about their smart home technology developed vis-à-vis the incident or over time, and their strategies to alleviate their worry. The two dominant reactions were restricted acceptance or discontinuance of the smart home technology, while three other interviews revealed nuanced reactions on the acceptance-rejection continuum. For each interviewee, we highlight their technology use, any major incidents, and their psychological processes leading up to their actions to reduce worry. This provides an in-depth look at worry around smart home technology products themselves, not their ability to perform, and how discontinuance, restricted acceptance, and other reactions reduce those worries. [ABSTRACT FROM AUTHOR]

What are the relationships between knowledge of, perceived benefits, adoption of, and use of smart home products? To explore this question, in our first two studies we focus on the general population's perceptions of benefits across many types of smart home products by creating a corpus of smart home product descriptions. Study 1 (n = 399) shows that previous product knowledge influences a range of perceived benefits. Study 2 (n = 242) demonstrates which benefits increase non-owners' likelihood of adopting these products. In study 3, we longitudinally survey eight residents in living laboratory houses equipped with 10 integrated smart home products. We find over a year access to the products increases perceptions of their benefits, but does not increase their actual use. Collectively, these studies contribute to an increased understanding of the relationship among benefits, use, and adoption of this emerging technology. [ABSTRACT FROM AUTHOR]

Do people personally witness artificial intelligence (AI) committing moral wrongs? If so, what kinds of moral wrong and what situations produce these? To address these questions, respondents selected one of six prompt questions, each based on a moral foundation violation, asking about a personally-witnessed interaction with an AI resulting in a moral victim (victim prompts) or where the AI seemed to engage in immoral actions (action prompt). Respondents then answered their selected question in an open-ended response. In conjunction with liberty/privacy and purity moral foundations and across both victim and action prompts, respondents most frequently reported moral violations as two types of exposure by AIs: their personal information being exposed (31%) and people's exposure to undesirable content (20%). AIs expose people's personal information to their colleagues, close relations, and online due to information sharing across devices, people in proximity of audio devices, and simple accidents. AIs expose people, often children, to undesirable content such as nudity, pornography, violence, and profanity due to their proximity to audio devices and to seemly purposeful action. We argue that the prominence in reporting these types of exposure may be due to their frequent occurrence on personal and home devices. This suggests that research on AI ethics should not only focus on the prototypically harmful moral dilemmas (e.g., autonomous vehicle deciding whom to sacrifice) but everyday interactions with personal technology. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) agents make decisions that affect individuals and society which can produce outcomes traditionally considered moral violations if performed by humans. Do people attribute the same moral permissibility and fault to AIs and humans when each produces the same moral violation outcome? Additionally, how do people attribute morality when the AI and human are jointly making the decision which produces that violation? We investigate these questions with an experiment that manipulates written descriptions of four real-world scenarios where, originally, a violation outcome was produced by an AI. Our decision-making structures include individual decision-making - either AIs or humans - and joint decision-making - either humans monitoring AIs or AIs recommending to humans. We find that the decision-making structure has little effect on morally faulting AIs, but that humans who monitor AIs are faulted less than solo humans and humans receiving recommendations. Furthermore, people attribute more permission and less fault to AIs compared to humans for the violation in both joint decision-making structures. The blame for joint AI-human wrongdoing suggests the potential for strategic scapegoating of AIs for human moral failings and the need for future research on AI-human teams. [ABSTRACT FROM AUTHOR]