Users' experiences are critical to understanding people's reactions to smart home technology (SHT). Moreover, comparing users' experiences with differing SHT usefully situates those technologies in the context of their competitors. To that end, we compare the brand "titans" of SHT in a controlled, laboratory-based comparative usability study. Forty-nine participants engaged in 21 identical tasks in three "living room" labs—an Amazon room, Apple room, and Google room—each set up with a system of connected SHT devices. Analysis of qualitative and quantitative data shows that, generally, Apple was preferred most overall, followed by Google, then Amazon; however, voice interaction with Apple's Siri was rated lower than the other two. Hubs were generally dispreferred, compared to using voice commands or phone apps, though participants also criticized voice command functionality and app interfaces. The primary themes that emerged as most important for users' evaluations included their prior experience with devices' brand, user friendliness, the layout of device interfaces, and the quality of virtual assistants' functionality.

In the news media, fiction, and conversations we are often presented with utopic and dystopic versions of the future related to the environment or technology. In this research, we ask how thinking about ecological or technological utopias—or dystopias—as potential future societies alters one’s motivation to change and justification of one’s current society. In Study 1a ( n = 121) and 1b ( n = 174), thinking about ecological, but not technological, utopias enhanced motivation to change one’s current society, whereas neither utopia changed justification with one’ current society. Study 2 ( n = 196), which included ecological and technological dystopia conditions, showed that ecological and technological utopias and dystopias increased motivation to change one’s current society, whereas none of them changed justification with one’s current society. Moreover, thinking about an ecological utopia showed added effects: compared to the technological utopia, it was more positively evaluated, functioned more to bring about change, and further increased motivation to change one’s current society. All together these results show that thinking about possible future societies, especially ecological utopias, is a powerful way to motivate change in our present time.

How do robot designers anthropomorphize their own creations? Because robot designers have the ability to alter the robot, identify as its creator, and understand their robot’s internal makeup, their process of anthropomorphism and its outcomes may be different from that of the typical robot user. We investigate this research question in the domain of combat robots, where anthropomorphism is critical to decision-making, communication, and trust in high-stakes, high-emotion combat situations faced by robot-soldier teams. We conducted an in-depth case study of a university’s student-led combat robotics design team over the design, construction, testing, and competition phases for their competitive combat robot. Based on inductive computational and human coding of extensive field notes, supplemented with interviews and surveys, we found that these robot designers anthropomorphize for three purposes. First, they anthropomorphize the bot to manage impressions of it within their team and to outsiders like competitors, spectators, and sponsors, specifically presenting it as a warrior. Second, they anthropomorphize it like a child, a pet, or simply treat it as a non-anthropomorphic mechanical set of parts as a way to calibrate their relationship and attach with or detach from their own creation. Third, they anthropomorphize the bots to assign blame either blaming it, its parts, or others based on their expectations of whether it is performing based on how they designed it. We conclude with implications for anthropomorphism by robot designers and application to military robot design.

Background The US organ transplantation system is pursuing modernization of the allocation process through the integration of new technologies such as artificial intelligence (AI). However, the legal and ethical issues within the transplantation industry are still of concern.Objective We explore the opportunities and challenges for Organ Procurement Organizations (OPOs) to adopt AI. The US organ transplant system is a highly regulated industry yet open to innovation.Methods Ten structured interviews were conducted with OPO representatives using the Extended Technology, Organization, Environment (TOE) framework.Results Overall, we identified five core tensions in AI adoption: (1) misconceptions, (2) approach to training, (3) need for AI expertise, (4) impact of organization size, and (5) top-down versus bottom-up adoption viewpoints. First, some of the positive perceptions of AI, such as bias elimination, are related to misconceptions about what is technically possible. Second, some OPOs believed that using AI systems requires basic knowledge about the AI system, while others stated that AI should be intuitive and require no training. Third, they disagreed on whether it is necessary to add AI-experienced staff as part of an AI adoption strategy. Fourth, smaller OPOs may struggle to develop, maintain, and implement AI systems due to their limited resources, yet they are more nimble and able to pivot due to less bureaucracy. Fifth, there are competing visions for how AI should be adopted across OPOs nationwide, either top-down driven by regulatory requirements or bottom-up driven by performance expectations.Conclusions Ongoing work is needed to determine best practices for integrating AI in OPOs to support optimal organ use and expand transplant access for patients. The TOE framework highlights organization-level tensions that need to be addressed by the transplant sector for successful AI adoption and integration.

Narratives about artificial intelligence (AI) entangle autonomy, the capacity to self-govern, with sentience, the capacity to sense and feel. AI agents that perform tasks autonomously and companions that recognize and express emotions may activate mental models of autonomy and sentience, respectively, provoking distinct reactions. To examine this possibility, we conducted three pilot studies (N = 374) and four preregistered vignette experiments describing an AI as autonomous, sentient, both, or neither (N = 2,702). Activating a mental model of sentience increased general mind perception (cognition and emotion) and moral consideration more than autonomy, but autonomy increased perceived threat more than sentience. Sentience also increased perceived autonomy more than vice versa. Based on a within-paper meta-analysis, sentience changed reactions more than autonomy on average. By disentangling different mental models of AI, we can study human-AI interaction with more precision to better navigate the detailed design of anthropomorphized AI and prompting interfaces.

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized control trial using an ecologically valid and realistic simulation platform for behavioral experiments, to estimate the impact on kidney utilization.

In many ways identities can alter stress processes leading to differential health outcomes; and, less studied, identities also develop as the outcome of stress and health behavior. Stressors and the meaning of those stressors are often affiliated with specific identities. Therefore, identities also provide social support and buffering resources to reduce the stress processes on health outcomes.

The hiring process is crucial for organizational success but has long been troubled by human biases. Many organizations now include AI in their hiring protocols to mitigate these biases and increase efficiency. However, AI itself can have biases baked-in. Human biases and AI biases are distinct but related; here, we examine how human and AI biases interact to affect hiring outcomes. Through an online experiment, we examine this question in the context of gendered hiring for a male-dominated leadership position in electrical engineering. The study tests how elevated and depressed AI recommendations for male and female job candidates affect participant evaluations of those candidates, moderated by participants' attitudes about gender. Findings show that all else constant, elevated AI recommendations increased participants' evaluations of candidates for both competence and likeability, while depressed AI recommendations decreased participants' ratings on both dimensions. However, the benefits of AI recommendations did not distribute evenly. High AI scores benefited male candidates more than female candidates. Ratings were also affected by participants' gender attitudes, revealing effects of sexism on hiring decisions, even when AI is involved. These preliminary findings offer insight into the intersection of human and AI biases as they influence hiring outcomes.

The ethical frontier of artificial intelligence (AI) is expanding as humans form romantic relationships with AIs. Addressing ethical issues of AIs as invasive suitors, malicious advisers, and tools of exploitation requires new psychological research on why and how humans love machines.

If affect control theory (ACT) can accurately predict affective impressions of technology, it can be expanded to human-computer interaction. We compared ACT's predicted impressions to actual collected impressions in three studies. Predicted impressions were 5-20% less accurate for technology actors than human actors (Study 1), similarly accurate in evaluation and activity for technological actors and objects after communication behavior (Study 2a), and 17-28% more accurate for technological actors than objects after physical behaviors (Study 2b). Overall predictions were accurate for technology two-thirds to three-fourths of the time, suggesting ACT's utility for modeling human-computer interaction, though there is room for improvement.

The growing emphasis on human capital has made it more critical for organizations to effectively attract, select, hire, onboard, utilize, and retain talent. While the integration of AI in recruitment offers unprecedented efficiency, scalability, reduced human bias, and optimized talent acquisition, its introduction generates challenges that may influence applicants’ perceptions and behavioral intentions. Based on signaling and organizational justice theories, the lack of human relational cue and AI’s transparency algorithms may lead job applicants to wonder whether the hiring process is trustworthy and procedurally just. Such challenges may alter not only how applicants interpret organizational values, but also whether they choose to complete the application process at all. Therefore, this study examines how AI-driven versus human-driven hiring processes influence trust, procedural justice, organizational attraction, and intent to apply. We further demonstrate how trust and procedural justice shape the relationship between hiring process and organizational attraction and applicants’ intention to apply during the early recruitment stage. An experimental survey of students indicated that applicants perceive AI use in the hiring process as less trustworthy, less just, and less attractive. They also showed less interest in applying for open positions at organizations leveraging AI in the hiring process. Increasing applicants’ trust in the hiring process and providing opportunities to effectively communicate and demonstrate their skills can positively alter perceptions and behavioral intentions of applicants. These findings challenge simplistic assumptions that technological efficiency equates to strategic effectiveness and instead emphasize the centrality of relational cues in attracting talent in an AI-enabled hiring landscape.

Language bias, both positive and negative, is a well-documented phenomenon exhibited among human interlocutors. We examine whether this bias is exhibited toward virtual assistants, specifically, Apple's Siri and Google Assistant, with various accents. We conducted three studies with different stimuli and designs to investigate U.S. English speakers’ attitudes toward Google's British, Indian, and American voices and Apple's Irish, Indian, South African, British, Australian, and American voices. Analysis reveals consistently lower fluency ratings for Irish, Indian, and South African voices (compared with American) but no consistent results of bias related to competence, warmth, or willingness to interact. Moreover, participants often misidentified voices’ countries of origin but correctly identified them as artificial. We conclude that this overall lack of bias may be due to two possibilities: lack of humanlikeness of the voices and lack of availability of nonstandardized voices and voices from countries toward which those in the United States typically show bias.

Objective This study manipulates the presence and reliability of AI recommendations for risky decisions to measure the effect on task performance, behavioral consequences of trust, and deviation from a probability matching collaborative decision-making model. Background Although AI decision support improves performance, people tend to underutilize AI recommendations, particularly when outcomes are uncertain. As AI reliability increases, task performance improves, largely due to higher rates of compliance (following action recommendations) and reliance (following no-action recommendations). Methods In a between-subject design, participants were assigned to a high reliability AI, low reliability AI, or a control condition. Participants decided whether to bet that their team would win in a series of basketball games tying compensation to performance. We evaluated task performance (in accuracy and signal detection terms) and the behavioral consequences of trust (via compliance and reliance). Results AI recommendations improved task performance, had limited impact on risk-taking behavior, and were under-valued by participants. Accuracy, sensitivity ( d’), and reliance increased in the high reliability AI condition, but there was no effect on response bias ( c) or compliance. Participant behavior was only consistent with a probability matching model for compliance in the low reliability condition. Conclusion In a pay-off structure that incentivized risk-taking, the primary value of the AI recommendations was in determining when to perform no action (i.e., pass on bets). Application In risky contexts, designers need to consider whether action or no-action recommendations will be more influential to design appropriate interventions.

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.

The rise of complex AI systems in healthcare and other sectors has led to a growing area of research called Explainable AI (XAI) designed to increase transparency. In this area, quantitative and qualitative studies focus on improving user trust and task performance by providing system- and prediction-level XAI features. We analyze stakeholder engagement events (interviews and workshops) on the use of AI for kidney transplantation. From this we identify themes which we use to frame a scoping literature review on current XAI features. The stakeholder engagement process lasted over nine months covering three stakeholder group's workflows, determining where AI could intervene and assessing a mock XAI decision support system. Based on the stakeholder engagement, we identify four major themes relevant to designing XAI systems - 1) use of AI predictions, 2) information included in AI predictions, 3) personalization of AI predictions for individual differences, and 4) customizing AI predictions for specific cases. Using these themes, our scoping literature review finds that providing AI predictions before, during, or after decision-making could be beneficial depending on the complexity of the stakeholder's task. Additionally, expert stakeholders like surgeons prefer minimal to no XAI features, AI prediction, and uncertainty estimates for easy use cases. However, almost all stakeholders prefer to have optional XAI features to review when needed, especially in hard-to-predict cases. The literature also suggests that providing both systemand prediction-level information is necessary to build the user's mental model of the system appropriately. Although XAI features improve users' trust in the system, human-AI team performance is not always enhanced. Overall, stakeholders prefer to have agency over the XAI interface to control the level of information based on their needs and task complexity. We conclude with suggestions for future research, especially on customizing XAI features based on preferences and tasks.

The use of artificial intelligence (AI) to compose music is becoming mainstream. Yet, there is a concern that listeners may have biases against AIs. Here, we test the hypothesis that listeners will like music less if they think it was composed by an AI. In Study 1, participants listened to excerpts of electronic and classical music and rated how much they liked the excerpts and whether they thought they were composed by an AI or human. Participants were more likely to attribute an AI composer to electronic music and liked music less that they thought was composed by an AI. In Study 2, we directly manipulated composer identity by telling participants that the music they heard (electronic music) was composed by an AI or by a human, yet we found no effect of composer identity on liking. We hypothesized that this was due to the "AI-sounding" nature of electronic music. Therefore, in Study 3, we used a set of "human-sounding" classical music excerpts. Here, participants liked the music less when it was purportedly composed by an AI. We conclude with implications of the AI composer bias for understanding perception of AIs in arts and aesthetic processing theories more broadly. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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-a-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.

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.