Computers in Human Behavior: Artificial Humans最新文献

Social robots are upcoming innovations in the healthcare sector. Currently, those robots are merely used for entertaining and accompanying people, or facilitating telepresence. Social robots have the potential to perform more added value tasks within healthcare. So, the aim of our paper was to study the acceptance of a social robot in a rehabilitation centre. This paper reports on three studies conducted with the Pepper robot. We first conducted an acceptance study in which patients (N = 6) and nurses (N = 10) performed different tasks with the robot and rated their acceptance of the robot at different time points. These participants were also interviewed afterwards to gather more qualitative data. The second study conducted was a flash mob study in which patients (N = 23) could interact with the robot via a chatbot and complete a questionnaire. Afterwards, 15 patients completed a short evaluation questionnaire about the easiness and intention to use the robot and possible new functionalities for a social robot. Finally, a Social Return on Investment analysis was conducted to assess the added value of the Pepper robot. Considering the findings from these three studies, we conclude that the use of the Pepper robot for health-related tasks in the context a rehabilitation centre is not yet feasible as major steps are needed to have the Pepper robot able to take over these health-related tasks.

Artificial intelligence (AI) systems increasingly support human decision-making in fields like medicine, management, and finance. However, such human-AI (HAI) collaboration is often less effective than AI systems alone. Moreover, efforts to make AI recommendations more transparent have failed to improve the decision quality of HAI collaborations. Based on dual process theories of cognition, we hypothesized that suboptimal HAI collaboration is partly due to heuristic information processing of humans, creating a trust imbalance towards the AI system. In an online experiment with 337 participants, we investigated motivation and accuracy information as potential factors to induce more deliberate elaboration of AI recommendations, and thus improve HAI collaboration. Participants worked on a simulated personnel selection task and received recommendations from a simulated AI system. Participants' motivation was varied through gamification, and accuracy information through additional information from the AI system. Results indicate that both motivation and accuracy information positively influenced HAI performance, but in different ways. While high motivation primarily increased humans’ use in high-quality recommendations only, accuracy information improved both the use of low- and high-quality recommendations. However, a combination of high motivation and accuracy information did not yield additional improvement of HAI performance.

Background

Artificial Intelligence (AI) is expected to transform the work context deeply. Currently, multiple AI systems are being studied and applied in the mental healthcare field, challenging traditional ways of performing tasks by professionals.

Objectives

This study aims to verify to what extent AI impacts mental healthcare workers’ tasks, describe how AI impacts those tasks, and identify which tasks are impacted.

Design

Two databases were used to find empirical research published between 2019 and December 2022. A total of 46 papers were included in the review.

Results

AI was most often employed for assessment tasks, in which it is generated to support physicians in the diagnostic process. Patient monitoring was also explored by a few papers, which applied intelligent systems to aid professionals by identifying variables that can predict the outcome of the therapeutic process and detect the patients' mood. Regarding therapy, AI systems can contribute by providing insights into patient-therapist interaction and the patient's emotional states. Finally, documentation and medical prescriptions were addressed by one article which measured physicians' opinions on the impact of AI on their jobs.

Conclusion

Artificial Intelligence systems impact the tasks of mental healthcare workers by providing support and enabling greater insights. Most systems aimed to aid mental healthcare workers instead of replacing them. These results highlight the relevance of training professionals to enable hybrid intelligence.

ChatGPT, the latest iteration of OpenAI's natural language generation model, has found applications in a wide range of tasks such as question answering, text summarization, machine translation, classification, code generation, and dialogue A.I. Its potential in the financial industry has garnered significant attention. This paper aims to bridge the gap between chatGPT and human services in the financial domain, while also exploring the opportunities and challenges it presents in this industry. To comprehensively evaluate the processing capabilities of chatGPT in the financial field, we collected a dataset of n = 7165 financial questions and analyzed the perplexity value, emotion value, accuracy, professionalism, and real-time performance of both human-generated and chatGPT-generated content using machine learning algorithms and evaluation tests. The experimental results indicate that chatGPT exhibits higher levels of professionalism and accuracy compared to manual services, leading to improved efficiency, cost reduction, and enhanced customer satisfaction, thereby boosting the competitiveness and profitability of financial institutions. However, challenges such as a lack of emotional value in its responses, potential bias from one-sided training data, information errors, and the risk of job displacement need to be addressed. These findings provide theoretical and data-driven support for the future implementation of chatGPT in financial innovation and development.

Similar to interactions between humans, social robots are able to adapt to different people by altering their behavior. However, in contrast to humans, robots' adaptations allow for more extensive configurations, for example switching their persona to the most fitting one for the next user. Because people normally do not experience such fast and comprehensive adaptations of their interaction partners, such persona adaptations might cause unintended problems in multi-user scenarios if they are witnessed by a robot's users. Referring to perspective-taking and self-monitoring theory this laboratory study experimentally tested the effects of interpersonal adaptations on users' evaluations of robots and their interaction duration by manipulating the degree of experienced adaptation (none, behavioral, persona) in a between-subjects design. Empirical data from N = 115 participants contradict the assumption that experienced persona adaptations of social robots do necessarily impair human-robot interactions. This is shown with regard to both, users' time spent on the interaction and the evaluation of the respective robot. Furthermore, no benefits of behavioral adaptations could be observed, that were expected to unfold since they should not conflict with users' perceived understanding of the robot. In sum, both kinds of robotic adaptations were perceived similarly to the non-adapting robot.

Recent advancements in artificial intelligence and natural language processing, particularly in developing powerful Generative AI tools such as ChatGPT, have piqued the interest of social psychology researchers. The potential of ChatGPT to revolutionize the field by analyzing vast amounts of textual data, modeling social interactions, and providing valuable insights into human behavior and social dynamics is undeniable. However, the application of Generative AI in social psychology research also presents ethical, theoretical, and methodological challenges that must be addressed. This paper provides a comprehensive overview of the use of ChatGPT in social psychology research, examining its benefits and limitations and discussing recommendations for its practical and responsible use. Additionally, we emphasize the importance of developing a clear theoretical framework that links the application of Generative AI to existing social psychology theories, ensuring that the technology contributes meaningfully to advancing knowledge in the field. Researchers can harness its potential while safeguarding their work's ethical and scientific integrity by navigating these challenges and adopting a critical and reflective stance towards using Generative AI.

The ability to resolve conflict while preserving relationships is ever more vital in our divisive, global society. Traditional conflict-resolution training is mostly delivered in one-off sessions with practice opportunities limited to a fixed number of pre-defined role play scenarios. This is insufficient for acquiring the notoriously difficult skill of communicating effectively amidst conflict. We present a new web application that teaches relationship-preserving language for conflict resolution. Our system uses artificial intelligence (AI) to provide automated feedback to open text, natural language input, alerting users to language that may sound judgmental or be otherwise ineffective for resolving conflict. Our application prompts users to respond to scenarios of workplace conflict while receiving feedback from the AI. We conducted qualitative interviews with 13 participants and explore a range of themes relevant to our users’ experiences. We discuss design implications of our results through the cognitive, active, affective and relational dimensions of experiential design.