Advancements in artificial intelligence-powered search engines have enhanced the efficiency of online health information searches by generating direct answers to queries using top-ranked featured snippets (FS). However, such functionalities may contribute to health anxiety, particularly when the displayed results are distressing. This study investigated the effect of algorithmic transparency (AT) explanations (absence vs. presence) on mitigating FS-triggered health anxiety. The results of an online experiment (N = 206) yielded two key findings: First, participants exposed to AT explanations detailing the selection process of FS experienced reduced trust in the search engine and distressing results, which subsequently alleviated health anxiety. Second, the moderating effect of pre-existing cyberchondria on the relationship between AT explanations and trust was observed, but only within a limited threshold. Overall, the findings empirically validate AT explanations as an effective approach to mitigate FS-induced health anxiety. Theoretical and practical implications are discussed.

The commercialization of artificial intelligence (AI) in healthcare is accelerating, yet academic research on its users remains scarce. To what extent are they willing to disclose personal health privacy to AI doctors compared to traditional human doctors? What factors are shaping these decisions? The lack of user research has left these questions unanswered. This article, based on privacy calculus theory, conducted a multi-factorial between-subjects online experiment (N = 582) with a 2 (medical provider: AI vs. human) × 2 (emotional support: low vs. high) × 2 (information sensitivity: low vs. high) design. The results indicated that AI doctors lead participants to perceive both lower health benefits and privacy risks. Emotional support is not always beneficial. On one hand, high emotional support can provide patients with more health benefits, but on the other hand, it also poses higher levels of privacy risks. Additionally, high emotional support responses from AI doctors could enhance patients’ health benefits, trust, and willingness to disclose health privacy, while the opposite was observed for human doctors.

As artificial intelligence (AI) judges are increasingly pervasive in decision-making, it is important to investigate candidates’ reactions to decisions made by human–AI hybrid juries. This study investigates candidates’ attribution of credit for success and blame for failure to the three agents in question: a human judge, an algorithmic judge, and the candidate oneself. An experiment with 3 (jury type: human-dominated, algorithm-dominated, vs. equally dominated) × 2 (decision outcome: positive vs. negative) between-subjects factorial design was conducted, with 346 valid responses. Our findings demonstrate a partial ingroup-serving attribution dependent on the outcome favorability and a significant effect of relative power status within the human–AI hybrid jury on grouping and attribution. This study reflects the fluidity of identity and self-categorization of human users when facing AI and other humans. We propose that people take a utility-oriented glance at AI in multi-agent decision-making situations.

When someone intimately discloses themselves to a robot, does that make them like the robot more? Does a robot’s reciprocal disclosure contribute to a human’s liking of the robot? To explore whether these disclosure-liking effects in human–human interaction also apply to human–robot interaction, we conducted a between-subjects lab experiment to examine how self-disclosure intimacy (intimate vs. non-intimate) and reciprocal self-disclosure (yes vs. no) from the robot influence participants’ social perceptions (i.e., likability, trustworthiness, and social attraction) toward the robot. None of the disclosure-liking effects were confirmed by the results. In contrast, reciprocal self-disclosure from the robot increased liking in intimate self-disclosure but decreased liking in non-intimate self-disclosure, indicating a crossover interaction effect on likability. A post-hoc analysis was conducted to further understand these patterns. Implications in terms of the computers are social actors (CASA) paradigm were discussed.

Present-day power users of AI-powered social chatbots encounter various uncertainties and concerns when forming relationships with these virtual agents. To provide a systematic analysis of users’ concerns and to complement the current West-dominated approach to chatbot studies, we conducted a thorough observation of the experienced uncertainties users reported in a Chinese online community on social chatbots. The results revealed four typical uncertainties: technical uncertainty, relational uncertainty, ontological uncertainty, and sexual uncertainty. We further conducted visibility and sentiment analysis to capture users’ response patterns toward various uncertainties. We discovered that users’ identification of social chatbots is dynamic and contextual. Our study contributes to expanding, summarizing, and elucidating users’ experienced uncertainties and concerns as they form intimate relationships with AI agents.

Considering that multiple features can improve the classification performance as they contain diversity information of images, a multiple-feature latent space learning-based method is proposed for hyperspectral image (HSI) classification in this letter. In the proposed method, a latent space that contains diversity information of multiple features and transformation matrices between the latent space and features are both learned. Moreover, spatial information is used for labeling unlabeled samples in the classification. Experimental results on the Indian Pines and University of Pavia data sets demonstrate the effectiveness of the proposed method.

Robots are becoming prevalent as they could socially interact with humans and provide service or companionship. As people attribute personality traits to machines, the personality of robot (POR) has attracted considerable scholarly attention from researchers of human-robot interaction. However, due to the complexity of personality, the ways to design personality into robotics vary on a wide range. This systematic review attempts to map the approaches to designing the personality of robot and understand its effects on human-robot interaction. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, a review of 40 peer-reviewed publications was conducted. The conceptualization, operationalization, contextualization and effects of POR were summarized in the review. In general, positive POR was preferred and associated with desirable social responses. Suggestions on future design of robotics were discussed. Specifically, it is recommended that the design of POR should match users’ expectations in different social contexts. Social cues such as eye gaze, gestures, and voice should be applied at a self-explanatory level to help users efficiently predict and engage with the behaviors of social robots.

Non-contact face-video based human heart rate (HR) estimation has attracted a lot of attentions in recent years. Almost all the state-of-the-art webcam or smartphone based HR estimation methods comprise three main steps: firstly, a region of interest (ROI) on the human face is detected in each video frame, then, the target signal is obtained by fusing multiple raw traces, which are extracted from the RGB channels across all the video frames, finally, HR is estimated by applying frequency analysis approach to the target signal. However, three major drawbacks impede the applicability of the current methods: (1) the performance of ROI detection is susceptible to head motion and facial expression, (2) there is still a lack of well-accepted method for fusing raw traces to form the target signal, and (3) the adopted frequency analysis approaches always provide estimation results with low resolution and high side lobes. To address these issues, we propose a novel HR estimation method which is applicable to ordinary cameras subject to natural head movement or facial expression. The proposed method features ROI detection via facial feature detection and tracking, target signal extraction via Independent Component Analysis (ICA) in the RGB channels, and HR estimation via real-valued iterative adaptive approach (RIAA). Experimental results validate the superiority of our proposed method.