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The Effect of Type of Explanation on Algorithm Appreciation: The Role of Risk Perceptions in Healthcare Decision-Making
No full textThis study examines the impact of various types of Artificial Intelligence (AI) explanations—local, counterfactual, and global—on individuals' appreciation of algorithms in healthcare decision-making contexts. Using a scenario-based experiment involving 611 US-based participants, we take a risk perspective to examine how eXplainable (XAI) system credibility (risk probability) and perceived condition severity (risk severity) mediate the relationship between the type of explanation and algorithm appreciation. We also explore how decision-makers’ risk-taking propensity (risk perception) moderates these relationships. Participants assessed diabetes risk predictions for a hypothetical relative based on explanations generated by an XAI system. Findings reveal that the type of explanation significantly influences algorithm appreciation through the perceived severity of the condition, but not through the credibility of the XAI system. Importantly, the effects of the type of explanation vary with participants' risk-taking propensity. Hence, this research highlights the need for personalized, XAI strategies to maximize algorithm appreciation in high-risk healthcare decision-making contexts involving non-expert decision-makers
Exploring the Temporal Dynamics of Facial Mimicry in Emotion Processing Using Action Units
No full textFacial mimicry—the automatic, unconscious imitation of others' expressions—is vital for emotional understanding. This study investigates how mimicry differs across emotions using Face Action Units from videos and participants' responses. Dynamic Time Warping quantified the temporal alignment between participants' and stimuli's facial expressions, revealing significant emotional variations. Post-hoc tests indicated greater mimicry for 'Fear' than 'Happy' and reduced mimicry for 'Anger' compared to 'Fear'. The mimicry correlations with personality traits like Extraversion and Agreeableness were significant, showcasing subtle yet meaningful connections. These findings suggest specific emotions evoke stronger mimicry, with personality traits playing a secondary role in emotional alignment.Notably, our results highlight how personality-linked mimicry mechanisms extend beyond interpersonal communication to affective computing applications, such as remote human-human interactions and human-virtual-agent scenarios. Insights from temporal facial mimicry—e.g., designing digital agents that adaptively mirror user expressions—enable developers to create empathetic, personalized systems, enhancing emotional resonance and user engagement
Multimodal Extraction and Recognition of Arabic Implicit Discourse Relations
No full textMost research on implicit discourse relation identification has focused on written language, however, it is also crucial to understand these relations in spoken discourse. We introduce a novel method for implicit discourse relation identification across both text and speech, that allows us to extract examples of semantically equivalent pairs of implicit and explicit discourse markers, based on aligning speech+transcripts with subtitles in another language variant. We apply our method to Egyptian Arabic, resulting in a novel high-quality dataset of spoken implicit discourse relations. We present a comprehensive approach to modeling implicit discourse relation classification using audio and text data with a range of different models. We find that text-based models outperform audio-based models, but combining text and audio features can lead to enhanced performance