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Knowing When to Ask: Self-Gated Clarification for Hierarchical Language Agents Collaborative Human-Agent Protocol (CHAP) UXBench: Benchmarking User Experience in AI Assistants Impedance MPC for Physical Human-Robot Interaction: Predictive Disturbance Rejection with Joint-Limit Safety Formalizing all indexed mathematics as a benchmark for general reasoning, with the example of implementing dilatations of categories Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset What LLMs Must Forget to Teach Effectively: A DIY Approach to Premodern Japanese Language Pedagogy Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build Learning to Decide with AI Assistance under Human-Alignment Positive Alignment: Artificial Intelligence for Human Flourishing Sycophantic AI makes human interaction feel more effortful and less satisfying over time Prober.ai: Gated Inquiry-Based Feedback via LLM-Constrained Personas for Argumentative Writing Development Exploring Interaction Paradigms for LLM Agents in Scientific Visualization The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers Participatory provenance as representational auditing for AI-mediated public consultation Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading Can LLMs Reason About Attention? 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Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective
Siqi Cao, Di Fu, Xu Yang, Stefan Wermter, Xun Liu, Haiyan Wu · 2021-10-09 · via cs.HC updates on arXiv.org

Sensory and emotional experiences such as pain and empathy are essential for mental and physical health. Cognitive neuroscience has been working on revealing mechanisms underlying pain and empathy. Furthermore, as trending research areas, computational pain recognition and empathic artificial intelligence (AI) show progress and promise for healthcare or human-computer interaction. Although AI research has recently made it increasingly possible to create artificial systems with affective processing, most cognitive neuroscience and AI research do not jointly address the issues of empathy in AI and cognitive neuroscience. The main aim of this paper is to introduce key advances, cognitive challenges and technical barriers in computational pain recognition and the implementation of artificial empathy. Our discussion covers the following topics: How can AI recognize pain from unimodal and multimodal information? Is it crucial for AI to be empathic? What are the benefits and challenges of empathic AI? Despite some consensus on the importance of AI, including empathic recognition and responses, we also highlight future challenges for artificial empathy and possible paths from interdisciplinary perspectives. Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy.