Human AI Interaction
Human-AI interaction research focuses on designing effective and safe collaborations between humans and artificial intelligence systems, primarily aiming to optimize task performance, user experience, and ethical considerations. Current research emphasizes developing novel interaction paradigms beyond simple turn-based exchanges, utilizing large language models (LLMs) and reinforcement learning algorithms to create more natural and intuitive interfaces, including non-verbal communication methods. This field is crucial for advancing AI safety, improving the usability of AI tools across various sectors (healthcare, education, industry), and informing the ethical development and deployment of increasingly autonomous AI systems.
Papers
HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions
Xuhui Zhou, Hyunwoo Kim, Faeze Brahman, Liwei Jiang, Hao Zhu, Ximing Lu, Frank Xu, Bill Yuchen Lin, Yejin Choi, Niloofar Mireshghallah, Ronan Le Bras, Maarten Sap
Artificial Human Intelligence: The role of Humans in the Development of Next Generation AI
Suayb S. Arslan
Trusting Your AI Agent Emotionally and Cognitively: Development and Validation of a Semantic Differential Scale for AI Trust
Ruoxi Shang, Gary Hsieh, Chirag Shah
MindGPT: Advancing Human-AI Interaction with Non-Invasive fNIRS-Based Imagined Speech Decoding
Suyi Zhang, Ekram Alam, Jack Baber, Francesca Bianco, Edward Turner, Maysam Chamanzar, Hamid Dehghani
MindSpeech: Continuous Imagined Speech Decoding using High-Density fNIRS and Prompt Tuning for Advanced Human-AI Interaction
Suyi Zhang, Ekram Alam, Jack Baber, Francesca Bianco, Edward Turner, Maysam Chamanzar, Hamid Dehghani
Attaining Human`s Desirable Outcomes in Human-AI Interaction via Structural Causal Games
Anjie Liu, Jianhong Wang, Haoxuan Li, Xu Chen, Jun Wang, Samuel Kaski, Mengyue Yang
Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making
Min Hun Lee, Silvana Xin Yi Choo, Shamala D/O Thilarajah