Human Centric
Human-centric AI focuses on aligning artificial intelligence systems with human values, needs, and expectations, prioritizing user understanding, trust, and collaboration. Current research emphasizes developing models that can understand and respond appropriately to human behavior in diverse contexts, employing techniques like reinforcement learning from human feedback, large language models for generating human-centric environments, and multimodal data fusion for improved perception and decision-making. This field is crucial for ensuring the safe and beneficial integration of AI into society, impacting areas such as healthcare, robotics, and human-computer interaction.
Papers
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models
Jiayi Guo, Zan Chen, Yingrui Ji, Liyun Zhang, Daqin Luo, Zhigang Li, Yiqin Shen
HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding
Keliang Li, Zaifei Yang, Jiahe Zhao, Hongze Shen, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen