Human Ai Collaboration
Human-AI collaboration (HAIC) research focuses on optimizing the interaction between humans and artificial intelligence systems to achieve superior outcomes compared to either alone. Current research emphasizes improving AI explainability and transparency, particularly through methods like Shapley values and explainable AI (XAI), to foster trust and appropriate reliance, while also addressing issues like AI bias and the potential for misinformation from incorrect explanations. This field is significant because effective HAIC can enhance decision-making across diverse domains, from healthcare and cybersecurity to software engineering and scientific discovery, ultimately leading to more efficient and reliable processes.
Papers
Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration
Hussein Mozannar, Yuria Utsumi, Irene Y. Chen, Stephanie S. Gervasi, Michele Ewing, Aaron Smith-McLallen, David Sontag
A Hierarchical Approach to Population Training for Human-AI Collaboration
Yi Loo, Chen Gong, Malika Meghjani
Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue
Cristian-Paul Bara, Ziqiao Ma, Yingzhuo Yu, Julie Shah, Joyce Chai
Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructions
Shrestha Mohanty, Negar Arabzadeh, Julia Kiseleva, Artem Zholus, Milagro Teruel, Ahmed Awadallah, Yuxuan Sun, Kavya Srinet, Arthur Szlam