Human AI
Human-AI interaction research focuses on optimizing collaboration between humans and artificial intelligence, particularly large language models (LLMs), to improve efficiency and quality in tasks like writing and decision-making. Current research emphasizes developing effective human-AI teaming frameworks, including the design of intuitive interfaces and explainable AI (XAI) methods to enhance trust and understanding. This field is crucial for addressing the ethical and practical implications of increasingly prevalent AI tools, impacting diverse areas from education and scientific writing to healthcare and aerospace.
Papers
A Survey on Human-AI Teaming with Large Pre-Trained Models
Vanshika Vats, Marzia Binta Nizam, Minghao Liu, Ziyuan Wang, Richard Ho, Mohnish Sai Prasad, Vincent Titterton, Sai Venkat Malreddy, Riya Aggarwal, Yanwen Xu, Lei Ding, Jay Mehta, Nathan Grinnell, Li Liu, Sijia Zhong, Devanathan Nallur Gandamani, Xinyi Tang, Rohan Ghosalkar, Celeste Shen, Rachel Shen, Nafisa Hussain, Kesav Ravichandran, James Davis
Cooperative Bayesian Optimization for Imperfect Agents
Ali Khoshvishkaie, Petrus Mikkola, Pierre-Alexandre Murena, Samuel Kaski
GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency
Catherine Yeh, Gonzalo Ramos, Rachel Ng, Andy Huntington, Richard Banks
Artificial intelligence and the transformation of higher education institutions
Evangelos Katsamakas, Oleg V. Pavlov, Ryan Saklad