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
Towards Hybrid Intelligence in Journalism: Findings and Lessons Learnt from a Collaborative Analysis of Greek Political Rhetoric by ChatGPT and Humans
Thanasis Troboukis, Kelly Kiki, Antonis Galanopoulos, Pavlos Sermpezis, Stelios Karamanidis, Ilias Dimitriadis, Athena Vakali
Enhancing Sentiment Analysis with Collaborative AI: Architecture, Predictions, and Deployment Strategies
Chaofeng Zhang, Jia Hou, Xueting Tan, Caijuan Chen, Hiroshi Hashimoto
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on Uncertainty
Teodor Chiaburu, Frank Haußer, Felix Bießmann
Generative AI for Requirements Engineering: A Systematic Literature Review
Haowei Cheng, Jati H. Husen, Sien Reeve Peralta, Bowen Jiang, Nobukazu Yoshioka, Naoyasu Ubayashi, Hironori Washizaki