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
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications
Francesco Cremonesi, Lucia Innocenti, Sebastien Ourselin, Vicky Goh, Michela Antonelli, Marco Lorenzi
HAIFAI: Human-AI Collaboration for Mental Face Reconstruction
Florian Strohm, Mihai Bâce, Andreas Bulling
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