Comprehensive Taxonomy
Comprehensive taxonomies organize complex domains into hierarchical structures, aiming to clarify relationships between concepts and facilitate knowledge discovery and application. Current research focuses on developing and refining taxonomies across diverse fields, including natural language processing, computer vision, and machine learning, often leveraging large language models and advanced algorithms to automate the process and improve accuracy. These efforts are significant because well-structured taxonomies improve the efficiency of research, enhance the interpretability of complex models, and enable the development of more robust and reliable applications in various domains.
Papers
Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review
Catalina Gomez, Sue Min Cho, Shichang Ke, Chien-Ming Huang, Mathias Unberath
A Survey of Federated Unlearning: A Taxonomy, Challenges and Future Directions
Yang Zhao, Jiaxi Yang, Yiling Tao, Lixu Wang, Xiaoxiao Li, Dusit Niyato
General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance
Isaac Triguero, Daniel Molina, Javier Poyatos, Javier Del Ser, Francisco Herrera
Sources of Opacity in Computer Systems: Towards a Comprehensive Taxonomy
Sara Mann, Barnaby Crook, Lena Kästner, Astrid Schomäcker, Timo Speith
Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions
Shahab Saquib Sohail, Faiza Farhat, Yassine Himeur, Mohammad Nadeem, Dag Øivind Madsen, Yashbir Singh, Shadi Atalla, Wathiq Mansoor