Black Box
"Black box" refers to systems whose internal workings are opaque, hindering understanding and analysis. Current research focuses on methods to analyze and mitigate the limitations of black-box models, particularly deep neural networks, across diverse applications like code generation, robot design, and autonomous systems. Key approaches involve developing surrogate models, employing novel optimization techniques, and designing explainable AI (XAI) methods to enhance interpretability and trustworthiness. This research is crucial for ensuring the safety, reliability, and fairness of increasingly prevalent AI systems in various fields.
273papers
Papers - Page 5
July 19, 2024
The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments
Shivansh Sharma, Mathew Huang, Sanat Nair, Alan Wen, Christina Petlowany, Juston Moore, Selma Wanna, Mitch PryorFuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer
Tiago Dias, Eva Maia, Isabel Praça
July 18, 2024
Semantic Prototypes: Enhancing Transparency Without Black Boxes
Orfeas Menis-Mastromichalakis, Giorgos Filandrianos, Jason Liartis, Edmund Dervakos, Giorgos StamouBlack-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models
Zhuo Chen, Jiawei Liu, Haotan Liu, Qikai Cheng, Fan Zhang, Wei Lu, Xiaozhong Liu
July 2, 2024
June 7, 2024
June 5, 2024
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems
Wei-Ting Tang, Ankush Chakrabarty, Joel A. PaulsonAlignment Calibration: Machine Unlearning for Contrastive Learning under Auditing
Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao