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.
Papers
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
Jianfei Yang, Xiangyu Peng, Kai Wang, Zheng Zhu, Jiashi Feng, Lihua Xie, Yang You
Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models
Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour
Comment on "Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care"
Jakim Berndsen, Derek McHugh
Fault-Tolerant Deep Learning: A Hierarchical Perspective
Cheng Liu, Zhen Gao, Siting Liu, Xuefei Ning, Huawei Li, Xiaowei Li