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
Generalizing Orthogonalization for Models with Non-Linearities
David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler
From Attack to Defense: Insights into Deep Learning Security Measures in Black-Box Settings
Firuz Juraev, Mohammed Abuhamad, Eric Chan-Tin, George K. Thiruvathukal, Tamer Abuhmed
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha, Avisek Naug, Sahand Ghorbanpour
BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
Haitao Li, Qingyao Ai, Jia Chen, Qian Dong, Zhijing Wu, Yiqun Liu, Chong Chen, Qi Tian
Manipulating Neural Path Planners via Slight Perturbations
Zikang Xiong, Suresh Jagannathan