Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability

1 code implementation • 17 Feb 2025 • Simin Zheng, Jared M. Clark, Fatemeh Salboukh, Priscila Silva, Karen da Mata, Fenglian Pan, Jie Min, Jiayi Lian, Caleb B. King, Lance Fiondella, Jian Liu, Xinwei Deng, Yili Hong

To address this gap, this paper focuses on conducting a comprehensive review of available AI reliability data and establishing DR-AIR: a data repository for AI reliability.

Statistical Perspectives on Reliability of Artificial Intelligence Systems

no code implementations • 9 Nov 2021 • Yili Hong, Jiayi Lian, Li Xu, Jie Min, Yueyao Wang, Laura J. Freeman, Xinwei Deng

We also describe recent developments in modeling and analysis of AI reliability and outline statistical research challenges in this area, including out-of-distribution detection, the effect of the training set, adversarial attacks, model accuracy, and uncertainty quantification, and discuss how those topics can be related to AI reliability, with illustrative examples.

Out-of-Distribution Detection Uncertainty Quantification

Investigating the Robustness of Artificial Intelligent Algorithms with Mixture Experiments

no code implementations • 10 Oct 2020 • Jiayi Lian, Laura Freeman, Yili Hong, Xinwei Deng

Artificial intelligent (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including computer vision, autonomous driving, and medical diagnostics.

Autonomous Driving Classification +3