تقرير
Detection of Unknown Errors in Human-Centered Systems
| العنوان: | Detection of Unknown Errors in Human-Centered Systems |
|---|---|
| المؤلفون: | Maity, Aranyak, Banerjee, Ayan, Gupta, Sandeep |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Human-Computer Interaction |
| الوصف: | Artificial Intelligence-enabled systems are increasingly being deployed in real-world safety-critical settings involving human participants. It is vital to ensure the safety of such systems and stop the evolution of the system with error before causing harm to human participants. We propose a model-agnostic approach to detecting unknown errors in such human-centered systems without requiring any knowledge about the error signatures. Our approach employs dynamics-induced hybrid recurrent neural networks (DiH-RNN) for constructing physics-based models from operational data, coupled with conformal inference for assessing errors in the underlying model caused by violations of physical laws, thereby facilitating early detection of unknown errors before unsafe shifts in operational data distribution occur. We evaluate our framework on multiple real-world safety critical systems and show that our technique outperforms the existing state-of-the-art in detecting unknown errors. Comment: arXiv admin note: text overlap with arXiv:2309.02603 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.19569 |
| رقم الأكسشن: | edsarx.2407.19569 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |