Reliability, Resilience and Human Factors Engineering for Trustworthy AI Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908134254051328 |
|---|---|
| author | Mishra, Saurabh Rao, Anand Krishnan, Ramayya Ayyub, Bilal Aria, Amin Zio, Enrico |
| author_facet | Mishra, Saurabh Rao, Anand Krishnan, Ramayya Ayyub, Bilal Aria, Amin Zio, Enrico |
| contents | As AI systems become integral to critical operations across industries and services, ensuring their reliability and safety is essential. We offer a framework that integrates established reliability and resilience engineering principles into AI systems. By applying traditional metrics such as failure rate and Mean Time Between Failures (MTBF) along with resilience engineering and human reliability analysis, we propose an integrate framework to manage AI system performance, and prevent or efficiently recover from failures. Our work adapts classical engineering methods to AI systems and outlines a research agenda for future technical studies. We apply our framework to a real-world AI system, using system status data from platforms such as openAI, to demonstrate its practical applicability. This framework aligns with emerging global standards and regulatory frameworks, providing a methodology to enhance the trustworthiness of AI systems. Our aim is to guide policy, regulation, and the development of reliable, safe, and adaptable AI technologies capable of consistent performance in real-world environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08981 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reliability, Resilience and Human Factors Engineering for Trustworthy AI Systems Mishra, Saurabh Rao, Anand Krishnan, Ramayya Ayyub, Bilal Aria, Amin Zio, Enrico Artificial Intelligence Systems and Control As AI systems become integral to critical operations across industries and services, ensuring their reliability and safety is essential. We offer a framework that integrates established reliability and resilience engineering principles into AI systems. By applying traditional metrics such as failure rate and Mean Time Between Failures (MTBF) along with resilience engineering and human reliability analysis, we propose an integrate framework to manage AI system performance, and prevent or efficiently recover from failures. Our work adapts classical engineering methods to AI systems and outlines a research agenda for future technical studies. We apply our framework to a real-world AI system, using system status data from platforms such as openAI, to demonstrate its practical applicability. This framework aligns with emerging global standards and regulatory frameworks, providing a methodology to enhance the trustworthiness of AI systems. Our aim is to guide policy, regulation, and the development of reliable, safe, and adaptable AI technologies capable of consistent performance in real-world environments. |
| title | Reliability, Resilience and Human Factors Engineering for Trustworthy AI Systems |
| topic | Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2411.08981 |