Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability
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arXiv
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| Format: | Preprint |
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2025
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| author | Zheng, Simin Clark, Jared M. Salboukh, Fatemeh Silva, Priscila da Mata, Karen Pan, Fenglian Min, Jie Lian, Jiayi King, Caleb B. Fiondella, Lance Liu, Jian Deng, Xinwei Hong, Yili |
| author_facet | Zheng, Simin Clark, Jared M. Salboukh, Fatemeh Silva, Priscila da Mata, Karen Pan, Fenglian Min, Jie Lian, Jiayi King, Caleb B. Fiondella, Lance Liu, Jian Deng, Xinwei Hong, Yili |
| contents | Artificial intelligence (AI) technology and systems have been advancing rapidly. However, ensuring the reliability of these systems is crucial for fostering public confidence in their use. This necessitates the modeling and analysis of reliability data specific to AI systems. A major challenge in AI reliability research, particularly for those in academia, is the lack of readily available AI reliability data. 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. Specifically, we introduce key measurements and data types for assessing AI reliability, along with the methodologies used to collect these data. We also provide a detailed description of the currently available datasets with illustrative examples. Furthermore, we outline the setup of the DR-AIR repository and demonstrate its practical applications. This repository provides easy access to datasets specifically curated for AI reliability research. We believe these efforts will significantly benefit the AI research community by facilitating access to valuable reliability data and promoting collaboration across various academic domains within AI. We conclude our paper with a call to action, encouraging the research community to contribute and share AI reliability data to further advance this critical field of study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12386 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability Zheng, Simin Clark, Jared M. Salboukh, Fatemeh Silva, Priscila da Mata, Karen Pan, Fenglian Min, Jie Lian, Jiayi King, Caleb B. Fiondella, Lance Liu, Jian Deng, Xinwei Hong, Yili Applications Artificial Intelligence Artificial intelligence (AI) technology and systems have been advancing rapidly. However, ensuring the reliability of these systems is crucial for fostering public confidence in their use. This necessitates the modeling and analysis of reliability data specific to AI systems. A major challenge in AI reliability research, particularly for those in academia, is the lack of readily available AI reliability data. 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. Specifically, we introduce key measurements and data types for assessing AI reliability, along with the methodologies used to collect these data. We also provide a detailed description of the currently available datasets with illustrative examples. Furthermore, we outline the setup of the DR-AIR repository and demonstrate its practical applications. This repository provides easy access to datasets specifically curated for AI reliability research. We believe these efforts will significantly benefit the AI research community by facilitating access to valuable reliability data and promoting collaboration across various academic domains within AI. We conclude our paper with a call to action, encouraging the research community to contribute and share AI reliability data to further advance this critical field of study. |
| title | Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability |
| topic | Applications Artificial Intelligence |
| url | https://arxiv.org/abs/2502.12386 |