A survey on artificial intelligence assurance
Abstract Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide (and growing) library of algorithms that could be applied for different problems. One important notion for the adoption of AI algorithms int...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2021-04-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-021-00445-7 |
| _version_ | 1818866031253258240 |
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| author | Feras A. Batarseh Laura Freeman Chih-Hao Huang |
| author_facet | Feras A. Batarseh Laura Freeman Chih-Hao Huang |
| author_sort | Feras A. Batarseh |
| collection | DOAJ |
| description | Abstract Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide (and growing) library of algorithms that could be applied for different problems. One important notion for the adoption of AI algorithms into operational decision processes is the concept of assurance. The literature on assurance, unfortunately, conceals its outcomes within a tangled landscape of conflicting approaches, driven by contradicting motivations, assumptions, and intuitions. Accordingly, albeit a rising and novel area, this manuscript provides a systematic review of research works that are relevant to AI assurance, between years 1985 and 2021, and aims to provide a structured alternative to the landscape. A new AI assurance definition is adopted and presented, and assurance methods are contrasted and tabulated. Additionally, a ten-metric scoring system is developed and introduced to evaluate and compare existing methods. Lastly, in this manuscript, we provide foundational insights, discussions, future directions, a roadmap, and applicable recommendations for the development and deployment of AI assurance. |
| first_indexed | 2024-12-19T10:56:58Z |
| format | Article |
| id | doaj.art-70ef07f53cda44c7bed3084532268a3f |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-19T10:56:58Z |
| publishDate | 2021-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-70ef07f53cda44c7bed3084532268a3f2022-12-21T20:24:47ZengSpringerOpenJournal of Big Data2196-11152021-04-018113010.1186/s40537-021-00445-7A survey on artificial intelligence assuranceFeras A. Batarseh0Laura Freeman1Chih-Hao Huang2The Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University (Virginia Tech)Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech)College of Science, George Mason UniversityAbstract Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide (and growing) library of algorithms that could be applied for different problems. One important notion for the adoption of AI algorithms into operational decision processes is the concept of assurance. The literature on assurance, unfortunately, conceals its outcomes within a tangled landscape of conflicting approaches, driven by contradicting motivations, assumptions, and intuitions. Accordingly, albeit a rising and novel area, this manuscript provides a systematic review of research works that are relevant to AI assurance, between years 1985 and 2021, and aims to provide a structured alternative to the landscape. A new AI assurance definition is adopted and presented, and assurance methods are contrasted and tabulated. Additionally, a ten-metric scoring system is developed and introduced to evaluate and compare existing methods. Lastly, in this manuscript, we provide foundational insights, discussions, future directions, a roadmap, and applicable recommendations for the development and deployment of AI assurance.https://doi.org/10.1186/s40537-021-00445-7AI assuranceData EngineeringExplainable AI (XAI)Validation and verification |
| spellingShingle | Feras A. Batarseh Laura Freeman Chih-Hao Huang A survey on artificial intelligence assurance Journal of Big Data AI assurance Data Engineering Explainable AI (XAI) Validation and verification |
| title | A survey on artificial intelligence assurance |
| title_full | A survey on artificial intelligence assurance |
| title_fullStr | A survey on artificial intelligence assurance |
| title_full_unstemmed | A survey on artificial intelligence assurance |
| title_short | A survey on artificial intelligence assurance |
| title_sort | survey on artificial intelligence assurance |
| topic | AI assurance Data Engineering Explainable AI (XAI) Validation and verification |
| url | https://doi.org/10.1186/s40537-021-00445-7 |
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