Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911627228479488 |
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| author | Wang, Chenyu Yang, Zhou Lyu, Yunbo Li, Ze Shi Damian, Daniela Lo, David |
| author_facet | Wang, Chenyu Yang, Zhou Lyu, Yunbo Li, Ze Shi Damian, Daniela Lo, David |
| contents | Artificial Intelligence (AI) is now used across nearly every industry, making AI model quality essential for building reliable and trustworthy systems. Historically, correctness has been the main focus, but industry AI models must also satisfy many other important quality attributes. To understand how these attributes are perceived, the challenges they create, and the solutions used in practice, we identify nine key quality attributes and interview 15 AI practitioners from diverse backgrounds. The interviews show that practitioners prioritize attributes differently depending on context. For example, efficiency can matter more than correctness in real-time applications, while scalability and deployability are no longer seen as primary concerns. Data imbalance emerges as a major obstacle to maintaining model correctness and robustness, and practitioners commonly use mitigation strategies such as active learning. We validate our main findings with a survey of 50 practitioners, which shows that most of the findings are widely recognized. These results can help researchers focus on the attributes practitioners value most and avoid improving one attribute at the expense of others that are considered more critical. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16391 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions Wang, Chenyu Yang, Zhou Lyu, Yunbo Li, Ze Shi Damian, Daniela Lo, David Software Engineering Artificial Intelligence (AI) is now used across nearly every industry, making AI model quality essential for building reliable and trustworthy systems. Historically, correctness has been the main focus, but industry AI models must also satisfy many other important quality attributes. To understand how these attributes are perceived, the challenges they create, and the solutions used in practice, we identify nine key quality attributes and interview 15 AI practitioners from diverse backgrounds. The interviews show that practitioners prioritize attributes differently depending on context. For example, efficiency can matter more than correctness in real-time applications, while scalability and deployability are no longer seen as primary concerns. Data imbalance emerges as a major obstacle to maintaining model correctness and robustness, and practitioners commonly use mitigation strategies such as active learning. We validate our main findings with a survey of 50 practitioners, which shows that most of the findings are widely recognized. These results can help researchers focus on the attributes practitioners value most and avoid improving one attribute at the expense of others that are considered more critical. |
| title | Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2402.16391 |