Position: Early-Stage Quality Assurance in Annotation Pipelines Is More Cost-Effective Than Late-Stage Validation
A recent position paper advocates for prioritizing early-stage quality assurance in annotation pipelines over late-stage validation. The authors argue that focusing on when validation occurs can significantly reduce error rates and costs associated with data quality. They propose a taxonomy of validation points and emphasize the need for the machine learning community to address timing in quality assurance practices.
- ▪The paper highlights that early-stage quality assurance can be more cost-effective than late-stage validation in annotation pipelines.
- ▪It points out that only 4% of recent studies report when validation occurs, indicating a gap in current research practices.
- ▪The authors propose three QA trigger points: pre-annotation, post-annotation, and post-review, to improve validation timing.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15714 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Software Engineering arXiv:2605.15714 (cs) [Submitted on 15 May 2026] Title:Position: Early-Stage Quality Assurance in Annotation Pipelines Is More Cost-Effective Than Late-Stage Validation Authors:Sunil Kothari, Sumukha Sharma Thoppanahalli Chandramouli, Naman Khandelwal, Parth Kulshreshtha, Ashi Jain, Kriti Banka, Tanuja Chintada, Venkata Triveni, Gulipalli Praveen Kumar, Manish Mehta, Tao Liu View a PDF of the paper titled Position: Early-Stage Quality Assurance in Annotation Pipelines Is More Cost-Effective Than Late-Stage Validation, by Sunil Kothari and 10 other authors View PDF HTML (experimental) Abstract:This position paper argues that the machine learning community should prioritize early-stage quality assurance in annotation pipelines over the prevailing…
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