Human Correction Costs in Automated Hearing Reports
摘要
A central question for occupational hearing-risk assessment is not simply whether an output is accurate, but whether it survives scrutiny of human oversight. The article develops a research agenda through critical comparison of published evidence. The primary literature contributes a bounded result: the accessible publication describes 7139 initially recruited noise-exposed subjects and longitudinal and external evaluation; baseline 3-kHz and 3–6-kHz measures contribute prognostic information. Oversight is a workflow, not the mere presence of a person. Reviewers need time, authority, and evidence to change an output. Record interventions, reviewer time, and unresolved cases. Compare assisted and unassisted outcomes without crediting the model for decisions that required unreported manual repair. The proposed comparison concerns review queues, correction logs, occupational physicians; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. Implications are restricted to the evidence roles relevant to occupational hearing-risk assessment, leaving broader efficacy and deployment claims unresolved.
参考
Yu, Xiao, et al. “Prediction of Risk of Hearing Loss by Industry Noise from Cross-Sectional and Longitudinal Data.” Communications Medicine, vol. 6, 2026, https://doi.org/10.1038/s43856-026-01463-3.
Li, Yuanhao, et al. “BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models.” arXiv, version 4, 18 Sept. 2026, https://arxiv.org/abs/2605.09134v4. Preprint.
Sang, Yinghao. “Adaptive Quantization Strategies for Robust ML Inference under Distribution Shift.” Proceedings of the 2026 5th International Conference on Cyber Security, Artificial Intelligence and Digital Economy, ACM, 2026, pp. 362–68, https://doi.org/10.1145/3819836.3819892.
National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. National Academies Press, 2019.
Wilkinson, Mark D., et al. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data, vol. 3, 2016, article 160018.
Hernán, Miguel A., and James M. Robins. Causal Inference: What If. Chapman & Hall/CRC, 2020.
Rothman, Kenneth J., Sander Greenland, and Timothy L. Lash. Modern Epidemiology. 3rd ed., Lippincott Williams & Wilkins, 2008.
Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B, vol. 57, no. 1, 1995, pp. 289–300.
Rajkomar, Alvin, Jeffrey Dean, and Isaac Kohane. “Machine Learning in Medicine.” New England Journal of Medicine, vol. 380, 2019, pp. 1347–58.
Wiens, Jenna, et al. “Do No Harm: A Roadmap for Responsible Machine Learning for Health Care.” Nature Medicine, vol. 25, 2019, pp. 1337–40.
Rusu, Andrei A., et al. “Policy Distillation.” arXiv, 2015, arXiv:1511.06295.
Schulman, John, et al. “Proximal Policy Optimization Algorithms.” arXiv, 2017, arXiv:1707.06347.
Tishby, Naftali, Fernando C. Pereira, and William Bialek. “The Information Bottleneck Method.” Proceedings of the 37th Annual Allerton Conference on Communication, Control, and Computing, 1999, pp. 368–77.