面向AI辅助生物多样性调查的定向审查：主动连续评分占用模型

中文摘要 AI 辅助

我们越来越多地使用机器学习来标注科学数据集。我们开发和部署的模型在不断改进，但它们目前不完美，而且可能永远不会完美。错误很重要，因为错误会传播到我们的科学理解中，尤其是在系统性偏差的情况下。因此，科学家们非常合理地审查大量机器学习生成的标签，以验证或纠正错误，从而确保其科学发现不受机器学习偏差的影响。在这项工作中，我们专注于帮助科学家根据其科学目标最优地分配审查工作。我们关注一类特定的科学家（生态学家）和一个特定、广泛且有影响力的建模目标（占用模型，该模型根据环境因素估计物种可能出现的区域）。我们引入了主动连续评分占用模型（ACORN），该方法将机器学习预测纳入占用模型，并策略性地选择对下游生态分析信息量最大的样本供专家审查。在相机陷阱和生物声学数据集上，我们的方法恢复了接近完全人工标注数据所得的生态结论，同时所需的专家审查次数远少于非定向审查策略。我们的结果表明，机器学习辅助的科学工作流程应针对下游推断优化专家工作，而非仅针对分类器准确性，尤其是在人工审查预算有限的情况下。我们的代码可在以下网址获取：https URL

英文摘要

We increasingly use machine learning to label scientific datasets. The models we develop and deploy are improving all the time, but they are not and will likely never be perfect. Mistakes matter, as errors can propagate into our scientific understanding, particularly when systematically biased. Very reasonably, scientists thus review substantial proportions of ML-generated labels to verify or correct mistakes in pursuit of ensuring their scientific findings are not biased by ML. In this work, we focus on helping scientists optimally allocate this reviewing effort relative to their scientific goals. We focus on a specific class of scientists (ecologists) and a specific, widespread, and impactful modeling target (occupancy modeling, which estimates where species are likely to occur, conditioned on environmental factors). We introduce Active Continuous-Score Occupancy Modeling (ACORN), a method that incorporates ML predictions into occupancy models and strategically selects samples for expert review that are maximally informative for downstream ecological analysis. Across camera-trap and bioacoustic datasets, our method recovers ecological conclusions close to those obtained from fully human-labeled data, while requiring substantially fewer expert reviews than non-targeted review policies. Our results suggest that ML-assisted scientific workflows should optimize expert effort for downstream inference, rather than for classifier accuracy alone, especially when human review budget is limited. Our code is available at https://github.com/timmh/acorn

发表机构

• MIT（麻省理工学院）
• Google Research（谷歌研究院）
• The Institute for Bird Populations（鸟类种群研究所）

机构由 AI 辅助整理，请以论文原文为准。