SoCS: Collaborative Research: A Human Computational Approach for Improving Data Quality in Citizen Science Projects

SoCS：协作研究：提高公民科学项目数据质量的人类计算方法

• 批准号: 1209589
• 负责人: Steven Kelling
• 金额: $ 57.54万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1209589&HistoricalAwards=false
• 关键词: SoCS; Collaborative; Research; Human; Computational

A unique interdisciplinary team of computer scientists, information scientists, ornithologists, project managers, and programmers will develop a novel network between machine learning methods and human observational capacity to explore the synergies between mechanical computation and human computation. This is called a Human/Computer Learning Network, and while the focus is to improve data quality in broad-scale citizen-science projects, the network has the potential for wide applicability in a variety of complex problem domains. The core of this network is an active learning feedback loop between machines and humans that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. The Human/Computer Learning Network will leverage the contributions of broad recruitment of human observers and process their contributed data with artificial intelligence algorithms leading to a total computational power far exceeding the sum of their individual parts. This work will use the highly successful eBird citizen-science project as a testbed to develop the Human/Computer Learning Network. eBird engages a global network of volunteers who submit tens of millions of bird observations annually to a central database.This research addresses three fundamental data quality challenges in citizen-science. These are: 1) reducing errors in identification or classification of objects; 2) identifying and quantifying the differences between individual observers; 3) reducing the spatial bias prevalent in many citizen-science projects. To address these challenges, the project will build on advances in artificial intelligence that now provide the opportunity to study systems through the generation of models that can account for enormous complexity. Preliminary work on observer classification will be extended by developing new multi-label machine learning classification algorithms that provide better ecological interpretations and more accurate predictions. In addition, the research will develop new active learning algorithms by constructing sampling paths that will optimize volunteer survey efforts to maximize overall spatial coverage, and incentivize participation via crowdsourcing techniques. Finally, it will study how participants can improve the quality of their observations based on the feedback and information provided by the artificial intelligence. Broad-scale citizen-science projects can recruit extensive networks of volunteers, who act as intelligent and trainable sensors in the environment to gather observations. Artificial intelligence processes can dramatically improve the quality of the observational data that volunteers can provide by filtering inputs based on observers' expertise, a judgment that is based on aggregated historical data. By guiding the observers with immediate feedback on observation accuracy and customization of observation worksheets, the artificial intelligence processes contribute to advancing expertise of the observers, while simultaneously improving the quality of the training data on which the artificial intelligence processes make their decisions. The results of the project will have significant benefit for all citizen science and broader impact in an emerging world of ubiquitous computing in which human-machine partnerships will become increasingly common.

一个由计算机科学家、信息科学家、鸟类学家、项目经理和程序员组成的独特跨学科团队将在机器学习方法和人类观察能力之间开发一个新的网络，以探索机械计算和人类计算之间的协同作用。这被称为人/计算机学习网络，虽然重点是提高大规模公民科学项目的数据质量，但该网络具有广泛适用于各种复杂问题领域的潜力。该网络的核心是机器和人类之间的主动学习反馈回路，可以显著提高两者的质量，从而不断提高整个网络的有效性。人类/计算机学习网络将利用广泛招募的人类观察员的贡献，并使用人工智能算法处理他们提供的数据，从而使总计算能力远远超过其各个部分的总和。这项工作将使用非常成功的eBird公民科学项目作为开发人类/计算机学习网络的试验平台。eBird拥有全球志愿者网络，他们每年向中央数据库提交数千万份鸟类观察结果。这项研究解决了公民科学中的三个基本数据质量挑战。这些是：1）减少物体识别或分类的错误; 2）识别和量化个体观察者之间的差异; 3）减少许多公民科学项目中普遍存在的空间偏见。为了应对这些挑战，该项目将以人工智能的进步为基础，这些进步现在提供了通过生成可以解释巨大复杂性的模型来研究系统的机会。观察者分类的初步工作将通过开发新的多标签机器学习分类算法来扩展，这些算法提供更好的生态解释和更准确的预测。此外，该研究还将通过构建采样路径来开发新的主动学习算法，优化志愿者调查工作，以最大限度地提高整体空间覆盖率，并通过众包技术激励参与。最后，它将研究参与者如何根据人工智能提供的反馈和信息提高观察质量。大规模的公民科学项目可以招募广泛的志愿者网络，他们在环境中充当智能和可训练的传感器，收集观察结果。人工智能流程可以通过根据观察者的专业知识过滤输入，大大提高志愿者可以提供的观察数据的质量，这是一种基于汇总历史数据的判断。通过用关于观察准确性的即时反馈和观察结果的定制来指导观察者，人工智能过程有助于提高观察者的专业知识，同时提高人工智能过程做出决策所依据的训练数据的质量。该项目的结果将对所有公民科学产生重大利益，并在人机合作伙伴关系将变得越来越普遍的无处不在的新兴计算世界中产生更广泛的影响。