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

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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.