eBird: A Human / Computer Learning Network to Improve Biodiversity Conservation and Research

eBird is a citizen‐science project that takes advantage of the human observational capacity to identify birds to species, and uses these observations to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a human/computer learning network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both and thereby continually improves the effectiveness of the net‐ work as a whole. In this article we explore how human/computer learning networks can leverage the contributions of human observers and process their contributed data with artificial intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.