Abstract: As we enter an age of increasingly larger and noisier data, dynamic interplay between human and machine analysis grows ever more important. Researchers and toolbuilders work to better understand and support the analytical process through systems that employ novel visual interactive interfaces along with computational support. These systems leverage the acuity of the human visual system as well as our capacity to understand and reason about complex data, nuanced relationships, and changing situations. In designing and building these systems, we rely on the intuition that the lived experience, perceptual advantage, and adaptability of the human analyst may prove crucial in areas where purely computational analyses fail. Similarly, by pairing the human analyst with a machine collaborator we hope to overcome some of the limitations imposed by the human brain such as limited working memory, bias, and fatigue.
With many promising examples of human-machine collaboration in the literature and everyday life, how do we tell if a new problem would benefit from human-computer collaboration and how should we allocate computational tasks?
Recommended citation: R. Jordan Crouser, Alvitta Ottley, and Remco Chang. Balancing human and machine contributions in human computation systems. In Handbook of Human Computation, pages 615–623. Springer New York, 2013.