Abstract
Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perceptions of machines. Humans use scripts around training the machine—a form of behavioral anthropomorphism—to develop social relationships with them. Correcting machine-inferred data labels changes these scripts and evokes self-doubt around who is right, which substantiates the argument that this is a form of human-machine communication.
DOI
10.30658/hmc.6.5
Author ORCID Identifier
Keri K. Stephens: 0000-0002-9526-2331
Anastazja G. Harris: 0000-0003-3756-1453
Amanda Hughes: 0000-0002-7506-3343
Carolyn E. Montagnolo: 0000-0001-9067-943X
Karim Nader: 0000-0003-3571-1796
Tara Tasuji: 0000-0003-1054-5218
Yifan Xu: 0000-0001-5239-4951
Hemant Purohit: 0000-0002-4573-8450
Christopher W. Zobel: 0000-0002-0952-7322
Recommended Citation
Stephens, K. K., Harris, A. G., Hughes, A., Montagnolo, C. E., Nader, K., Stevens. S. A., Tasuji, T., Xu, Y., Purohit, H., & Zobel, C. W. (2023). Human-AI teaming during an ongoing disaster: How scripts around training and feedback reveal this is a form of human-machine communication. Human-Machine Communication, 6, 65-85. https://doi.org/10.30658/hmc.6.5
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