DOAJ Open Access 2023

Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication

Keri K. Stephens Anastazja G. Harris Amanda L. Hughes Carolyn E. Montagnolo Karim Nader +5 lainnya

DOI

Abstrak

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.

Topik & Kata Kunci

Technology (General) Oral communication. Speech

Penulis (10)

Keri K. Stephens

Anastazja G. Harris

Amanda L. Hughes

Carolyn E. Montagnolo

Karim Nader

S. Ashley Stevensons

Tara Tasuji

Yifan Xu

Hemant Purohit

Christopher W. Zobel

Format Sitasi

APA MLA BibTeX

Stephens, K.K., Harris, A.G., Hughes, A.L., Montagnolo, C.E., Nader, K., Stevensons, S.A. et al. (2023). Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication. https://doi.org/10.30658/hmc.6.5

Akses Cepat

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doi.org/10.30658/hmc.6.5

Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.30658/hmc.6.5
Akses: Open Access ✓