Abstract
Machines have a plethora of applications in spaces dangerous, hostile, or high-risk to humans. Given such applications, the fields of human-machine teaming and cognitive engineering, while nascent, are developing at a staggering rate. At the heart of these disciplines is a doctrine espousing an agent-centric concept based on common properties shared between machines and human operators. Symbiotically, the goal is to ensure machine and human mutually benefit from the teaming. Yet, the literature has been fixated on system design and machine-agent optimization for performance. Thus, the literature appears to be biased as it does not consider the cognitive requirements of the human agent. The problem then, is that recommendations from the literature focus on changing the machine to instigate less demanding human participation rather than adapting the human to induce optimal machine participation. For this reason, this work examined what characteristics of human-machine teaming literature demonstrate a statistically significant relationship with the category of focus in the same research. The characteristics-as-variables included author discipline, count of publications in the field, author affiliation, gender, and country of origin. A multinomial regression revealed a significant relationship with focus on the machine element as opposed to the human element in human-machine pairing in the cognitive engineering literature. Furthermore, author discipline, affiliation, and country of origin, demonstrated a significant bias effect towards the machine element in human-machine pairing literature.
Recommended Citation
Pittman, Jason M. and Crosby, Courtney
(2024)
"Bias in Cognitive Engineering for Human Machine Teaming Literature,"
American Journal of Science & Engineering (AJSE): Vol. 2:
Iss.
3, Article 3.
Available at:
https://research.smartsociety.org/ajse/vol2/iss3/3