TY - GEN
T1 - Analysis of the Importance of Gender Balanced Data Sets for Human Motion Operated Robots
AU - Guinot, Lena
AU - Iwata, Hiroyasu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This research investigates the intricacies of women's perceptions and experiences when interacting with a robot trained on imbalanced user motion data, harnessed from wearable Inertial Measurement Unit (IMU) sensors. Utilizing motion data as the primary communication conduit between user and robot, the study provides a multifaceted exploration into human-robot collaboration dynamics. A cross-gender comparison reveals disparities in performance outcomes, highlighting a discernible difference between male and female users. Furthermore, our study delves deep into the stages of trust building, its subsequent violation, and the repair process, offering insights into how these phases distinctly influence women's experiences. A pivotal aspect of our research culminates in unveiling the impacts of awareness. When informed of the robot's biased nature, many women participants exhibited unexpected responses in attributing the subpar performance. These findings illuminate the subtle, yet profound, implications of gender biases in robotic training data sets and underscore the imperative need for balanced and transparent Artificial Intelligence (AI) and robotic systems.
AB - This research investigates the intricacies of women's perceptions and experiences when interacting with a robot trained on imbalanced user motion data, harnessed from wearable Inertial Measurement Unit (IMU) sensors. Utilizing motion data as the primary communication conduit between user and robot, the study provides a multifaceted exploration into human-robot collaboration dynamics. A cross-gender comparison reveals disparities in performance outcomes, highlighting a discernible difference between male and female users. Furthermore, our study delves deep into the stages of trust building, its subsequent violation, and the repair process, offering insights into how these phases distinctly influence women's experiences. A pivotal aspect of our research culminates in unveiling the impacts of awareness. When informed of the robot's biased nature, many women participants exhibited unexpected responses in attributing the subpar performance. These findings illuminate the subtle, yet profound, implications of gender biases in robotic training data sets and underscore the imperative need for balanced and transparent Artificial Intelligence (AI) and robotic systems.
UR - http://www.scopus.com/inward/record.url?scp=85186267105&partnerID=8YFLogxK
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U2 - 10.1109/SII58957.2024.10417085
DO - 10.1109/SII58957.2024.10417085
M3 - Conference contribution
AN - SCOPUS:85186267105
T3 - 2024 IEEE/SICE International Symposium on System Integration, SII 2024
SP - 1470
EP - 1475
BT - 2024 IEEE/SICE International Symposium on System Integration, SII 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE/SICE International Symposium on System Integration, SII 2024
Y2 - 8 January 2024 through 11 January 2024
ER -