Analysis of the Importance of Gender Balanced Data Sets for Human Motion Operated Robots

Lena Guinot, Hiroyasu Iwata

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2024 IEEE/SICE International Symposium on System Integration, SII 2024
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1470-1475
ページ数6
ISBN(電子版)9798350312072
DOI
出版ステータスPublished - 2024
イベント2024 IEEE/SICE International Symposium on System Integration, SII 2024 - Ha Long, Viet Nam
継続期間: 2024 1月 82024 1月 11

出版物シリーズ

名前2024 IEEE/SICE International Symposium on System Integration, SII 2024

Conference

Conference2024 IEEE/SICE International Symposium on System Integration, SII 2024
国/地域Viet Nam
CityHa Long
Period24/1/824/1/11

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 制御およびシステム工学
  • 制御と最適化
  • モデリングとシミュレーション
  • 器械工学

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