TY - GEN
T1 - Classifier-Based Analysis of Visual Inspection: Gender Differences in Decision-Making
AU - Heidl, Wolfgang
AU - Thumfart, Stefan
AU - Lughofer, Edwin
AU - Eitzinger, Christian
AU - Klement, Erich
PY - 2010/10
Y1 - 2010/10
N2 - Among manufacturing companies there is a widespread
consensus that women are better suited to perform visual
quality inspection, having higher endurance and making decisions
with better reproducibility. Up to now gender-differences in visual
inspection decision making have not been thoroughly investigated.
We propose a machine learning approach to model male and
female decisions with classifiers and base the analysis of genderdifferences
on the identified model parameters. A study with 50
male and 50 female subjects on a visual inspection task of stylized
die-cast parts revealed significant gender-differences in the miss
rate (p = 0.002), while differences in overall accuracy are not
significant (p = 0.34). On a more detailed level, the application of
classifier models shows gender differences are most prominent in
the judgment of scratch lengths (p = 0.005). Our results suggest,
that gender-differences in visual inspection are significant and
that classifier-based modeling is a promising approach for analysis
of these tasks.
AB - Among manufacturing companies there is a widespread
consensus that women are better suited to perform visual
quality inspection, having higher endurance and making decisions
with better reproducibility. Up to now gender-differences in visual
inspection decision making have not been thoroughly investigated.
We propose a machine learning approach to model male and
female decisions with classifiers and base the analysis of genderdifferences
on the identified model parameters. A study with 50
male and 50 female subjects on a visual inspection task of stylized
die-cast parts revealed significant gender-differences in the miss
rate (p = 0.002), while differences in overall accuracy are not
significant (p = 0.34). On a more detailed level, the application of
classifier models shows gender differences are most prominent in
the judgment of scratch lengths (p = 0.005). Our results suggest,
that gender-differences in visual inspection are significant and
that classifier-based modeling is a promising approach for analysis
of these tasks.
UR - https://www.scopus.com/pages/publications/78751526899
U2 - 10.1109/ICSMC.2010.5642213
DO - 10.1109/ICSMC.2010.5642213
M3 - Conference proceedings
SN - 9781424465880
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 113
EP - 120
BT - Proceedings of the IEEE Conference on Systems, Man and Cybernetics, SMC 2010
A2 - IEEE, null
PB - IEEE Press
ER -
