Classifier-Based Analysis of Visual Inspection: Gender Differences in Decision-Making
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the IEEE Conference on Systems, Man and Cybernetics, SMC 2010
Original Kurzfassung:
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.