Decision Tree-Based Analysis Suggests Structural Gender Differences in Visual Inspection
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the IASTED Artificial Intelligence and Applications (AIA) Conference 2011
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 genderdifferences
in visual inspection decision making have not
been thoroughly investigated. We propose a machine learning
approach to model male and female decisions with classification
trees and base the analysis of gender-differences
on the identified model structure. A study with 50 male and
50 female subjects on a visual inspection task of stylized
die-cast parts revealed highly significant structural genderdifferences
(p = 0:00005), in spite of non-significant differences
in overall accuracy (p = 0:34). Going beyond asking
which sex is better at a given ability, our results suggest
that classifier-based modeling is a promising approach to
analyze differences in the structure of cognitive abilities.