  Convolutional Neural Networks (CNNs) have been used successfully across a
broad range of areas including data mining, object detection, and in business.
The dominance of CNNs follows a breakthrough by Alex Krizhevsky which showed
improvements by dramatically reducing the error rate obtained in a general
image classification task from 26.2% to 15.4%. In road safety, CNNs have been
applied widely to the detection of traffic signs, obstacle detection, and lane
departure checking. In addition, CNNs have been used in data mining systems
that monitor driving patterns and recommend rest breaks when appropriate. This
paper presents a driver drowsiness detection system and shows that there are
potential social challenges regarding the application of these techniques, by
highlighting problems in detecting dark-skinned driver's faces. This is a
particularly important challenge in African contexts, where there are more
dark-skinned drivers. Unfortunately, publicly available datasets are often
captured in different cultural contexts, and therefore do not cover all
ethnicities, which can lead to false detections or racially biased models. This
work evaluates the performance obtained when training convolutional neural
network models on commonly used driver drowsiness detection datasets and
testing on datasets specifically chosen for broader representation. Results
show that models trained using publicly available datasets suffer extensively
from over-fitting, and can exhibit racial bias, as shown by testing on a more
representative dataset. We propose a novel visualisation technique that can
assist in identifying groups of people where there might be the potential of
discrimination, using Principal Component Analysis (PCA) to produce a grid of
faces sorted by similarity, and combining these with a model accuracy overlay.
