hello everyone it is my pleasure to present our late
breaking work on human AI interactive and continuous
sense making a case study of image classification
using scribble attention maps my name is Haifeng Shen
from HilstLab Australian Catholic University
this work is in collaboration with the colleagues from Australian Institute for
Machine Learning at the University of Adelaide and from
Flinders Medical Center in Adelaide Australia
AI-based systems are making massive impacts on our everyday life
these techniques can make superior judgments or predictions
compared to humans on a variety of tasks in the past decade
the focus of AI research has been on algorithmic
advances such as more powerful and efficient machine learning and deep
learning algorithms that possess remarkable understanding
towards logical reasoning behind the solved the tasks
in recent years attention is being paid to studying how humans can effectively
work with AI-based systems this has led to an emerging research area of
human AI interaction a common criticism of AI techniques is
the black box nature of their learning and reasoning
process as they do not provide a reasoning rationale for the output to
humans this has a detrimental effect on human's
trust in the decisions made by AI based systems explainable AI is a hot topic in
both AI and HCI communities in order to open up
the black box to elucidate AI's reasoning process
however human cognition is also a black box to AI
and effective human AI interaction requires unveiling both
black boxes to each other for mutual sense making
if AI's explanation makes sense to the human and the human is able to point
out incorrect reasoning in such a way that
makes sense to AI AI will be able to fine-tune itself for
performance improvement by incorporating human's feedback our
contribution is the human AI interactive and
continuous sense making conceptual framework or HAICS
for supporting effective human AI interaction
and its implementation in image classification application using CNNs
the framework consists of three iterative processes that start with
the human sense making AI through AI's explanation
then proceed with the AI sense making human's feedback on its explanation
and end up with AI fine tuning its model based on human's feedback
in the image classification case study human sense making of
CNN is through heatmap-based attention visualization
in this example the attention on the central part of the ship
provides a correct explanation because the redder region indeed contains
more discriminative features of military vessel
AI sense making of human's feedback on its explanation is through scribble
annotation overlaid onto the attention maps
with a red positive attention mark indicating the image region that network
attention should be at and the green negative attention
indicating the image region that should be ignored by the network
in this example a carrier ship is wrongly classified
as a cargo ship because the attention on the middle part of the vessel is wrong
and the correct attention should be at the rear part of the vessel
we developed a browser-based network attention visualization tool which
allows the user to view visualized attention heat maps
and provide scribble annotations
for human AI mutual sense making this diagram illustrates AI's
fine-tuning process with the human's feedback the top half of
the diagram shows the online attention computation
from inspected network which is then upsampled by interpolation and overlaid
to the input image for presenting to the user for annotation
via the developed browser interface the bottom half of the diagram
illustrates the offline fine tuning process of the same CNN model
using the collected scribble annotation a real world scaphoid fracture dataset
containing nearly 1200 images from 300 patients
is used to validate that AI's performance is significantly improved
after fine tuning the CNNs to incorporate human's feedback
and that the scribble style annotation feedback is more
effective than an alternative bounding box style
feedback
when λ the weighting parameter to control the influence
of the attention regulation loss is between 0.75
and 2 the test accuracy with the scribble style annotation in orange
color is all statistically significantly
improved from the baseline in green color however none of the test accuracy with the
bounding box style annotation in red color
is statistically significantly improved from the baseline
this example shows the image from the
posteroanterior ulnar deviation view with no human feedback the
classification probability of a fracture class is only
25 percent with the bounding box style annotation feedback
the probability is increased to 64 percent
with the scribble style positive and the negative annotation feedback the
probability is increased 84 percent that's all for my presentation thank you
very much