Hi, I'm Wencan Zhang, a Ph.D. student from
the Ubicomp lab at the National University of Singapore
directed by Prof. Brian Lim.
Here I'm glad to introduce our new paper at CHI 2022
Towards Relatable Explainable AI with the Perceptual Process.
Hey Wencan, you are sounding fearful. Cheer up.
Well, you're right.
You know, it's my first time to present at CHI.
But how do you know I'm fearful?
Because your speech looks like this
and the highlighted regions reflect fear.
In this example, although the saliency map
with the spectrogram can faithfully explain AI's decision,
this explanation doesn't make sense for end-users
since the information is too technical to understand
and not semantically meaningful.
And the way that model thinks is also not human-like.
All these problems limit the model's interpretability for various real applications.
To mitigate the previous problems, we argue that
explainable AI should be relatable to abstract concepts,
examples that are representative to the concept class
and cues that describe in terms of semantics.
Inspired by the human perceptual process from cognitive psychology,
where given stimuli, we select salient parts,
organize them into cues, and interpret them by
recalling relevant concepts and associated cues,
comparing their similarity to the stimuli and categorizing to a concept.
We propose the XAI perceptual processing framework with corresponding capabilities.
Let's consider a task of speech emotion recognition.
Inspired by the well-known saliency map technique
for image recognition, Grad-CAM,
we use saliency bars to select important word segments
for recognizing an emotion from speech.
Intuitively, unique salient regions are more useful
to distinguish between emotion categories for users,
which means the importance of common salient regions needs to be degraded.
To this end, we introduced a discount factor to mitigate the issue
and compute contrastive saliency with the Hardmard product
between the Grad-CAM and its corresponding discount factor.
We use StarGAN, a generative model
to synthesize realistic counterfactual speech samples by changing from a source emotional style:
"Kids are talking by the door."
into another one: "Kids are talking by the door."
while maintaining other information, such as
the speaker's identity and the lexical content.
Counterfactual can help with user's intuition,
but it can hardly tell the subtle difference between two emotions.
Prior evidence from psychology studies indicates that
vocal cue patterns vary with speech emotional styles,
and people can distinguish emotions by analyzing the relative cue relation.
Inspired by those findings, we derive the vocal cue patterns
from speech clips with a phonetic analysis tool.
By comparing the relative cue relation,
we provide contrastive cues as another explanation
to distinguish between the target sample and its counterfactual.
Bringing all these features together,
we developed the RexNet architecture
to provide multiple relatable explanations, including
contrastive saliency, counterfactual synthetic, and contrastive cues.
To evaluate how well RexNet works and
how it affects user's behavior,
we first conducted a modeling study to demonstrate its performance.
Then in a formative user study, we investigated how users would use relatable explanations,
and finally, with a summative user study,
we evaluated the effectiveness of each explanation.
Here summarize the findings from our modeling study
where the most important one is that
RexNet can improve the primary task performance,
that is the emotion recognition accuracy.
We attribute this performance gain to making the model being aware of vocal cues.
To further evaluate the impact of relatable explanations on end-users,
we developed a user interface under the vocal emotion recognition task.
After playing the voice clip to be judged,
"Dogs are sitting by the door."
The user can view the system recommended prediction,
view or listen to various contrastive explanations.
"Dogs are sitting by the door."
The contrastive saliency bar highlights important words
while the contrastive cues describe in terms of relative cue relation.
At any time, the users can check the contrastive explanations
belonging to different emotion categories to support their decision-making.
"Dogs are sitting by the door."
After going through a tutorial session to learn
how to use various explanations and a corresponding screening test.
The participant was tested with a human-simulatability task
where she needs to listen to the voice clip,
view any explanations depending on the allocated condition,
label emotion in a balls and bins questions,
and finally, read perception questions.
To answer the question of "how usable are relatable explanations",
we conducted an interview in a think-aloud protocol with 14 participants,
who were recruited from a university mailing list.
The result indicates that users consider counterfactual samples more intuitive than others
and users made their decision based on
mental dual processing when vocal queues come in.
We also observed the risk of misinterpreting saliency bars,
maybe too technical for users and
identified the difference in perceptual acuity of cues
between users and the system.
We further conducted a between-subject experiment
with 175 participants recruited from Amazon Mechanical Turk.
to explore the question "How useful are relatable explanations".
We found that all relatable explanations
improved the perceived helpfulness compared with the baseline
and some explanations lead to improved decision correctness.
Surprisingly, combining all explanations together reduces the benefits of decision correctness,
probably due to the weaker coherence between explanations.
To summarize, we contribute an XAI perceptual processing framework
with the RexNet model that can provide multiple relatable explanations,
which is also the first time to provide
human-understandable explanations for audio prediction.
Please check our paper for more details about the approach and experiments.
Thanks for listening and I'm open to questions.