This talk is to present our CSCW 2020
paper "Designing Alternative Representations of Confusion
Matrices to Support Non-Expert Public Understanding
of Algorithm Performance."
My name Hong Shen.
I'm a system scientist at Carnegie Mellon University.
This work is a collaborative effort
with my co-authors Haojian Jin, Alex Cabrera, Adam Perer,
Haiyi Zhu, and Jason Hong.
Imagine you're a journalist who needs to write a story covering
issues of differential treatment of two demographic groups
by a recidivism prediction algorithm in order
to generate informed public debate.
This is exactly what ProPublica had
tried to do in their very influential story "Machine
Bias" back in 2016.
This task is not easy as members from the general public
often lacks a technical background required
to understand the relatively complicated terms
and the performance of the underlying machine learning
models.
So our project is actually set up
towards this rather ambitious goal.
That is, to develop non-expert-oriented toolkit
to support public understanding and debate of AI performance.
In this research, we focused on redesigning confusion matrices
as a first step.
Confusion matrix is a widely used tool
in machine learning to communicate
the performance of a classifier on a set of training or test
data when the ground truth is known.
In this research, we conducted two studies.
Study 1 used a qualitative method to identify challenges
lay people have in understanding standard confusion matrices.
Study 2, we used between-subjects experiment
to evaluate the effectiveness of four alternatives we designed
for this project in terms of objective
and subjective understanding and time costs.
We have also developed a evaluation framework
of objective understanding which includes
three types of questions--
comprehension, comparison, and simulation.
We used those questions in our following studies.
In study 1, we presented our participants with the confusion
matrices showed on this page, used the data
ProPublica collected in 2016 in the context of recidivism
prediction.
We identified two sets of challenges
our participants have.
The first set is, in general, they
are confused about terminologies,
such as false positives.
The second set of challenges, they
are confused about the underlying relationship
between the four types of predictions.
We have further identified a series of challenges here.
They are confused about direction of reading the data.
They are confused about the layered relations.
And they are confused about the quantities involved.
We then conducted a design workshop
to design four different alternatives to tackle
those challenges, one by one.
We used a contextualized confusion
matrices to contextualize all the terminologies.
We used a tree diagram to unpack layered relations.
We used a flowchart to point out the direction
of reading the data.
And then we used a bar chart to clarify the quantity involved.
After the design workshop, we conducted study 2
to run the experiment on MTurk to evaluate
their effectiveness.
In Analysis 1, we compared contextualized
against standard confusion matrices.
In Analysis 2, we compared tree, flow, and bar
against the confusion matrices, always contextualized
terminologies.
Here is our results.
Our Analysis 1 indicates that comparing
contextualized terminologies and standard terminologies
contextualized significantly outperformed standard ones
both in objective understanding and subjective understanding.
Our Analysis 2 further suggested that,
all using contextualized terminologies,
only flowchart outperformed contextualized confusion matrix
in terms of overall objective understanding.
So here are our main findings.
Contextualizing terminologies can significantly
improve users' objective and the subjective understanding.
And the flowcharts that point out
the direction of reading the data
were most useful in improving objective understanding
though its level of subjective understanding
was not improving.
If you are interested, feel free to read
into our paper in the ACM Digital Library.
Thank you.