hi my name is Yim Register and this is
Amy J
Ko and we're from the University of
Washington Information School.
I'm going to walk you through our work
that we did in the "learning machine
learning" space.
yeah that's a mouthful... it just kind of
stuck and now it's forever
"learning machine learning" in our minds.
So our paper covers how learning machine
learning with personal data
helps the stakeholders ground advocacy
arguments in model mechanics.
Whew! Academic titles are brutal, I know. Feel free to think of it as
"learn ml on your own data and it helps
you stand up for yourself".
so let's start at the beginning. Machine
learning is now part of so many of our
daily activities.
There are some of the more obvious ones
like facial recognition filters,
speech recognition when we're screaming
at ALEXA, or targeted ads. Then there's more subtle
ones, like our Facebook newsfeeds getting
automatically organized,
Google search results getting ranked, and
the machine learning behind
content recommender systems. And then
there are the many cases of machine
learning models being used behind the
scenes to approve people for loans, assess how
likely they are to commit a crime,
or targeted political content
suggestions. Basically,
models with the potential for harm. In
order for everyone to have a voice
in an ML- driven society, us educators
have to be able to teach ML
and enough literacy to help people
affected by the models
to understand what is happening. Let's
check in with various strategies and
research
for teaching machine learning and data
science in general.
Some of you might be familiar with how
machine learning is typically taught in
the computer science department...
Let me just jot down this super super
simple and
obvious equation (sarcasm). Just kidding. How about
introductory machine learning and
statistics and data courses?
Step right up for a fun and exciting
exploration of....
iris plants (disappointed). So many concepts are taught
on these boring, unrelatable data sets
that the learner can't even connect to!
Besides,
Ronald Fisher was a eugenicist anyways.
We have a whole wide world of data...
so maybe it's better to use relevant and
interesting data sets for students to
learn on.
Even better, include them in the process
of curating and exploring that data.
You could use CORGIS data sets, take a look at Abigail Zimmerma-Nefield's work
with athletics and machine learning
models, or listen to the literature and try out
customized data sets to the learner's
domain.
We know from the learning sciences that
experiential learning can be beneficial,
looking at philosophers like Dewey or
Papert.
This work was especially inspired by
something called "funds of knowledge",
work that shows using the cultural
backgrounds and experiences of the
non-white kiddos in the classroom
really helps them feel represented and
draws them into the learning, because they can work off of domains
that are intimately relevant to them.
Okay so all of this implies that the
real magic is that the data was inside
us all along.
I know, very warm and fuzzy. But I'm kind
of not kidding! We carry so many
experiences, domain expertise, cultural
values,
insights, and ideas inside of us. And only
a fraction of those will ever be
represented in the machine learning
classroom.
So what would happen if we included
personal experience when teaching
machine learning?
First of all, would that even work? What
would it look like?
But more importantly, would it help
learners to understand enough of the
machine learning
to be able to speak up for themselves
when models in the world go wrong?
There are lots of sources of personal
data, and in the pilot we even used
learner's own Instagram posts to build a
regression model of "likes" and comments.
For this study, we kept it focused on the
academic context by asking students to
rate their classes on how interesting
they thought they were,
and then reporting the grade that they
got. We had surveyed some novices
about what machine learning concept
they'd like to learn about and it was
basic foundations.
So I built a tutorial called LearnMy
Data that introduced linear regression
and "gradient descent":
the machine learning algorithm that
systematically hones in on the right
parameters for the linear equation that
best fits the data.
We want to know the effects of using
personal data on learners ability to
learn the machine learning mechanisms
and to advocate for themselves, so we set
up a study.
First there was the Facts condition, that
mimicked a brochure or presentation
or [where] a bunch of bullet point statements
are made about linear regression.
This was the bare minimum instruction,
and no, it did not actually take the form
of a brochure that says:
"ML & You! Your Brain on Linear Regression".
Then, there was the Impersonal condition,
where learners used the LearnMyData
tutorial
except it wasn't their own data it was a
hypothetical student's data that they
learned on.
And of course i'm representing a student
here by putting them in a graduation cap
because that's
obviously what we wear to school every
day. (sarcasm) Then there's the Personal condition,
where learners input their own data. For
five classes
rate how interested you were in that
class from 1 to 7,
and then report the grade you got. Then
it gets plotted on a scatter plot.
For most students, their grades increase
with their interest.
Not me though, actually, I do worse in
classes I like...
Don't ask.
We had 51 participants,
pre-screened to be total data science
novices who got randomly assigned to a
condition,
learned about linear regression, and then
filled out a worksheet.
In the worksheet they got to see some
linear model scenarios that they might
encounter in the real world.
They got the chance to critique those
models and write "self-advocacy letters".
More on that
later. So they learned on the example of
interest and grades,
so what if there were some consequences
of a model like that?
Imagine an instructor wanted to help
students who are struggling in the class,
and had students rate their interest at
the beginning of the class.
From last year's data, the instructor can
predict how well each student might do.
If the linear model predicts that the
student will get less than a 75 in the
class,
the instructor reaches out and
intervenes. We don't do this in our
classrooms
(I hope) but it's reasonable enough.
The learner in the study was asked to
write a letter to the instructor
in the situation where the model has
made a mistake.
Next, we tested them on a whole new
scenario to test generalization.
Financial aid offices use models to
determine how much aid families should
get.
The scenario imagined a model where the
number of siblings the student had
increased the amount of aid that the
student received.
They used data from last year from
families who did not appeal the aid
package.
The learner in the study was then asked
to write a letter to the financial aid
office
in the situation where the model has
made a mistake. We can then analyze those
self-advocacy
letters to see how much they understood
about the machine learning mechanisms,
and how well they argued their critiques
of the models.
But hold on... What is self-advocacy
anyways?
This work is inspired from disability
studies, Goodley et al. specifically,
where people like me need to advocate
for our needs within complex systems
that may not understand our disabilities.
Self-advocacy involves knowing your
rights, negotiating where you can,
collaborative problem solving, and
understanding the inner workings
of a harmful system. So these complete
novices
wrote self-advocacy letters for these
scenarios, and we used thematic analysis
and inductive coding to surface the
themes that emerged.
Learners mentioned lots of concepts like:
causality assumptions,
Construct Validity: like what we were
measuring, was that even valid?
Confounds in the setup of the model,
Additional Features you could use,
whether or not the model accounted for
Outliers,
and then Model Performance: how well the
model even fit the data it had.
All of those are indicators that the
learner was paying attention to the
mechanisms of machine learning,
and we can actually count up how many
each learner used
in their self-advocacy arguments. On
average,
learners in the Personal condition used
more of the mechanisms of machine
learning
in their letters. This would be pretty
good for self-advocacy arguments, which
need good articulation of the problem
and understanding of the inner workings
of the harmful system.
And this of course is the "Big Result",
which you
are totally going to read about in the
paper right? (sarcasm)
Okay but even if you just skim it, you'll
get to check out some of the letters
that learners wrote,
and you'll see that the biggest driver
of the difference we see
comes from learners talking about Model
Performance
(or how well the model actually fit the
data it had).
The participants in other conditions
hardly mentioned how well the models fit
the data,
whereas the Personal conditioners were
more critical.
We theorized that the immersion in the
personal data made them pay attention
more to the modeling, and also critique
the problems in a more sophisticated way.
Let's take a look at one of the letters
from the Personal condition.
[reading student letter] "This model is limited in its ability to
"predict student performance in this
class student interest is only one of"
"many factors that influence class
performance."
"Since the model does not take any of
these other factors into account, such as"
"time spent studying,
previous experience with course material,
"or access to learning resources,
it is unreasonable to use it to predict"
"student outcomes.
The university should conduct further"
"research in order to make a more
accurate model that more closely"
"predicts student grades.
Although interest and grade may be"
"correlated, the instructor is not taking
into account how good of a fit the model"
"has to the data.
Given that only one predictor is being"
"taken into account,
my guess is that the fit isn't very good."
"Therefore,
the instructor should not be making"
"decisions that directly affects student
outcomes"
"on an extremely flawed model." [finish reading student letter]. And as you
can see,
we had to learn to read a lot of messy
handwriting.
You'll see more letters like this in the
paper which you're totally going to read (sarcasm),
including the high scoring and low
scoring letters from each condition,
and more. So what are some takeaways?
Give students and stakeholders of models
a place to self-advocate!
That could be letters, Google forms, group
discussions,
post-its on a wall, debates... And involve
the learner and their data!
Allow them to explore real "dirty data"
that makes sense to them.
That can mean letting students do a
project on their own FitBit data,
surveying the class on various topics
and exploring correlations together,
measuring ACTUAL iris flowers by hand,
who knows? I'm sure students will amaze
and surprise you
with their ideas. And for now, I have some
discussion questions for us. [will repeat later]
What are some ways that you can
integrate self-advocacy
into your work? Next, let's do a personal
data brainstorm.
and keep in mind that some data isn't
neutral.
If we were to collect gender statistics
right now all of us non-binary people
could either be outed or targeted!
But for me, the point is still to prepare
people to advocate in the real world,
where that could be a real problem. So
what are some good ideas around
integrating personal data? And what do we
do about the many algorithms we
DON'T know the inner workings of? How can
people advocate for themselves
against those? Thank you very much. It's
been a pleasure to share this work with
you.
And as it turns out... the warm and fuzzies
might have been right.
The real data was inside us all along. *giggle*
