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My name is
Shannon Ellis.
I am an assistant teaching professor in HDSI,
so the Halicioglu Data Science Institute
in the Cognitive Science Department.
I focus mainly on teaching
large undergraduate data science
and programming courses to
our wonderful undergraduates here at UC San Diego.
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This question should
be way easier than it actually
is because it's hotly debated in the field.
Everybody has their own definition.
Mine, I try to keep it simple and sweet.
Data science, to me,
is the scientific process of extracting value from data.
That value could be monetary,
but it also could be value in the context
of knowledge or gained information in some way.
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Data science is
relevant now for as many reasons
as you could possibly think of.
We are awash in data.
Our phones are constantly collecting data on us.
Every time we send an email,
that's a whole bunch of data.
So we have more data than we've ever had before,
both on the individual level and at the population level,
just with the way technology
has gone over the past few decades.
Having these data mean that
we can then extract more value.
We can ask more interesting questions.
Think of your favorite field, and there's
a data science question and
a data science approach to answer that question.
Simply by having the data, and having
the technology and the ways
to store the data and process the data,
we're able to ask way
more interesting questions than we used to.
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I do actually have an aha moment.
As an undergraduate, I was a biology major, and I
was working with somebody named
Jeremiah [inaudible] at King's College in Wilkes-Barre,
Pennsylvania, so this tiny liberal arts college.
He and I worked together for four years.
I had done research with him.
He knew I had an interest in genetics.
He knew I wanted to do research.
We worked on these little things called microarrays.
You extract RNA, and then
you put a whole bunch of washes over top of them,
and the end, you get this tiny,
little slide that the colors on it represent
the relative levels of
expression for this thing that we're studying,
which is called Cryptococcus neoformans fungus.
All of that said, I spent
months preparing these slides.
Then we took the slides to a scanner, and we put
them through the scanner, and it took
a picture of my slide,
and that picture was my data.
I had spent months pipetting things in little tubes,
not really seeing what was going on to get this picture.
So I hold a bunch of these pictures and
then there was a software that we
used to analyze the data.
I entered my pictures and out came the results.
I was, like, what just
happened when I clicked that button?
I asked my professor, I said, what's going on?
How did it go from that picture
to those numbers to those results?
He said to me, if you weren't about to graduate,
I would teach you Python.
I thought to myself, note to self,
go Google that when you get back
because I didn't know what Python was.
I Googled Python. I
learned it was a programming language.
At that aha moment, I was, like,
I want to understand
analysis so that I don't
have to trust this piece of software,
even though it might be trustworthy.
I want to understand what's happening in
that black box when I click "Go" to get my results.
At that point I was, like, I'm going
to continue on research because that's what I want
to do at the time, doing
something that I will learn to do computation,
whatever that meant, until I
chose graduate programs where there was
a deep statistical set of
courses and that I would be able to join
a lab where I would learn computation.
That was my aha moment where I was, like,
how did it decide those things for me, after I spent
months, in a second to come up with those answers?
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I sometimes joke
with other people who ask about
my job, and I say it's really
easy because data science is
that hot thing that all the students want to learn.
Nobody is going to come into my class and be, like,
when am I ever going to use this,
because students have this understanding
that these skills are applicable as they go forward.
But in the classroom, it changes a ton.
Students come in thinking and
knowing that they're interested in this concept
of data science without having
a full and deep understanding of what that means.
For example, they come in, and they're like, AI,
machine learning, that's
the hot new thing. I want to do that.
I try to rein them
in a little bit in the classroom, and I'm, like,
the techniques used for
machine learning problems are super interesting.
But let's spend the time talking
about the ethical approaches.
What do you need to think about the data,
are your data complete,
and to really think about all those steps leading up
to using your new favorite machine learning algorithm,
trying to add the context, and
the ethics, and the deep thinking students
need so that they can go on to do
those interesting algorithmic approaches
that they want to do,
and that's what I try to do inside the classroom.
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The specific challenges
for accessing information,
I have a few thoughts, and they're not all tied together.
For the first one, I'm going to put on
my biology hat and my genetics hat,
which is what my background is.
There, if you got government funding in
the United States and wanted to publish your work,
it has been established for a while
that you have to make the data public.
The data are available, and
this is so that others can either
reproduce your work or replicate your work, or
so that others can use the data
in some other way that works for them.
So the data are there,
but often they're in these huge systems.
They're not well annotated,
meaning, you don't know much about
the data or the sample that it came from,
or they're hard to get out. You still
have to do a ton of processing.
They take a ton of space.
There's access issues.
Not that the data aren't there for you to use,
but more it's not a trivial process to actually use them.
I think that's one side of
the spectrum that's actually an easier to access issue.
The other side, in the classroom,
in one of my courses,
we do group projects. I don't do them.
My students do group projects that I ask them to
do, and I have them start with the question.
I tried to tell them, think of a topic that you're
interested in that would help your community,
would help your family, that would help
large society and they can choose whatever they want.
They have to come up with their question
before they go find their data.
Sometimes they come up with
really interesting questions,
but the data are going to be proprietary,
meaning, a company has those data for sure.
You want to analyze
the sentiments people are saying on Twitter over
some period of time and look
at how it corresponds to politics, for example.
Twitter has what's known as an API,
so you can get some data from Twitter.
But if you work inside Twitter,
you would have access to a lot more data.
I'm using Twitter as
an example there for very good reasons.
Companies don't give you access to all of their data,
both for monetary reasons that they
have and for privacy reasons.
I'm not saying it's a problem so much
as the barrier is sometimes you have a really great idea,
the data exist,
but they just don't exist to
you, and so you just won't be able to access them.
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Typically, my work starts with a question.
There's something I want to answer,
and I try to mimic that for students as well
because it can be really
easy to go search on the Internet for
an available dataset and
then form a question around that,
but that's not really what you end up doing at work.
Your company actually is going to be, like, go look at
the data and see what you can ask.
Typically, they're going to say, we have
these benchmarks
we want to ask a question about,
but we want to see why this isn't working
as intended, and you answer that question.
So for me, it starts with a question, and
then it starts with,
do I have the data to answer it,
and simultaneously, do I have
the knowledge background to answer it?
This is something I also try to teach students.
You can ask a really interesting question.
But if you've never thought about that topic before,
you have to do your due diligence.
You need to do background research.
You need to read other publications,
you need to get some knowledge in that area
before you can do any analysis.
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In my own work now, I
mostly ask questions about
the classrooms that I teach in,
and so it's a whole other
can of worms. I have the data,
it's a handful of questions.
One is, what can I use these data for ethically?
A lot of working with the IRB, which is
the Institutional Review Board, which is
a great body that exists at every academic institution,
and so I tell them what I want to do and they
tell me if that's okay and they are experts,
so they are great people to get to know. Then the
second is there's a ton of variability in education.
Every quarter, every term,
I have different students,
so how can I compare this term to
last term when it's not the same people?
What if I want to change up how I
ask one question on a survey,
or clearly my exams aren't
the same from one quarter to another,
so it's not as much as the data accessibility
but what information do
these data have, and what can I use, and
what should I not ask, and what should I not
use, and talking to the experts there as well.
Me asking questions of my students
doesn't put them nearly at
as much risk as like me opening
up someone's skull and doing brain surgery.
Of course, not every biological human subject research
is opening up someone's brain,
but there's less inherent risks.
So there are different procedures that you go through with
the IRB just because the risks are lower.
When I submit things to the IRB,
they want to see the surveys I'm asking.
They want to see what I've communicated to
students about how their data are going
to be used and what those students expectations were.
They're really coming at it with
the lens of protecting students
and student information while
at the same time knowing and just being
realistic that these students are being
put at as much risk
as they could be in other human subjects research.
We do have different paths and different avenues there,
but there they are protecting students, and they're
protecting student's privacy as well,
making sure how are we going to anonymize data?
Is it truly anonymized?
Is something identifiable,
even if you remove their names, for
example? It is a different lens.
It is not as thought of
harm and risk, even though of course they're trying to
protect students as some human subjects research,
but I found the process to
be really thoughtful. Every time
I submitted something and gotten something
back being like, Did you think of this?
How are you handling this? Have you considered
about this identification process from the IRB?
It's just made me more
thoughtful through the process as well.
When I think about data access and data flow;
getting the data sometimes easier than others,
once you have the data and you've
determined that it can answer your question
I try to encourage my students and
myself to do tons of exploratory data analysis.
By that I mean get to know your data deeply.
Who has missing information?
What are the typical values?
Does anybody have a value that looks
way off, and there could have been an error?
If something's missing, is it
missing at random, or is it missing
not at random, and how are we going to consider that?
This is deep. Get to know your data to convince yourself
even further that it is the data
you need to answer that question.
It's a really important part of this data flow.
Sometimes it's circular, where you're,
like, "Do this exploration."
You're like, "These aren't what I need.
I either need to go back and do a different experiment,
or I need to go back and talk to
the company about the data
that they have and this isn't going to
work or a different format."
That process often doesn't
show up in your resulting work.
You don't talk about, in
your report or your paper, all the things that
failed in the middle, and
that's just part of the process,
so I like to talk about that.
You make mistakes, your question is not great,
you didn't get the data you need,
you explore it and realize you didn't have enough,
and you have to go back to the drawing board and
either ask a different question because you're not going
to answer it or get different data to be able to see
the process through to the end where you do
the analysis and you write
the report or you write the paper.
In graduate school I was
analyzing the genetic basis of autism.
I was in a lab at a School of Medicine, and we had
samples from individuals who
are affected with autism and those who were not,
and these were postmortem samples.
So these are individuals who had passed away
and who had donated their brains to science,
and I was analyzing these data.
The closest I got to the autistic population
was looking at this spreadsheet of
information I knew about these people which gave me
very little information about who
they were or even what their "autism" was.
We know it's this huge spectrum.
I wasn't there integrated with the autistic community.
I wasn't asking about their needs.
I wasn't communicating with them.
I'm, like, "If I'm doing this research,
what is it that you, as a community,
feel you want and feel you need?"
I think we're having a reckoning right now that,
that needs to be true, regardless of who you're studying,
or who you're working with,
or who you're developing for.
For example, if you're
developing for the disability community,
individuals with a disability you're trying to
address should be part of
the process from start to finish,
not just answering questions
but probably on your engineering team.
Probably they're in the room as you're doing development,
as you're doing better testing,
as you're launching this into the world.
Even if you think you have the world's
best idea, and you're
going to make this brilliant startup around it,
make sure you've talked to the people who are trying to
help before you come up with this product.
That's what I try to teach my students,
that's what I'm excited for us to do better,
is to make sure those that we're
helping, or innovating for,
or developing for are
just part of the process throughout.
The barriers are somewhat historical,
so we all know the stories of
the largest tech companies that are starting up.
Maybe we don't all know about
the demographics of individuals
who get venture capital funding,
but I can tell you that
the diversity has just not been there.
I don't think it's for malintent necessarily,
I think it was people have
an idea, and they want to innovate on that idea.
People have been willing to fund that,
and they hit the ground, and run with it.
I think part of
the barriers are just awareness, is the first one.
If you're not aware
that you should probably have other people as part of
your team and how that can make
your team and your product better overall,
then you wouldn't think to go
in that direction and you would just hire
your friends who happened to
be people that looked like you in college.
We've had some of that historical pressure.
Another part is economical in that if you
start a startup you have
very few people in the beginning,
and often you're not
making a ton of salary in the beginning.
Only certain people from certain backgrounds
can afford to not make a ton of money for a whole year.
Now sometimes these founders
end up making tons of money down the line,
but you need that ability in the beginning
to take that risk and
obviously that's going to limit
large swaps of our population.
I do teach hundreds and thousands of students each year.
I think that the biggest impact I'll have is
by teaching others how
to consider the ethical implications
of their work throughout their entire process.
That doesn't just mean,
are you allowed to have these data?
It means, should you use these data to ask that question?
Are you protecting individuals
in collecting the data and storing the data?
Are you looking for biases
and the results of your analysis?
Teaching that throughout the entire process
because data is the through line,
I think that's where I'll have
the most important role
in this when it comes to data access,
and teaching that, and giving examples,
and making sure it's a theme throughout my course and
not just 20 minutes in a lecture one day.
That's what it looks like to me,
is continuing to teach this and
demonstrate it to the next-generation.
In my own work, I hope to have
access to the data that I need to
answer the questions that I'm interested in,
but if it's not that data that I
should have, I hope that the other experts in
that field are the ones asking
the questions that I
set out to ask, or I collaborate with them.
I think that secondary though
to making sure that my students
are thinking about this
as they go forward and acting upon it.
This field has moved quickly and even if you
think about Google there's move-quickly-break things.
I'm excited to reign things back,
pull things in, and think deeply.
There is a huge field of
AI ethics and machine learning ethics.
I'm not an ethics expert, and this is not necessarily
where I did my dissertation by any means,
but I'm excited for
those voices to continue to get heard,
and to get more exposure,
and to be valued inside of companies.
I'm painting large spinning broad strokes here.
I have prioritized this ability to innovate,
and embrace new technology,
and learn more about what your preferences are,
so they can give you advertisements.
Companies, and analysts,
and data scientists have found that fun.
I'm excited for us to take a look back, be introspective,
and make sure that those systems
are not harming individuals,
not harming certain populations.
I would say we're at this crossroads
right now where those people exist,
and they are knowledgeable,
and they are speaking out,
but they are not necessarily being listened to
as much as they should be,
and so I'm excited for companies to embrace
these individuals and academics.
They tend to have academic jobs often,
but for both academics and companies to embrace
this idea that we need to think
carefully about the products we put out in the world.
We need to check for bias. We
need to make sure we're not harming
groups or individuals within marginalized groups.
Probably not what most data scientists
would say they're most excited about,
but it is what I'm most excited about.
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