>> Announcer: Live from
Stanford University.
It's theCUBE, covering Stanford
Women in Data Science 2020,
brought to you by SiliconANGLE Media.
(upbeat music)
>> Hi, and welcome to theCUBE.
I'm your host, Sonia Tagare.
And we're live at Stanford University
covering the fifth annual WiDS
Women in Data Science Conference.
Joining us today is Lucy Bernholz,
who is the Senior Research
Scholar at Stanford University.
Lucy, welcome to theCUBE.
>> Thanks for having me.
>> So you've led the Digital
Civil Society Lab at Stanford
for the past 11 years.
So tell us more about that.
>> Sure, so the Digital Civil
Society Lab actually exists
because we don't think
digital civil society exists.
So let me take that apart for you.
Civil society is that weird
third space outside of markets
and outside of government.
So it's where we associate together,
it's where we as people get together
and do things that help other people
could be the nonprofit sector,
it might be political action,
it might be the eight of
us just getting together
and cleaning up a park
or protesting something we don't like.
So that's civil society.
But what's happened over
the last 30 years really
is that everything we use to do that work
has become dependent on digital systems
and those digital systems, some tier,
I'm talking gadgets, from our phones,
to the infrastructure over
which data is exchanged.
That entire digital system
is built by companies
and surveilled by governments.
So where do we as people
get to go digitally?
Where we could have a
private conversation to say,
"Hey, let's go meet downtown
and protest x and y,
or let's get together
and create an alternative
educational opportunity
'cause we feel our kids are
being overlooked, whatever."
All of that information
that get exchanged,
all of that associating that we might do
in the digital world,
it's all being watched.
It's all being captured (laughs).
And that's a problem because both history
and political science, history
and democracy theory show us
that when there's no space for people
to get together voluntarily,
take collective action,
and do that kind of thinking
and planning and communicating
it just between the people
they want involved in that
when that space no longer
exists, democracies fall.
So the lab exists to try
to recreate that space.
And in order to do that, we
have to first of all recognize
that it's being closed in.
Secondly, we have to make
real technological process,
we need a whole set of different
kind of different digital
devices and norms.
We need different kinds of organizations,
and we need different laws.
So that's what the lab does.
>> And how does ethics play into that.
>> It's all about ethics.
And it's a word I try to avoid actually,
because especially in the tech industry,
I'll be completely blunt here.
It's an empty term.
It means nothing the
companies are using it
to avoid being regulated.
People are trying to talk about ethics,
but they don't want to talk about values.
But you can't do that.
Ethics is a code of practice
built on a set of articulated values.
And if you don't want
to talk about values,
you don't really having
conversation about ethics,
you're not having a
conversation about the choices
you're going to make in
a difficult situation.
You're not having a
conversation over whether
one life is worth 5000 lives
or everybody's lives are equal.
Or if you should shift the
playing field to account
for the millennia of systemic
and structural biases
that have been built into our system.
There's no conversation about ethics,
if you're not talking about
that thing and those things.
As long as we're just
talking about ethics,
we're not talking about anything.
>> And you were actually on
the ethics panel just now.
So tell us a little bit about
what you guys talked about
and what were some highlights.
>> So I think one of the key things
about the ethics panel here
at WiDS this morning was that
first of all started the
day, which is a good sign.
It shouldn't be a separate
topic of discussion.
We need this conversation about values
about what we're trying to build for,
who we're trying to protect,
how we're trying to recognize
individual human agency
that has to be built in
throughout data science.
So it's a good start to
have a panel about it,
the beginning of the conference,
but I'm hopeful that the
rest of the conversation
will not leave it behind.
We talked about the fact that
just as civil society is now
dependent on these digital
systems that it doesn't control.
Data scientists are building data sets
and algorithmic forms of analysis,
that are both of those two things
are just coated sets of values.
And if you try to have a
conversation about that,
at just the math level,
you're going to miss the social level,
you're going to miss the
fact that that's humanity
you're talking about.
So it needs to really be
integrated throughout the process.
Talking about the values of
what you're manipulating,
and the values of the world
that you're releasing these tools into.
>> And what are some key
issues today regarding ethics
and data science?
And what are some solutions?
>> So I mean, this is the Women
and Data Science Conference
that happens because five
years ago or whenever it was,
the organizers realize,
"Hey, women are really
underrepresented in data science
and maybe we should do
something about that."
That's true across the board.
It's great to see hundreds of women here
and around the world
participating in the live stream, right?
But as women, we need to make sure that
as you're thinking about, again,
the data and the algorithm,
the data and the analysis
that we're thinking about
all of the people, all of the
different kinds of people,
all of the different kinds of languages,
all of the different abilities,
all of the different races,
languages, ages, you name
it that are represented in
that data set and understand
those people in context.
In your data set, they may look like
they're just two different points of data.
But in the world writ large,
we know perfectly well
that women of color face
a different environment
than white men, right?
They don't work, walk through
the world in the same way.
And it's ridiculous to assume
that your shopping algorithm
isn't going to affect that difference
that they experience to the real world
that isn't going to
affect that in some way.
It's fantasy, to imagine that
is not going to work that way.
So we need different kinds of people
involved in creating the algorithms,
different kinds of people
in power in the companies
who can say we shouldn't build
that, we shouldn't use it.
We need a different set
of teaching mechanisms
where people are actually trained to
consider from the beginning,
what's the intended positive,
what's the intended negative,
and what is some likely negatives,
and then decide how far
they go down that path?
>> Right and we actually had on
Dr. Rumman Chowdhury, from Accenture.
And she's really big in data ethics.
And she brought up the idea
that just because we can
doesn't mean that we should.
So can you elaborate more on that?
>> Yeah well, just because we
can analyze massive datasets
and possibly make some
kind of mathematical model
that based on a set of
value statements might say,
this person is more
likely to get this disease
or this person is more
likely to excel in school
in this dynamic or this
person's more likely
to commit a crime.
Those are human experiences.
And while analyzing large data sets,
that in the best scenario might actually
take into account the societal creation
that those actual people are living in.
Trying to extract that kind of analysis
from that social setting,
first of all is absurd.
Second of all, it's going to accelerate
the existing systemic problems.
So you've got to use that
kind of calculation over
just because we could
maybe do some things faster
or with larger numbers,
are the externalities
that are going to be caused
by doing it that way,
the actual harm to living human beings?
Or should those just be ignored,
just so you can meet
your shipping deadline?
Because if we expanded our
time horizon a little bit,
if you expand your time horizon
and look at some of the big
companies out there now,
they're now facing those externalities,
and they're doing
everything they possibly can
to pretend that they didn't create them.
And that loop needs to be shortened,
so that you can actually sit down at
some way through the
process before you release
some of these things and
say, in the short term,
it might look like we'd make x profit,
but spread out that time
horizon I don't know two x.
And you face an election
and the world's largest,
longest lasting, stable democracy
that people are losing faith in.
Set up the right price to pay
for a single company to meet
its quarterly profit goals?
I don't think so.
So we need to reconnect
those externalities
back to the processes
and the organizations
that are causing those larger problems.
>> Because essentially,
having externalities
just means that your data is biased.
>> Data are biased, data
about people are biased
because people collect the data.
There's this idea that there's
some magic debias data set
is science fiction.
It doesn't exist.
It certainly doesn't exist
for more than two purposes, right?
If we could, and I don't
think we can debias a data set
to then create an algorithm to do A,
that same data set is
not going to be debiased
for creating algorithm B.
Humans are biased.
Let's get past this idea
that we can strip that bias
out of human created tools.
What we're doing is we're
embedding them in systems
that accelerate them and expand them,
they make them worse (laughs) right?
They make them worse.
So I'd spend a whole
lot of time figuring out
how to improve the systems and structures
that we've already
encoded with those biases.
And using that then to try
to inform the data science
we're going about, in my opinion,
we're going about this backwards.
We're building the biases
into the data science,
and then exporting those
tools into bias systems.
And guess what problems are getting worse.
That so let's stop doing that (laughs).
>> Thank you so much
for your insight Lucy.
Thank you for being on theCUBE.
>> Oh, thanks for having me.
>> I'm Sonia Tagare, thanks
for watching theCUBE.
Stay tuned for more.
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