when we go
ahead and get started- let me
call the order the Senate
select committee on the growing
impact of artificial
intelligence in California.
Today's hearing is focused on
applications and impacts for
law enforcement in criminal
justice and we're also pleased
to help up the presiding judge
the Orange County Superior
Court here judge not camara
exit talk a little bit about
other interests with respect to
artificial intelligence- we
have here councilmember Jordan
brand men and account number
brand along of the- some women
number X. office. Councilmember
Brandon a direct. Thank you.
Anaheim sport here in assisting
us with this. Just so all of
you know- this is being live
streamed up throughout up to
the Kaplan throughout the state
M. I'm sure that there are
millions and millions watching
some just letting you know that
before you you testified that
we're competing with the
impeachment hearings and I'm
I'm sure many are just
switching back and forth. So.
Councilmember Benjamin thank
you for being here. Thank you
Mister chairman and good
morning everyone welcome to the
city of Anaheim first off and
one I think a select committee
chair senator Tom Umberg and
the select committee members
for bringing this important
discussion to our city and
allowing our community. The
opportunity to be part of this
dialogue California is the
birthplace of some of our
nation's greatest technological
advances and I am glad to see
that our leaders have
acknowledged this and are
taking steps your research and
determine the best practices
for working with ever changing
medium. Our justice system.
Access data and technological
tools is greater than ever
before. Law enforcement's
ability to access this
continues to grow. It is
important that we are mindful
of this and how these new
mediums can positively impact
the ability of law enforcement
to uphold the law while also
being respectful of our
residents right to privacy. I
am encouraged Mister chairman
by today's meeting of our state
leaders and I know California
is well on its way tackling
these important questions thank
you again for allowing me to
welcome you to our city. And
for us to host you to. Thank
you cal men and that. Then of
making this hearing possible I
want to thank also the staff
that's here- both my staff sue
Chang staff in other staff from
Sacramento it it is a- an
operation to hold a hearing
sort of off campus and I'm
grateful for all of you for
assisting. Assess thank you
Senate opening Chang is here
with us today member the select
committee. Appreciate your
support pressure you being
here- let me just make a few
opening remarks a maternal over
to senator Chang- today we're
gonna be discussing current and
future impact of artificial
intelligence on federal state
and local law enforcement as it
relates to- possible predictive
policing- and policies. As well
as current future uses of our
intelligence in the judicial
system as it relates to
criminal justice and the
administration of justice with
us here today. Retired special
agent Claude Arnold from home
and security investigations.
Thank you Mister Arnold for
being here also- surgeon
Krister McDonald Orange County
sheriff's department crime
analysis unit. Thank you Sir
Jim McDonald- the presiding
judge the Orange County
Superior Court judge Kirk not
camara. Judge we're grateful
for your presence as well- and-
former state senator Joe don
who's hiding- all over in the
first- so to speak. Senator
done here is gonna talk is
special currently special
adviser. To the dean at UCLA
law school about of initiative
at UC law school to advance our
social intelligence you're in
California. I won't turn over
now to Senate change if you've
got any opening remarks
searching. Absolutely thank you.
Thank you so much C. chairman
number for convening this
hearing I'm really excited to
be here and I'm glad that he
was able to work with the two
days in our on our schedule- I
am also a founding member of
the tech innovation caucus so
truly is an honor for me here-
I am excited that artificial
intelligence is getting so much
attention in the legislature
and that we do have a clearing
house for information in
hearings. This newly formed
come. Yeah changing everything
we do and we have to pay
attention. Today we'll hear
about law enforcement but a I
also transforming the landscape
of transportation healthcare.
And how governments function. I
authored legislation this year
after year as did other members
of the legislature to leverage
a I. for the benefit of state
government and bill protections
against. Potential harms a I
create. The governor made clear
this year he wants little
legislative regulation around
the space at least for now. He
believes his the he is the work
Michigan is joining inception
to tackle the challenges ahead
of us so I hope that's true but
I tend to believe we will need
to solidify and copy
protection. Around it in the
coming years it's clear their
husband. Is with. Buy low and
for and it is our job to make
sure the benefits are not
drowned out. These negative
consequences like proliferating
bias through a I. job reduction
in breaching privacy. I'm
excited to hear from testimony.
The committee has brought
together some very exciting
experts so thank you so much
for being here and I look for
to our conversation. Thank you.
Thank you Sir John- we are
going to have a public comment
after we have the panelists-
testify in and respond to some
of our enquiries- we may have
depend upon the number of folks
who want to provide public
comment- we may. Have to limit
it to number of minutes we'll
we'll take that this. Later on
so let let let us begin first
of let me call up. Recent
retired special agent in charge
of security investigations here
in Los Angeles- special agent
Claude Arnold special agent
Arnold of first let me begin
with the politically incorrect
comic you look way too young to
been retired but- so. Been
retired for four years after
serving for twenty seven years
okay well listen up double down
on the counter so. Much for
having me US senators on Bergen
Chang- happy here to speak to
the committee a little bit
about- my experience and
understanding- with the use of
artificial intelligence
federally. And specifically
with of my former agency-
homeland security
investigations which is a
component of US immigration and
customs enforcement. I'll say
at the outset that I am not an
artificial intelligence expert.
So you know I can share my
experiences but I certainly
don't understand technically
how works I know how to
strategically and tactically
and I'm gonna give some
examples this morning. Of how
that is done. How would it's
been very effective as I've
experience throughout my career
so I served a brief background
on myself I ice last served as
a special agent in charge for
almond security investigations
here in Los Angeles for the
last for five years until my
retirement at the end of twenty
fifteen. Prior to that I was
especially been in charge in a
Minneapolis Minnesota and I
area of responsibility was a
five state region there in the
Midwest- I served in senior
positions in headquarters and
then you know my career it is
with us fads I. Across the U.
S. as I promoted transferred
Midwest south west east coast.
So yeah you know artificial
intelligence but by definition
it's just the development of
computer systems are able to
perform tasks that normally
require human intelligence it
you know in its simplest form a
one example that I like to cite
an an early example of
artificial intelligence is
license plate readers so-
probably in the nineties- mid
to late nineties the federal
government started using the
license plate read. That
automatically a recorded
license plates and then ran
those license placed against
various federal and other
databases to identify potential
violations of law. You know in
in my- world we used it to
identify drug smugglers- human
traffickers. People involved in
with in crime with the nexus to
the border. So even though you
know someone at near or in
quarters at or near the ports
of entry- license would be
license plates would be
recorded and then they would be
run across databases okay this
person may be headed south
they're connected to a case
this vehicle with drug
smuggling and they may have. B.
smuggling money southbound so
when they the port of entry-
you we wanted we want to do a
border search on that vehicle
or maybe we want the border
patrol over or in one of our
local because they make. Going
proceeds extra. But or weapons
thing like. So that that's an
early example of artificial
intelligence- but you know.
Artificial intelligence is you
several ways and law
enforcement it can be used for
crime analysis so. Cried and
analysis geographic Belle says.
Data analysis- network analysis
tell of on a record analysis
you know good example of this
is- phone link analysis and
this is something that started
probably in the late eighties
early nineties where in
connection with an
investigation you have a bunch
of phone numbers either
connected to a- an intercept of
phone intercept court ordered
phone intercepts or or just a-
pen registers what we call it
just recording phone numbers
from people who you think are
involved in criminal activity.
Well you link these phone
numbers to kind of create a
picture of what the criminal
activity is and who the players
are in the criminal activity
very early example of using a
computer software to do the
linkage instead of doing it on
a white board. It's a lot more.
At the end another great
example of artificial
intelligence that that is an
early example is strategic
analysis- so. You know this is
strategic targeting for example
I think one of the best
examples of this is Los Angeles
police department's compstat a
lot of departments and agencies
have employed that. That
strategy and that's where you
use. Computers to compile crime
statistics. Identify potential
threat areas so that you can
dedicate resources. Areas where
there's an emerging from all
threats. So I a you know given
some examples of. Early
artificial intelligence I want
to give you a couple more
examples. So get out of bases
another one and early on in my
career I was assigned to the
violent gang task force. The
day cold a lot of data about.
Many documented gang members
and they allow investigators to
make connections when there's a
crime between people identify
potential suspects they have
bio metrics in them
photographs- and where there's
photo recognition capability-
you can identify suspects
through a photo so- I can give
an example where I was.
Inducting investigation of
taking case this was in the
nineties and I was in Phoenix
Arizona agent and this was
ground zero for- human
smuggling and trafficking in
the whole U. S. and so street
gangs were- engaging and
hostage taking taking people
hostage. And then they could
hold them for their human
smuggling fee that still M.
from human smuggling
organizations. With guns in
their be shoot outs on our
people caught in the middle
people get murdered at center
was horrible- but so I had a
case where street gang head-
head of doc did a group of
Ecuadorian nationals who had
been smuggled into the U. S. at
gun point. And when we can get
a search warrant locate. We a
photograph of a person bill
didn't know who they were this
is ninety two thousand so the
technology even existed them.
But of the Phoenix police
department at the time was
using photo recognition
software and we're able to run
that through their databases
and we were able to identify
one of the gang members who was
involved. By this photograph
who previously was not
identified. So that's another
early example of. Artificial
intelligence so how does- H. a
sigh use- artificial
intelligence now well. Probably
the biggest way of that not
only H. aside but all federal
agencies use artificial
intelligence is through the
compilation of disparate data
right so there's many databases
both federal and public
database. That have a lot of
information. But they're
separate databases and so in
the old days- an investigator.
Who that was investigating
let's say a specific individual
as part of an organization
whatever they would have to go
run this person in this
database on the person this
database or you know to try and
find out as much information
about that target or even
organization. And that take
time and then that person so
would have trying to establish
connections between multiple
subjects of say a criminal
organization. From all these
disparate data I mean you can
see how that is very time
consuming labor intensive and
requires quite frankly. The
limitations of the investigator
that the human being. So now
what's that there's these their
systems that you know so all
this data is for and again I'm
not computer scientist I'm not
in a I. expert but- basically
all this data. Is put into a
central location and there's a
system that. Compiles at all
and makes connections linkages
you know the proverbial six
degrees of separation- and that
so this can be used and
criminal investigations in
terrorism investigations. In
child exploitation
investigations etcetera so bad
is. Probably currently how
artificial intelligence.
Currently is most used by both
homeland security
investigations and other
federal agents. A couple
examples- so after right after
nine eleven I know H. a site.
Developed you know through
contractors obviously a system
that did Josh this and the
purpose of it was specifically
for. To contribute to terrorism
investigations and terrorism
travel right because DHS has
data on how people travel but
then you take other data and
you make you know suspected
terrorists the different watch
lists. And travel patterns et
cetera and you you can make
these. Connections to people
where the human being in human
investigator might not have
made that connection because
there are several degrees of
separation but now with the use
of artificial intelligence and
these are computer programs
that can be done and you can
identify a potential threat.
Whereas previously that threat
may not have been identified
it's also used for example
child X. Isshin investigations-
there are certain. First of all
I you know H. a size of federal
agency that probably conducts
more child **** and child
exploitation destinations than
any other federal agency so-
they become very adept at doing
so and leveraging technology to
facilitate that so for example.
You know child sex tourism it's
a federal violation to travel
internationally obviously. To
go foreign and engaging in sex
act with with a child a but
it's a problem so how how do
you solve this problem well.
You can use our an artificial
intelligence is used to look at
people registered sex offenders
at center all this data from
disparate databases travel
patterns to identify potential
threats who may be traveling.
To engage in child sex tourism.
So that of foreign governments
can be notified. And perhaps
prevent- a child being
victimized and it happens all
the time ice has a program
called angel watch and they
just opened the center. It
ribbon cutting last week and
it's multiple federal agencies
and that the purpose is just
that to identify people who
they're intent may be to travel
to engage in child sex tourism
and hopefully. Prevent that
from happening. Another
examples financial
investigations every agency not
just federal agents every
agency. That conducts criminal
designations has a component of
which is a financial
investigation follow the money
so again it would require an
investigator and analysts to go
through lots of different data
things and try and make a
connection whereas using
artificial intelligence
computer programs. You can.
Detect anomalies in financial
activity- for example
structuring right someone
making deposits repetitively
just under the federal
reporting requirement of ten
thousand dollars- if you have
computer systems doing that.
You can identify a pattern of
potential criminal behavior and
identify a larger criminal
organization. I think that. In
the future going forward. What
one of the best applications
for artificial intelligence is
that. What the nine eleven
commission. One of the
recommendations was to unify
the many participants in the
counterterrorism effort and
their knowledge and their
knowledge in a network based
information stern system that
transcends traditional
government boundaries because
what we learned after nine
eleven we learn that. Even
federal agencies work talk you
know these turf battles
certainly they were sharing
information with state and
local governments and we found
that that still has been a
problem I that will that will.
That that may have. The Boston
Marathon bombing right. It it's
all due to a lack of sharing of
information so. Artificial
intelligence can occur bush. Is
our facilitate making these
connections and sharing his
informations across
jurisdictional boundaries right
and that would protect us all
from- potential public safety
or national security threats
course. The big thing is it has
to be done within the legal
framework and in a way. That
that doesn't violate people's
privacy rights. I hope I didn't.
Go over thank you no thank you
special agent Arnold- before
you with homeland security what
agency were you with. So my
career started with the I. N.
S. yeah. In this is something
I'm gonna hope I can perhaps
tap into each of the experts
here with respect artificial.
All of the concerns with our
intelligence is that it such
that it actually replaces human
beings in terms of decision
making and what the
implications are whether that
that there is some sort of
implicit bias for example
that's incorporated into
whatever design. Is created to
have that algorithm actually
make decisions so for example
decisions as to where resources
should be decisions as to- for
example who should be who
should be a fight as a suspect-
one of the issues that we're
faced is facial recognition
technology in Sacramento. So in
terms of the future- special
Arnold are there any concerns
that you have about the future
of artificial legends and how
it may at the river. Either
humans into decision making or
how there may be some sort of
implicit bias that that may
lead to stone- pass the two
questions. Yes so I don't think
you ever be able to remove the
human element from the decision
making. Process whether on the
macro or or micro level so for
example just on the
investigator level you may have
one of these systems. Create
some language linkages and
identify potential- a potential
target and maybe someone who's
involved in criminal activity
but then that person is still
going to have to present that
case to a local prosecutor
they're still going to have to.
You know on an affidavit
articulate probable cause for
some of legal action against a
target you a I will ever be
able to do that for the person
they're not gonna be able just
to swap out an affidavit from
front. Page so. I'm I think
that. Now could still be
misused. Yeah but but anyone
Forsman tool or any law
enforcement authority can be
abused and misused so it's a
incumbent upon law enforcement
agencies to have policies in
place. You know M. most of them
already do- that that when
authority is abuse or misuse
that- people are disciplined
and- you know so others are
deterred from doing that as
well I think that the in the
future- artificial intelligence
actually could create the
affidavit obviously you can M.
machine swear to it but
actually could created. An
affidavit to be submitted to a
judge shouldn't one would hope
that the Jeff is not a
computer- but the anyway that
that that's a concern so thank
you- Senate check if questions.
Comments. We're talking about
homeland security I have a
really quick question- are you
aware of how a I've been using
immigration and refugee. With
Roth G. system if. There's any
currently. Well the refugee
system is at and the G. to
catia refugee applications is
not done by my former agency.
You know violations of the law
relating to that are
investigated by my former
agency so yeah I'm not aware
of. Any he- current application
of a I. in relation to the
refugee system. Thank you US
special agent Arnold let's now
turn to US Surgeon McDonald and
certainly bottled up would ask
you some of the same questions-
I do think that in the future
that- there will be the
creation of affidavits I think
that in terms of judiciary
there probably will be program
lease that that they can be.
They can be braided or that
those affidavits to be plugged
in. And analysis as to whether
or not there is. Probable cause
such that that I the machine
can machine maybe. Not the
right term but that the system
can't can generate an answer or
deficiencies- that that may
exist. In Andy affidavits- so
let me turn to sergeant
McDonald serving Donald. Thank
you here's. So first of all
thank you very much for having
me today I really appreciate-
taking the time to listen to
Mike to kind of my incite over
I can provide that to you. A
little bit as to how we're at
on the state local side- it's
just a little bit about me I
come from a little bit of a bit
different background- I work
for the Orange County sheriff's
department. Been there twenty
years now. And I've had my
range of experience Lara forced
starting in the John working
patrol and after I went to a-
she went. To work with the
joint person task force with
the FBI so. The topics are in
talk about I have experience on
the federal side as well
working through Faiza data and
that larger. Data collection if
you will- and then more
recently I was promoted
sergeant- and put in charge of
a crime analysis detail so now
I'm looking at- the difference
our collection systems-
different data collection that
we have and how. To apply that
not necessarily to national
security or
counterintelligence. Threat but
more so on the local a crime
level looking at solving a
soldier or perhaps. So. My
despair back a little bit about
personally for me though I'm a
very private person- Leyva live
streaming this for me is a good
step in the- you know like.
Hello world I'm here I'm
definitely not on social media
at this there are people are
you right exactly yeah well
you're welcome that's the
thermic right. So you know you
will not find online at a lot
of. Measures in my personal
life to remove myself. From
that kind digital footprint if
you will. First because of my
previous assignment I didn't
want anybody else knowing where
I lived or what I did. So I'm-
I'm kind of in a in a unique
situation that. Here I am
trying to find clues that
people left but at the same
time remove myself from those
very same system I used. To
find people soft right. Sir a
little bit different let me.
And as I go through this
hopefully I'll give you a good
pictures to what we're using
currently in I. a I and we're
we're kind of I think we're
headed and then probably some
concerns that I have on that
I'm really private side- so
currently at in a minute Nancy
and at my local you know.
Southern California peer agency.
Probably the away is-
government data solutions is
typically pretty slow to
develop and evolve and I think
we're really struggling even in
my own agency to come up with
data real time little on
predictive- it- now looking at
our our crime analysis unit and
what it is that we're doing the
steps that were king I think
we're getting a lot closer to
get. Real time data. I will
visit department recently went
paperless with the reporting.
Five years ago- so if you look
at how it is we're we're coming
along there's a lot to take in
a in a very short amount of
time and I think that you know
as we look at it and
implementing these new. Data
forms would really need to take
a good strategic look and see
where we want to be and how is
it we want to use this data and
for. Future growth so.
Currently in NY who kind of
sphere of influence that the
systems that we're using right
now. Are predominately facial
recognition- I will tell you
that- you know the systems that
we use are. Are reactive it's
not like we have a passive
system. I. with government
counters cameras mounted up
places actively. Getting you
know photographs of everybody
in in the community. It's
typically we have our our data
set of photographs we have from
jail booking photos or things
like that. And compare it with.
Hey we have crime we have this
you know ring. Bell or you
have. You know stores CZ.
Camera we have a still image of
that. Roth that was reference.
What are known suspect please.
Yeah Joel book yes. A lot of
predictive analysis yet. The
recognition- you know I could
see at some point you know it
in twenty thirty years from now
you have a camera mounted and
you have photographs of
everybody that has an active
warrant and it automatically
scans the entire community.
Points the people elected
boards I don't I don't think
we're definitely not there now
and I don't think we would get
there in the future as well I
think that's a little too. A
public consumption- and what
we- and I think that I would
leave that up to you guys to.
Kind of stick a strategic look
and see how that. How that's
going to vault over time. So a
couple other kind of points to
make it in in some kind of the
A. I. F. today what we're
talking about my colleague
mentioned- the license plate
reader systems such more on
that a little bit- I think
that's probably the most
tolerated because it's not so
portion of personal it's your
vehicle's not necessarily your
face. That's being recorded.
Sometimes as offer a lot of
data and you do have you're the
concerns of how long to be all
that data and or we using that
data on the back and right and
currently it's not. A
predictive measure it's not
like we're tracking license
plates and saying that they're
going to particular place to
commit a crime we're not having
that. You know predictive alert
system. It's more reactionary
on the investigative side. Is
Hey we saw this vehicle how is
it that maybe that license
plate was in the area at the
time. Makes sense so. Actually
the current- a I. that's most
prevalent I think it's being I
don't it's- in the commercial
realm of you know what
advertisements to local law
enforcement is a voice
recognition- specifically and
the jail systems. Where we
record all the telephone calls
they go in and out of custody.
And it does so you know they
don't have an expectation of
privacy and it does give a
warning on the front and saying
Hey this calls being recorded.
Eccentrics address so- but what
I technologies doing it
transcribing that and this
looking words so it's a key
words or sentiment. Is looking
for. No it are are we having
previously having. Bowl
indicators that there might be
an assault. Or it's also
picking up on maybe a motion
where it's saying this person's
depressed. And maybe they're
having thoughts of being
suicidal and then it would send
an alert to somebody within
custody. Saying Hey this person
is X. means point some open
perhaps you should have some
type of targeted intervention
plan with mental services or
whatever it is I hueso. Kind of
two different ways that is
being used. One on the
investigative I'm and kind of.
Crime prevention side if you
were. But then also on kind of
more humanitarian- you know C.
need if well for. People that
are entrusted to us right so-
so that's kind of where we're
at- right now I mention that it
currently the Joel system at
least in my agency does not
currently use of that couple
voice recognition software but-
every once in while we do have
a company that tries to come
by. And say Hey we had we can
offer this- Bates it's a rather
expensive. I'm a lot of these
technologies are. So I'm but I
will say that I think as we
move into the future you know
we look at. What is it that- a
I. does for us and in an age
where we have to do more with
less- I think that it is a
natural step to say yes we
should be implementing these.
Because we- the staff and you
know it by. Double or triple
the size of. My analytical
team- to do crime analysis. I
would happily take it- but if
we can implement some of the
smaller technological solutions
hopefully that will kind of
offset. That need for the
increased I think so- I think
we need to use it but I think.
With that. I will say that I
think. In the age of big data
there's a lot of private
companies out there that are
collecting data and then it
just typically it's been on the
advertising or marketing side
you're getting all that data
and that's repackaged and then
sold to the government or law
enforcement for. An additional
use right and it's that kind of
a show use. Data set that for
me is a little bit concerning.
And I'll explain why so.
Currently we have you know
facial recognition we have
license plate readers and we're
using that and they're in
different pockets if you will
and you're trying to compare
one data set to another. And I
think in the future it. What
were. Possibly looking at a it.
Is a large company buying up
despair data- and then
aggregating it from different
sources and then making a
pattern. What I see in the
future and I don't think it's
that far off. Maybe five or ten
years is you have- a company
like it let's just say it's a
grocery store or a regular
department store. There is
actively currently there's the
technology where they track
cellular devices within the
stores to be able to figure out
which I'll you're on what you
like to spend money on where
you're spending your within the
store correct. I'm and it's
don't for marketing purposes.
If you then combine that with.
A facial recognition for the
CCTV camera and then maybe a
license plate reader at the
entrance to the store combining
all of the- data sets. I mean
you can get it really paranoid
and think about- the Bluetooth
sensor that measures your- tire
pressure gauge or Mr tire
pressure and sends it your car.
So you have very distinct
signals from different devices
whether to do your your your
cell phone or your- your
vehicle your face. You know
there's a lot of different data
that's collected there if you
aggregated in in total and then
you multiply it over- you know
either French doors or
different that same store maybe
it's a big change. How many
times they go on it at a into
this particular store and you
get a really good indication of
who your customer is and where
they live. Exactly when they
show up things like that and- I
think it's really good for
marking data. But I thought. I
would caution that you could
also use that if it's re
packaged and sold to law
enforcement say Hey or sold the
loss prevention. And say. You
know we've had this guy and he
committed a theft. Last year
and here's his entire history
and here's all of his devices.
So as he comes on the property
now we have a license plate
reader blue to censor something
and saying Hey. This in that
stone from us before is now
approaching. This store now it
was in a store and he has face
recognition yes it's confirmed
it's him and now he has a cell
phone that. We know is.
Associate. With right. So that
type of. X. track something
that could put together in the
long run but- my question is.
As we move along with that data
I know on the law enforcement
side we're struggling to hold
all of our data and all of our
evidence or digital evidence
it's a huge undertaking but I
think the but sector and their
capabilities with that type of
data in the cloud or or power
they're stored in. That de is
able to make those connections
and when it's repackaged in
given. I've to be. Transparency
on sector is how it is they're
collecting their data- and
potentially how it is that the
re packaging or selling are
analyzing it. And there's a lot
of. One of opportunity just to
solve crimes there but there's
also a lot of our
opportunities. For it to be-
yeah I was a misused but
repurposed with out the
understanding from the citizen.
That didn't know that just
going and going shopping was
going to be this big Joel
exhaust of the- that kind of
makes sense so. I'm so going
forward I think. A very large
amounts and I think you guys
have a very tough time to be
able to balance take all these
civil liberties and privacy
rights and everything else on
both the government side and-
you know what's commercially
available- and try to make it a
little bit more transparent as
to how it is that your data
gets. Into the systems. Was it
maybe you could. Or how can you
avoid it- even just today I
went in and something from a
store as paying cash and asked
me what your phone number. Well
okay why we need a phone number
to. Simple cash transaction
right there is a that data goes
and I don't know if there's
enough education on the public
side to be able to say Hey this
is when your data gets into the
system. And this is what it
could eventually be used for.
At you know it is it is what it.
Unions of people knowing at you
know who you are when you go
into a store and knowing your
history maybe it's easier to
return an item if you get my
phone number things like that
but- ultimately I think we have
to do to calm also look at the-
privacy rights of individuals
and say- you know. We're gonna
end up in the long run so is
before I absolutely think a
eyes we needed- within law
enforcement just because of the
nature of the cases that we
deal with. Us for crimes
economic crime there's a huge
amount of data that goes along
with. And we need help just
crunching it because we can't
look at that data individually
and make it that simple you
know this person this because I
saw on there. It is a- long.
Bill that we have to sort
through and- difficult.
Manually having that. And to be
able to do it. Is great but at
the same time we need to do in
that calculated measured way-
so that we are taking those
things into consideration so. I
believe it would at that for
now and in a little bit of
yeah. Thank you. Answer any
questions thank you. Sergeant
don't you you've hit upon a
number of different issues that
are before us right now.
Implicit bias issue privacy
issues- law enforcement in
terms of- refining our tools so
that we do a better job of
identifying. Of folks who are
wrong doers a couple things one
is it and I personally wrestle
with the- distinction between
data analysis. Which is so for
example you get a bunch of
phone numbers and you can you
know sort of analyze the- hub
in the spokes- versus
artificial intelligence which-
we'll get a better definition
I'm sure later on. The day of
where you have a system that
actually. Makes decisions for
you give that system problem
and they come up with with a
solution of the problem as
opposed just gathering data- a
couple questions though
specific to your testimony.
You'd mentioned this voice
recognition technology of our
are there any jurisdictions in
California the that are using
it for example in the jails.
I'm sure that there are I
believe I. R. I don't know
which one off hand I'm sure
there are. Jurisdictions using
it- I could definitely. So I
would be usable when you could
let us know- I'd I'd be curious
as to if G. if there's any
jurisdiction using it. But the
outcomes are- who the vendors
are those those kinds of things
because that is sort of the
classic situation where you're
giving. The system an issue
identify for me those who are
depressed and here you go and
it comes up with. Some some
responses to that you know the
river clear I think that. We
use it hi. I think Lara we need
to be. Of exactly how the data
is collected and how it's
analyzed. Behind the model so a
lot of the private companies
you know come out next they
want to sell your product and
say yes it does this and it's
this. A proprietary magic wand
and then makes this suggestion
this inference between. These
two data points it's inferring
this connection right based on
this key word and that. It
thinks that maybe you're
depressed in custody right. I'm
so having that human element to
be able to understand exactly
how the system works I think.
Develop a lot more credibility
really when you're looking at a
system. As. Instigated lead or
some type of. Action where you
are deciding to do something-
basically when we start talking
about when we go to court. How
is it that that system works we
can't just blindly trust a
machine to say yes you should
do this and- knowing where the
idea came from how it got
crunched. And then
understanding that logic and
your will to put into words and
ultimately into criminal
prosecution is. It's cute the
other issue that. Is of is
before us and is of interest.
Is this privacy. Concern. That
you mention so for example I
can see what the weather may be
implicit bias. If a store
identifies potential
shoplifters by your virtue of
where that person lives you can
identify either by cellphone or
by license plate you sell we're
gonna we're gonna track this
person because we think a
person comes from this
neighborhood is more likely to
shoplift. That someone else.
Honestly think it's based on
where they live but I think
it's based on their past
history. With their
relationship of knowing who
lives in their stone beds that
would be different but you
could use it. It if you were.
If you're being more simplistic
by just. By just analyzing
where the person lives and then
making the leap. That that they
may be more inclined which
would be reflective of an
implicit bias in my view would
be. Inappropriate so anyway
that that's that's the that's
interesting issue that you've
identified also be interested
coordination and perhaps we can
do this offline. But
coordination between federal
entities state and local
entities that Beth is seems to
always be a- continuing- both
issue in tension between the
various law enforcement
agencies I can understand. Why
folks are sometimes reluctant
to share for example the
confidential informants with
others because they're
concerned that. Press
information Mike it. Eight
either Edward Lear in it anyway
so the so maybe we could do
that conversation another point.
Senate Chang. I have a ton of
questions. My I concur with the
good chairmen- with all the
issues that you touched upon
that's stop that we are
grappling with in Sacramento
currently- in fact when we're
talking about- privacy you
know- there was last year we
passed the California consumer
privacy acts of C. C. P. A.'s
incredibly- yes right now it's-
the forefront of a lot of-
issues- the tech innovation
caucus members we actually
travel to DC. Was last week- to
talk about the need for kind of
an over arching- consumer
privacy- law at the federal
level- in California we're
still working on some of the
details because it's so complex
this is a complex issue- but I
am actually concerned now about
these private companies you
know these- data aggregators
now trying to sell data to law
enforcement and I feel like
there needs to be greater
transparency there because I
don't know what kind of data
there. Thanks. Sell you and
I'm- I'll talk. Later to see
what which Connie these are our
doing that because that it. Is
that we need to focus on
transparency and how that data
is gathered- and what kind of
data. But I also wanted to get
your thoughts on for example we
just passed a law it was AB
twelve fifteen which prohibits
law enforcement from installing
activating or using a biometric
surveillance system. That was
just codified and it has a
three year sunset so I'm not
sure if you have any thoughts
on. This matter is that the
same as the body worn camera
official recognition yes okay-
yes I might my thoughts on that
is I think we'll be. I think
it's a good step in the privacy
rights of people but I also I
kind of want to. Take it a step
away from looking at you know
we're we're at. To get ugly and
I have these two hats I'm a
really private person but at
the same time I want to solve
crime- and- I think there's
like a balance that they could
be made where. We're looking
at. You know the ability to.
Instantly identify or at least
get a pretty good idea as to
who somebody is. As a law
enforcement officer talks to
them. And I think there should
be a clear distinction that we
shouldn't be just randomly
scanning everybody on a body
worn camera however. I'm if we
have somebody lawfully detained
for investigation of a crime.
We should be able to use-
artificial intelligence to. By
who that person is. Whether I
take out my phone and I took a
picture and I sent to the
system. And it comes back as a
yes it's compared to this
booking photo we arrested him.
You know a year ago and this is
his photo we can compare that
face to face. And get a
positive ID- I know our
department our department
currently is. Spending a lot of
money on mobile fingerprint
recognition or mobile
fingerprint identification
systems- and I would say that
probably the younger generation
who wants information now-
doesn't really want to wait
for. Finger to come Bob. Just
take a picture of me. I'll tell
you who I am right- so I think
there's a balance between. What
it is that's expected what the
community expects us to be able
to do- I think. In Mike overall
career I've talked to younger
generations that. Have been
really surprised as to what
we're not able to do- you know
they think that. It is a lot
more capability than what we're
actually able to do because we
have. Different silos of
information. So. Going back to
the face recognition I mean I
think. There could be a balance
there. And it could be a need
based on the level of detection
that we have rather than. Maybe
an outright I'm not saying no
to buy metric solutions-
deployed. If that makes sense
yeah and then going long lines
of transparency do you have any
thoughts on. Or any suggestions
for us on how how we can help
maximize transparency. That's
going to be huge yeah I think-
you know in the future going
you know you so we started the
consumer privacy act or
whatever was in California- I
think it may be some. Very
clear language you know if you
go look at a company's- you
know privacy policy or terms of
service or things like that.
It's usually five ten fifteen
pages long and being able to
read through all that really
understanding what that means.
Is difficult sometimes and I'm
one of those guys that actually
reads those before you click
yes. Just to kind of see what
it is that that I'm that I'm
getting right am I getting
myself into. There should be
like almost. Also like a
surgeon general's warning or
something like that but a very
clear message that says this
bird this company will collect
this information- if that makes
sense and then as we go
forward. Maybe just some public
out reach gonna some. You know
essays on you know how
information is collected well
more so from your department's
perspective for. You know once.
Once the a eyes fully
implemented in terms
transparency from a law
enforcement perspective yeah I
think you know from a law
enforcement perspective I think
it's easy for us to say. This
is the data that we collect-
and we look at it and in
various ways- but being very
clear as to how we collect data
when we collected. And having
policies in place as to when we
purge that data. Is also very
very pertinent and that comes
back to you know when you look
at the intelligence system and
things like that we're looking
at twenty eight CFR. With the
collection retention and
purging of information I think
that is very a pretty good
model. Thank you. One final
question Mike. Co worker Mr
honest points out that he opens
his phone with facial
recognition in a as to any of
the four panelists I'm
wondering if that information
is been either offered to you
or if you're where where that
information is being sold to
law enforcement or other other
entities no I don't think it's
been you know sold on the- art
of use apple or Samsung or
whatever I don't think that
that is currently being sold I
don't think that Mr. The our
systems are set up it could be.
However I would say that I.
Probably unlock your phone by
showing your phone to your
face. So if you're looking at a
government ability to be able
to get into your device. Maybe
using your faces and- the most
secure way. Okay. Let me turn
to judge not camara- judge
Deborah I know you're you're
all busy people I know you're
particularly busy appreciate
you being here today to offer
your insights judge the floor
is yours well thank you for
inviting me to- explain what
our core is doing with respect
to artificial intelligence. Up
let me. Firstly thank you were.
So Bergen's etcetera Chang for.
Allowing me to speak I'm very
proud of our- court you're
going to records were the most
technologically advanced
quartz- in the country. We have
the support of judicial council
as well as our chief justice
Connie sick seals talking you
whip who is a very much in
support of using technology. To
make our court system more
efficient and fair. We have
numerous projects in the tech
technology field that we have.
Going on and are pursuing let
me just try to. Gloss over some
of the rather mundane projects
that we have and then go in
more detail with respect to
some once I consider much more
interesting in terms of that. I
require more computing power in
an artificial intelligence.
Some of the more a lesser
projects or jury summons
management- we are exploring
using predictive analytics to
determine if we can better
manage your jury summons.
Obviously we have to make sure
that the this use of a I. does
not apply yes our jury panel
pools so that we have that we
represent all. When we pull
these soldiers in we're looking
to analyze historical data in
our case the U. lies juries. In
you a I M. machine wanted to
see if we can protect our need
for jurors so we don't have to
go. Pull as many jurors in for
our jury trials. We are using.
Automatic we're trying to use
automatic redaction of
documents are filed with the
court we are very sensitive
with the documents that may
contain private information of
individuals such as social
security numbers and medical
data of when they file it we
were in the front. First courts
to use efile. So what we have
to use a certain level of. Of
US screen to make sure that
doctors should not contain
private information that
otherwise all of the public
that can be used- for purposes
that are appropriate. With
respect to translation
obviously in our court system
we need certified translators
to translate- in a criminal
trial however. In our self help
us centers we can use machine-
translators- to help people
that would come and who we have
a legal problem. With three
they were did your levels
portals- we are working with.
The various police departments
to use. Electronic evidence
with respect to traffic cases.
With respect to- data exchange
we are also working with-
various other police agencies
to exchange data. In order to
better serve our public. Let
me- point out of three super
proud of projects I think there
are very interesting and are
more complex with respect the
use of artificial intelligence
first would be used to
artificial intelligence for
online dispute resolution.
Other various court I've
already use online dispute
resolution of both in the civil
and criminal contacts and you
in Utah- there is a limited use
of online dispute resolution
and small claims cases. Of we
are trying to. Interface with
the Utah court in order to use
some of their programs to-
perhaps implement an all white
dispute resolution. Our system
with respect to civil this may
require a certain number of
change of our laws because it's
understood that. Unless we make
online speed resolution
mandatory for let against it's
probably not going to be used
of very often. I'm I just be
resolution is being used in the
Michigan courts with respect to
traffic citations it with the
with. Very much success. That's
something that we're looking
into preliminarily. With
respect to. Trying to have
traffic cases resolved so
people do not have to come into
court and ultimately ever cases
tried. Another use of
artificial intelligence is
something I thought of in-
implementing our obligations
under Senate bill fourteen
thirty seven. Which is the
abrogation of the felony murder
rule with respect to fourteen
thirty seven that requires the
court to go through literally
in Orange County thousands of
cases. In order to determine
whether. People who were
convicted as in the success
rate of felony murder roles are
entitled to resend C. of these
cases are extremely voluminous.
And will require a considerable
out of either staff timer G.
full time to go through the
records in order to determine
whether the various elements of
which entitles a defendant to
be re sentence are satisfied.
If we can develop. The
artificial intelligence program
which actually leans from the
record from these luminous
records the relevant documents
that would assist. The Trier of
fact the judge to determine
whether those elements are
present it so the defendant is
entitled to resend seeing this
could save our court hundreds
of thousands of hours a legal
researcher Clark hours in terms
of. Looking through of these
flumes transcripts. Our court
system is presently going to be
looking at S. B. ten of which
is a pre trial release program-
in lieu of bail that's- a
controversial situation there
is a- referendum out there the
trying to repeal SP ten
however. I at this point in
time we are proceeding- with
respect to our obligations to
determine whether there are
programs out there that will.
Valley predict whether we can
release defendants pre trial
without harm to the public and
with your high degree of
certainty they will appear for
a court appearance. I've looked
at the some of those brands
that are presently. Ben what
data. Of those programs I
believe are rather simplistic
they provide a- one point for
certain elements- at the V.
pro. Privacy be the- Virginia
tool. Has been validated for
the Virginia population- there
are various programs that are
being implemented by the
general counsel where. Grams
are trying to be validated for
population groups in
California. I feel that-
providing one point for each
one of these elements is rather
simplistic. If we can use.
Artificial intelligence to give
a more specific number of
rather than one as opposed to
one point three one point four
depending on what the data. A
was support- we could be much
more precise in determining
whether we should release. Of
these defendants- pretrial
conditions we should use yeah
I'm releasing these defendants
in a pre trial- arena yeah so
we are required to do what
presently under Senate bill and.
I was told by a center Donora
we're trying to develop some of
these. Artificial intelligence
programs- with- UC Irvine. A
one of those programs- mistral
is. Trying to assist the
suburbs of litigants to you
apply for protective order in a
domestic violence case so we
are- pursuing the situation
where we are using- artificial
intelligence to assist the-
suburbs litigants to- actually
apply. In a proper manner of
four of those types of orders.
So one. It's an exciting time
of many of these programs are
absent are at their but it's C.
I would. Urge senators here
that so who would have proper
funding to the courts obviously
so low can pursue- these many
have a use which I think will
make our- court rooms much more
efficient. I think fair. Thank
you thank you judge not Grenier
I've had multiple discussions
about how we can make the
system to go still just system
more efficient a couple
questions you mentioned using
artificial intelligence or
some. System to. Dick I think
predict in terms of the jury
pool what what you may need to
be more accurate is it are you
currently using that system our
or if I develop a program that
does this all all these
programs are out there in the
C. right we are not presently
using any of these right now
these are projects that we are.
Not trying to prioritize with
respect to what's most
important the court in terms of
what we feel that we- would try
to provide the most. Bang for
our Buck so to speak you say we
are you talking about just one
case core you talking about the
mystery of all courts I'm
talking about they're going to
record. We're up we have one of
the largest the staff of
technology of people. Problem
for but once we develop these
programs obviously we're not
gonna keep them to ourselves
we're gonna sure eleven song
yes right we are new we form a
trial court. Go ahead we a lot
of these programs developed
royally without the court's and
we will share them with and
that to the extent that- we've
developed programs are
beneficial to other courts
around the state we certainly
will. What if are there other
jurisdictions outside of
California that that have
developed a methodology in
terms of- calling the jury pool
to be more efficient you know-
not that I know of okay because
of the wondering had they been
challenged you said in Utah the
using some sort of artificial
intelligence system force for
small claims. Online is
pollution is it is there being
involved there there are human
beings involved the some online
three resolution systems are
rather Henry it's just a
situation were offers. An
answer given yeah that's not.
The value of the case and try
to suggest a- and number so to
select case I mean I think
these are it's one that we're
do you know where those are
being used. I. th. I think the-
use the other countries to. I'm
not here they've been used in
the United States. I know that-
we- in terms of insurance Kerry
years they've been using these
types of artificial
intelligence predicted but
analysis programs for years.
Days so we'll of they know eggs.
Degree how much a case is worth
the based on certain factors
they put into their programs
and of this is what they allow
their defense counsel offer.
Obviously. That trash and trash
out it's always good at sign.
The fact is they put in. I hear
tell the that there are
programs that can for example
after- council received vents
cal's receives a complaint that
can spit out of demurrer for
example- up in it would seem
that if that's the case that
apps courts could look and see
whether that demurrer is valid
another words just look over
the complaints if all the
elements are. Properly pleaded
is that being used anywhere no
not really it. And again the.
Color Jeff let the three bears
the judge to make the ultimate
decision there have been other.
It's actually been cases of.
Judges actually putting certain
factors in their decision that
ultimately gives a protective
decision for them to make an I
just basically allow the
computer to make that decision
and that was considered to be
an ethical because the judge
actually has to review the case
and make sure that that case
has specific. Factors that the
court has considered the vessel
with respect to the fourteen
thirty seven situation I'm not
proposing that ultimately we
put all the factors into a
court computer program- and
ultimately help that okay
recommendation it should be
only used to get the ribbon
information from these huge
files. Of. Huge amount time can
M. from the judge. Files all
right a law clerk eight related
legal research attorney so that
the time that's involved in
making that decision is very
very much abbreviated. So there
there's a new law effective
January one two thousand twenty
that provides the judges can
are the parties can stipulate
to- early exchange of
information analogous to
federal rule twenty six you may
have heard of this judge not
tomorrow yes I have a- and- a
well reasoned rule I mean well
thank you very much. And you
your query that that would be
something perhaps statewide
that the administrative also
courts may want to actually
take a look at to see. Who's
doing it what judges are
suggesting it what court rooms
is becoming more prevalent
whether that has any impact on.
Exploiting cases Ben and
reducing discovery motions just
a thought judge so yeah thanks
for the suggestion I think it's
very good one. Thank you Senate
Chang. Thank you I'm I just
want to go back to what you
were saying about the traffic
tickets was that. The A. R. A.
I. being used and- well yeah
again there's a. They're still
human element in online dispute
resolution at prickly when it's
Bates when it's done in in
Michigan what's happened is
that certain citations are.
Issued by the traffic officer
and a proposed a- resolution of
that. Is ultimately given up
why a judge or a trick attorney
with respect that usually at
something much less than what
you potentially could be. In
terms of the fine. I and that
that's offered to. Of the
defendant of the person who
made. The vial allegedly made
violation and they have the
opportunity to either accept
that or eventually go to trial.
So that's how in that's
resolved quite a few of the
various at least in Michigan.
Citation they have their
because but many times the
offer made by the judge or the
DNA is. Does not Derick choir a
point being placed on that the
defendant's record so what's
the a large enticement for that
defended the to accept that
offer as opposed to go to trial
and risk I having a point. Or
two placed on there. There's a
record so you mentioned there
isn't a human element and
there's definitely going to be
human element in any type of
technology that has been
created. But the algorithm that
is. Being constructed for this
traffic taking I'm kind of
curious about it because how
would that look like so is it
just not even. You don't even
have to appear in court is
that. Well a lot is the
resolution basically is the
ultimate goal is that people
don't appear in court and
they're given an offer. Us so
that the they don't high enough
so that they would. I believe
that the it's better for them
to take that off for us to
post. Your court yet let's take
an example of. It's a the
speeding ticket this this is
mission Jim because that's
where where they have this. And
it possibly can give you two
points and it could. Be a fine
of two three hundred dollars
which is a lot less and would
be in California but as we know.
Ultimately the judge might look
at the file and Mike. Might
ultimately say that this person
I'm a record of all prior
violations. Of I will offer
this person a traffic school
hundred dollars no points and
at that point in time that
person can make a decision
whether to contest that
particular citation in court or
take that offer. So that's how
that would ultimately you work
in that type of situation that
the in California the fines are
much higher- so we're gonna
want to show up in court. Right
so that's why I'm kind of
curious as to what how that
will help that's how it works.
Okay interesting. Can I and I
just want to comment that I
completely agree with you that-
assigning just one point- in
terms of what Virginia's doing
is a little bit too simplistic
and yes there's like seven
certain seven categories like
white one point four and it it
it just brought me is a very
simplistic way to determine
whether somebody should be-
release pre trial or not I
think we need something more
precise. Absolutely thank you
so much. Thank much. Up next we
have a senator don senator
slash dean's Lynch council done.
Seller Dodd floors yours I
thank you Mister chair- first
let me extend my personal thank
you along with my fellow
panelists to- the chair to
senator Chang your respective
staff to the Senate staff-
because I know as you indicated
Mister chair- going on the road
with the hearing is a logistic
nightmare obviously for the
great staff and then of course
here again. Thank you to
sergeant arms or here as well
to whom I've known for a very
very long time and it's great
to see them here as well and
now some of the greatest
professionals I've ever
encountered. I do have to
extend a- an apology on behalf
of the original designated-
speaker for this role and
implicit bias professor from
UCLA and fortunately she- was.
Taken down with the flu last
night it sends her her sincere
apologies for having to. Cancel
and- I get the lucky task of
stepping into her shoes. With
great honor- I'm gonna take a
slightly different approach to
this is the issue of implicit
bias you know particularly as
it relates to- application law
enforcement and judiciary- and
take a very practical approach
to it. But a little bit of
background first- I come to it
really in in two different
roles- as special adviser to
our dean song Richardson at the
UCLA school of law. And as many
of you know- she has been very
public about. Jean you see I
by- so one of the top twenty
five plus schools in the
country. As one of the first
within the law academia bill
breaks hi and in her words in
the DNA. Of the law school on
his Beth great honor to work
with her. It in that process
that is well under eight out.
I'm also here- and the role I
serve as chair of a national
foundation. These projects.
Your views projects mission is
to eliminate child sexual
exploitation and all of its
forms on a global basis- and
you'll see why that role that I
share- is gonna come into play
a relatively quickly here. Of
so one it just share a little
bit about what we've been doing
in zero abuse because I'm gonna
keep going back to it- as it
relates to implicit and
explicit biased in a I.
applications we have been two
years- it zero views project-
and we own the intellectual
property to it. To building in
a I. platform that is focused
on- identifying sexual
predators of children. The
prototype is built on what we
refer to as institutional
sexual abuse of children
meaning abuse inside of an
institution academia religious
sports whatever the institution
may be. And we've been working
in collaboration with local
national and international law
enforcement agencies with
respect to it we're two years
in we're about to reach what's
called minimum viable product
with- which is years simplest
form that it's ready to for
deployment- literally in about
two weeks- it's been a long
process. I learned the hard way
about a I. and billion a I.
platforms through that process
again and thank you to our
outside company that's been
building which is local. In
Irvine techno says a
tremendous- talent talent- that
they brought to it- the premise
of the platform simply is it
observers C. externally
observable. Behavioral patterns
of an institution. Termin if
the institution itself is
hiding sexual predators of
children inside. Focus is not
on the predators and it's not
on the survivors such victims
it's on the behavior of the
institution itself. If you can
collect enough data on the
externally observable
behavioral patterns are and
know what those patterns-
means. You can then in essence
on cover a cover up inside of
eight institution. Our it's no
surprise that there are some
well known institution of
struggled with this issue for
decades- the Catholic Church of
course the Boy Scouts and now
we're beginning to see in
academia. The end the Olympic-
world- many sports programs
unfortunately the list is very
very long. This is the key
investigative tool for long-
intended for law enforcement on
has no a I. application
actually dictates anything we
should all view output of any
I. platform. Is simply a tool-
some valuable information- that
should lead to in. Other law
enforcement contacts other.
Traits investigation it's not a
Beall and all conclusion but
helpful in a variety of
circumstances. Of the when that
platform goes live you will not
be made public- it will just be
available to academic
researchers law enforcement
exedra- in the a prototype-
it's eight an institution that
has had this problem for a very
long time there are. Thousands
of known predators of children
that have served in a
institution and many that have
not. Come to the light of day
and our goal is to take the
known- predators compared to
the database on the rest of the
individuals employed by that
institution. And see if we can
determine other predators- we
think that number of other
unknown predators to the
public. Sadly is quite large
consequences are huge- on
average a sexual predator of
children before here she is
taken away from any access to a
child. Usually has abused fifty
or more kids before they're
actually caught the numbers
are. Staggering. Excuse me. So
let me move into a practical
examination of how bias
Blissett and explicit get into
any I. platform. It's not a new
phenomenon we all know of the
concern about biased in a I.
applications- it's not a new
phenomenon and- the tech world
been aware of it from her for a
very very long time. How to
resolve it is a much more
difficult issue- there's a lot
of rhetoric around bias the
issue biased in a I.
applications- frayed at least
in my humble view the rhetoric
isn't necessarily on track.
Every a I- application has buys
in because it was created by
humans and we all have it. So
even though there's some that
want to argue with that- C. a
law enforcement platform- which
deliberately dot bias against
this community or that
community. None of them were
built that way- it's simply
inherent if you understand. The
build out of an A. I. platform
isn't defended or make it
right. But it's a very
complicated question and I
think for all of us
particularly you as Sanders as
policy makers. Of to ensure-
understand for all the
legislators that this is a
complicated question despite
all the rhetoric it sits. There
in particular in the media
space. I'm. I want to share
something wish I could claim
credit for this I can't- but
probably drives the point home
we all know what the cost of
any I. platform is an algorithm
or algorithms- and is often
times said by many far more
knowledgeable in the eyes and
I. Is an algorithm is simply
someone's opinion wrapped up in
computer code. It's a great
very laypersons definition
it'll be the humor in there.
But there's a lot of truth to
it- because when I develop a
premise for any I. platforms
really my opinion. Is wrapped
up by the tech folks in in
computer jargon I can use that
technical term- and if we keep
that in mind when we think
about bias in the I. that's a
truism for almost every a I-
application. That we see in the
public or private sector- today
including law enforcement the
judiciary as as well to. The
challenge however is for the
average person on the street-
many studies have shown. They
trust the conclusion that came
from a computer. More than
they'll trust. An individual-
perhaps the most common example
is this is Google maps. We've
all encountered a Google map
direction that says go this way
and you go. I just know that's
wrong. But 99% of people will
ignore their own experience and
trust what the computer says.
When in fact the computer can
do harm to- and so you start to
merge this opinion issue.
Wrapped up in code with an
inherent belief a computer is
always right by the public.
You've got a recipe for real
challenges here because-
implicit biases in every
platform but if the public is
going to assume the conclusion
from a computer is right. We
got a child. That's a big
challenge there. So how does
bias in- a I. applications come
in to be. And this is where I
want to get very practical-
and- I'm going to generalize
here so if we have any a I.
experts in the audience of
bristle at this but- it's a lay
person's way of looking out the
build out. Of an A. I.
application. To see how black
by it's gets introduced at many
of the stages- and this makes
it very difficult as- C. make
yours. And how do we wrestle
with issue when bias can be.
Inserted into an application at
many levels. We all have
different definitions of a I.
R. machine learning the tech
industry doesn't like
artificial intelligence they
prefer machine learning for
lots of good reasons- us Leyva
folks. Are just comfortable
with artificial intelligence
which is probably more of a
Hollywood label than it is
anything else. But at its
simplest form- is a I. machine
learning is an algorithm that
the tax patterns in big data
sets. That's basically what it
is in its in its simple and
less funny way. But
unfortunately it picks up
patterns that yeah are in a
database that a human created
therefore there's lots of
places that the- bias can can
surface. We know it's a well
known problem we know that a I.
algorithms can perpetuate bias
and justice whether it's in the
hiring process many of us know
the now infamous episode of the
Amazon hiring process- in which
they wanted to figure out. Who
were the best candidates for
application that would fit
within Amazon and they based it
upon- create a database of
their past out of hiring. Of
technical folks were very
successful at Amazon. The
unfortunate- problem was the
they turned it off pretty
quickly. The is it recommended
hiring a lot of people who look
like me and not very many
people. Gender race or
otherwise that didn't look like
me. It step that was a pretty
high profile on the situation
Monning many- it's shown it you
see it all it is C. the bias
and retail applications
security and law enforcement
issues Cannella justice system.
Every place a I. is use which
is almost every industry- in
society. Has a bias issue in
it- I don't think anybody would
stand before this committee and
say I have any I. application
that is zero bias. It doesn't
exist. So to look at the build
out of a I. platform you just
from a labor of perspective is
just three stages. It's what's
called the framing the issue
and requirements phase median
what you want to accomplish.
And what does the roadmap look
like for us the technical folks
in the subject matter experts
what's the road back look like.
To get to the end conclusion
you want to cheat. On ours your
abuse platform we wanted to
identify unknown predators of
children inside institutions
that was a promise. What's the
roadmap to get there on the
institution we picked to build
the platform on the prototype
we needed subject matter
experts about that institution.
A I tech experts by themselves-
they can create a lot of fun
stuff but if we want to solve
problems you need to join them
with subject matter expertise.
In the case of our presiding
judge you need judicial experts
to talk with the A. I. experts
to build platform. Well in that
for please call the requirement
phrase- you can introduce bias
quite unintended. And we've
seen this lenders will have.
Bill many different M.
locations but let's ask what
the mission is. If it's to
increase profits. Or is it to
increase repayment- percentages
both depend upon how that
lender will define credit
worthiness. But if it's not a
specific definition connected
to that particular goal I just
one increase profits of my
company. You can enter to bias
in simply defining a word in
that requirement face. I'll
parole risks there's a number
of a I. applications that are
trying to create. A I. analyses
on the parole risk of someone
who's about to be released. How
do you define risks. Is that
Rhys median likely to commit
another crime. That wrist
meaning likely to be calm
dependent upon state resources
for survival without committed
a crime. If you define it only
in one way but use it for
different mission you're going
to introduce by us and you
haven't even started to build
your database. Buyers can speak
there- we had to be very
specific on zero abuse pop form
on what did we want to
accomplish in their- folks
thought when they help from. We
presented to the U. N. twice on
the platform. Were we trying to
predict predators of that was
the initial response in the
answer is no. We don't do
anything in our database- and
our platform about the predator
all about examining the
institutional behavior. But if
you mix the two in your
requirement phase you're going
to get by. The second phase in
probably in most applications
the most complicated phase a
most difficult is building the
database. That is a very
challenging problem more
challenging than we thought as
we built this platform. On a
particular institution where
there's lots of publicly
available data. But just that
building of the database phase
can introduce lots of different
components of. Bias X. implicit
or explicit. Of so let me give
you a couple examples if your
data is incomplete you're going
to get some pretty biased
outcomes let me create a
hypothetical is loosely based
on a real life situation. I'm a
company that's trying to
develop self driving cars. Well
one of my challenges is tech
team to train the car. To
identify a pedestrian in a
crosswalk. Pretty obvious risks
for any driver obviously. And
so to do that I go to my tech
folks how are we going to train
the card to identify pedestrian
it to stop or to avoid that
pedestrian that's in the
crosswalk. Well in all
likelihood of all of the tech
folks who are designing the
pedestrian issue in the build
out look like me. Of what
happens is the card gets
trained to stop for a
pedestrian that. Looks like me
but unfortunately. And it
really happened number two
years ago. The tech folks
didn't intend any bias at all
but because of how they created
the database and we're training
the application it wasn't
complete database it could not
identify different gender than
me a different race than me
different heritages me. So I'm
safe in the self I. in cars
because I'm white male
everybody else not so much.
Obviously when they discovered
the problem they had to go back
to the drawing board but that's
an incomplete data set. On a
very critical issue that all of
a sudden the technicians didn't
mean to have a biased outcome.
But it ended up by this outcome
unfortunately. I we already
talk real briefly about the
Amazon situation predictive
policing. Lots of rhetoric
around a concept predicted
Ripley scene nobody's really
come up with much of a
definition but we got the
public debate over its got
privacy implications bias
implications all kinds of
implications. We come up with
if we agree upon the definition.
But often times predicted
please scene type platforms as
they've been called are based
on incomplete data. And if you
have that predictably seen a-
application may end up focusing
on a particular community a
particular type of person- and
only focus on those and just
perpetuate the bias and it's
all started from an incomplete
thought about the base. How you
prepare the database. Now
before you put the third phase
which is putting the A. I. he's
on to the database which is
often times the most simplest
process of the whole process
interestingly enough- how you
prepare it- in our- database
for zero abuse project. We had
to figure out what the flags
were of that institution
behavior that indicates that
there is they're hiding a
predator inside that
institution. Well developed red
flags- we found for the subject
matter experts on that
particular five platform. They
had a list of red flags of how
the institution handled folks
inside that seem to indicate
that they institutional
concluded was predator of
children. Of. Their own
inherent bias in they'll- our
it seemed to be an object of
issue and it turned out to be a
subject. We haven't even gotten
to train. Platform yet- that
these are just examples of how
bias can does begin in the face
to for most a I build out which
is. Getting a workable
database. Of I'm gonna use some
technical terms and they're
only ones because I'm not a
technical guy- in our situation
which is often times the case
you have to take the raw data
and person code. Meeting you
you pretty to a format that's
readable and workable in Exeter
up. Of how you parsing code get
introduce bias into your
platform never intended- in our
situation we found that- as we
are perceiving coding would
seem to be just. Addresses and
street names and states and
accept or something seemingly
objective. Interestingly enough
our own decisions about that
part how to parsing code with
the technical team ended up
introducing some potential-
bias into the process. And then
of course your face three which
is adding the A. I. application
to it- that's where you train
the A. I. aspect of it on what
you want to accomplish because
in a I. platform can't do
anything on its own it has to
be trained. What does though is
it then moves from a kid a
gardener. He we take twenty
some odd years as humans to do
that. And take a matter of days
to go from kindergarten level
to a PhD level on whatever the
subject matter is. And then
start connecting dots that the
human mind can't see inside
that database. Okay but now
let's go to the issue how do
you fix implicit bias is a well
known problem- it's just the
extent of how implicit bias
gets into an A. I. application
again whether it's judicial law
enforcement. Private sector
retail whatever the case may be
it's all. The same. Of and
again I'm ignoring those folks
who deliberately designed by S.
A. I. platform that's a whole
different problem it's the-
unintended. Bias that is really
the challenge for us as a
society from my perspective.
There's that hotfixes obviously
the Amazon hiring situation
they all my gosh. We're not
getting very many women-
identified qualified applicants
through this process so the
immediately go to die okay we
have to eliminate. Explicit
gender works for example women
applicant list. College
activity women's swim team.
Phone that thing got rejected
by the email Amazon apple.
Because there weren't very many
women says swim team- folks
wouldn't hire the past because
everybody look like me. I'm
overstating a little bit so
Amazon doesn't get a little
upset about that. They thought
they fixed it but the A. I.
application learn so fast and
also picked up from the past
database of applicants that
there are certain words that
tend to surface on application
submitted by woman versus a man.
Not the obvious word of a
women's swimming team. But how
things are phrased whether
right or wrong it had concluded
from the tap bass database that
in fact. There is a subtle
difference in language use on a
resume and accept our so went
to an implicit level and
rejected the application. So it
gets really complicated Amazon
of course never intended to be
biased in its it's a I.
platform. But it happened both
explicit and implicit so
there's that hot fixes when you
identify the bias in the source
of the problem but that's just
ad hoc after the fact- fact.
The more the sing surface
without getting caught ahead of
time the more the public's
faith. Goes down. And
rightfully so. You can bill
detection systems- most a I.
platforms are built for
performance not for bias
detection. You can build in
there are a I. platforms under
development of can a I identify
its own bias. Whether those
will work we'll see- there are
some who are debating whether
in the requirements phase to
we- require a platform in the
it's its creators to define
what absence of bias looks like.
The problem so let me just
finish with the following. It
will say from a public policy
perspective that's probably
where most of the work is being
done on how to deal with bias
in the I. platforms. Could in
law enforcement judicial which
is so critical to function in
society. I think we can be less
concerned if I'm selling pet
rocks and trying to figure out
as a more through any I.
application. But there is
great. Being done on the policy
lawn ethics surrounding bias in
eight I but a lot of folks
aren't aware of that- lot of it
is on accountability issues
responsibility issues ethical
issues and etcetera and I'll-
not leave it vague- the United
Nations and several of its
agencies. Are doing great
international work in this area
it's been an honor to work with
us some of those agencies. From
UNESCO- to the I. T. you know
most people have no idea what
that is that's the oldest of
the agencies in the United
Nations stands for
international.
Telecommunications union not
union as we see the word union
but- store clean- it's been
around actually for over a
hundred years but it became the
first. U. N. agency they deal
with all the technicians the
policy. One we're working with
his unit Cree that stands for
the United Nations inter
regional crime. And research
Dodd- justice institute I get
that right and maybe off just a
little bit- they're doing great
model policy work on- setting
up. And via a barriers along
the roadways as far as
developing a I. in minimizing
the risk of a I. number of
trade associations that have
many of the big tech companies
are working on this. They're
trying to find a fine balance.
Many at the public level don't
think that balance is a knife
towards eliminating bias
they're trying to find a nice
balance of still makes for
work. A I. apple turns but- at
the same time Bates minimizes
much as Italy's practically
possible. A long ways to go
unites these government of
course is doing some states are
doing. This this committee of
course it is falls into that.
Thank problem the two areas
that are most focused on right
now on the issue of bias in L.
A. I. applications. Our
transparency and explain
ability those two words- and
how do we require the president
let me just interrupt you just
for second we made certain
representations of the
panelists the time commitment
almost finished out I want to
make sure that that we're
honoring those commitments so-
for the three of you you're-
certainly welcome to stay. But
but it won't arrest you if you.
Believe so. Anyway so thank
you. Janet are right right so
centered on thank you have
reached our agents here but all
the struck them not ten fifteen
years. Okay a letter senator
done tries that's a different
story- my only last comment has
are there is model policies-
through many U. N. agencies or
working on this. And others-
that focus on dealing with
implicit. Bias at least now as
initial step. In focusing on
transparency about bias and
explain ability of the A. I.
platform so that at least the
public at large has an
understanding and awareness. Of
the bias issue- now obviously a
lot of the creators of a I.
platforms or worry about
lecture property rates and all
that kind of stuff because- I
know the senator the chair is
well aware of this. Gap in I.
P. protection for a I.
platforms is not easy. Because
of how one defines a patentable
idea. It has particularly
difficult application in a I'd
and. See a lot of businesses
vendors exciter keeping their
cards close because they know
in many respects the can't get
I. P. protection- but there's
focus now transparency and
explain ability with respect
the- platforms as a way of.
Least highlighting the bias so
folks are aware and encouraging
more work on the policy side
tech side. And otherwise- and
I'll just end with the
following challenge I always
the C. from our law enforcement
community national state local.
They they face a very big
problem because they the and
they realize that this is not
not not new. In dealing with a
I. nouns true in in past times
the- all they know that some of
the biggest spenders in
developing a I. platform happen
to be the drug cartels around
the world- who aren't very
interested in someone's
constitutional rights or
privacy or etcetera. Or biased
in their application. And how
does law enforcement
effectively deal with something
as severe and serious as drug
cartels on a global basis who
are spending billions on a I.
development for their purposes.
And still respond from an
effective law enforcement
perspective- but still having
having to which everybody
agrees including law
enforcement. Under all the
constitutional privacy perspex-
protections that exist in this
country- and it makes for a
very very difficult tension-
and when you're dealing with a
criminal entities out there.
They have lots of money to
spend on a I don't care about
bias don't care about privacy
don't care about constitutional
protections. And how do we
respond effectively in today's
society there's a way to do it-
but it's a difficult- pathway
and sadly- of for our policy
makers in the state legislature
and the governor's office. You
guys have to make these hard
decisions but I. N. was simply
biased in a I. including law
enforcement and judiciary. It
it's introduced at multiple
levels in every single
application it doesn't mean
that anybody is trying to do
something inappropriate. It's
just inherent into it because
it's us humans to build them-
and how do we as a society deal
with those by that was a very
difficult challenge as the tech
world tried figured out. But
you have to try to figure it
out from a policy perspective
all that all in their centers
thank you. Thank you senator-
and appreciate the tutorial
and- recognize identifying what
will the reasons we wanted to
deal with. Artificial
intelligence and how it relates
the judiciary and law
enforcement community and the
issue of bias is because- this
is an area can be most
pernicious- if for example you
happen to get additional
advertising because somebody is
used in. A I. algorithm to
identify you as someone who is
a potential purchaser of
running shoes but the big deal.
If your liberty is affected
because of a certain bias.
Whether it's in the judicial-
brown or law enforcement that
that is a big deals and so- I
know you- as both professor as
well as a foreign policy maker.
Are concerned about a note I
don't know if you've got any of
your students here today my
desire if I. Understand your so
Cassie okay well thank you very
much for being here but but I
also understand you're gonna be
working with the orange case
peer court- judge not kommer a
to deal with some of these
issues and- I'm hoping you'll
keep us abreast. Of how we can
as policy makers make sure that
that. The tools that we permit
our our tools that are both
effective but all so not- let's
not biased in ways that. Are
inappropriate so I want to
thank you for that- thank you
Senate check your questions.
Maybe just some comments I want
to thank you so much for your
comprehensive in very thorough
presentation I know all the
getting your contact info and-
talking offline but just. Just
a brief comment about the self
driving cars because- when I
was in the same way I
introduced a- legislation self.
Driving cars but why would they
need explicit data in order to
prevent you know of an
accident. Because of a person
is crossing the street why when
they use an algorithm just to
figure out the mass. Rather
than like. No gender and skin
color I mean that that's just a
little bizarre to me I would.
When you're thinking about like
people crossing the street I
would. I would consider like
the mass of even a dog right.
And not so much like. Like the
gender and race of a person
crossing the street so I'm
gonna. Use about that
particular company in the
algorithm but- again thank you
so much for your presentation I
really appreciate your
expertise thank you senator
it's- interesting on this self
driving cars is. They their
approach was to identify a mass
mass that look like a
pedestrian for example. Never
intending- to introduce any
sort of data or any other
aspect that might indicate that
there is. Some relevant
difference between somebody
who's male versus female or-
inverse after American Exeter
a- but very subtle it got
introduced in the A. I.
platform picked it up. And that
came from. That's interesting.
All right it in a moment we're
going to have public Connie I'm
gonna ask those who wish to
comment to come to the
microphone identify yourself
and if you would Dodd. Where
you reside- the purpose today's
hearing is to focus on
artificial intelligence and how
it relates to law enforcement
so I would ask in terms of.
Providing us valuable input
that we focus on that aspect-
and- I'm going to right now I'm
gonna live it folks to three
minutes each. So if you are if
you wish public comment if you
would approach the Mike from we
have not had folks fought hard
so- if you come forward and-
for yourself and where you live
a great. Any public comment any
with any persons wish to make.
Lookup. Thank you so if you if
you wish you could come forward
there's not a long line so
you'd come forward and common.
Right right. Hi good afternoon
my name is Jennifer Ross and I
work with the ACLU of southern
California here in Orange
County I'm a resident of
Huntington beach and I work in
orange. You know as thank you
for holding this hearing today
as you well know the public
deserves to feel safe. In their
own neighborhoods and that's
why public safety in this
digital era. Must include
extensive transparency
community oversight and
accountability. For a I.
technologies that allow police
to track and detain civilians
as we go about our de daily
lives. You know the committee
spoke today on the need for
public outreach when law
enforcement employees
surveillance technology. And
often times particularly in
this county we find our police
departments and sheriff's
departments purchasing a
technology without the public
without public knowledge or
input- and- we really believe
that the public has a right to
know when local law enforcement
and is considering the purchase
of a I. technology. The secret
use of a technology for as we
know and as was spoken spoken
on today of fuels
discriminatory policing and
provides recipe for abuse. Add
technology can easily become a
powerful tool for police
misconduct. These technologies
as we know can have the power
to invade our private lives and
create databases vulnerable to
exploitation by local and
federal government. Of for this
reason we believe that
community members must have
extensive opportunity to
provide input before any.
Artificial intelligence
technology is purchased by our
local law enforcement agencies-
and as a legislature we hope
that. The you know are these
committees and the legislature
as a whole- does all that is in
their power to prevent
technology from being purchased
without extensive public input
and knowledge. I thank you very
much for your time. Well anyone
else wish to comment. But Sir
go ahead my name is David
radius and I live in Anaheim
for Leslie seven years and I
was invited by a friend of mine
and I came here to learn you
know and so. There's a lot of-
concern as chairman- mention
regarding bias and privacy and
me being a man of. Color well I
have some concerns on how that
works you mentioned that. There
was a el Salvadorian who
photographed then if I was
somebody does a smuggler and
that help you catch that person.
How accurate is this you know
so if my face. Had a picture
similar somebody a cartel. You
know. Misidentification. How
accurate is the your morning at.
So can you help me out on this.
Do I have to fear as a person
you know with this the negative
really mission positive. Things
that it can do but is very
negative examples that you guys
experience. So Nancy certain.
The no I appreciate appreciate
your comment. The that if you
direct your comments to us as
policy makers what will take
them aboard that and after
everyone comments on- provide
some response but- thank you
thank you. Right others yes Sir.
Hey good morning my name's Erik
I'm with the AC you southern
California and I'm in Anaheim
resident have been all my life-
and I think just a couple of
comments I think one-
predictive policing the sort of
come up a lot and I think it's
important to note that
predictive policing predicts D.
E. R. activity of police and
that region right these trends
the area as on the people are
all based on who polices
enforcing on not you know what
I mean and so did that the
concerned about the target
retargeting of out of these
communities I think it's- very
important and has to be sort
of. Dealt with that with the
doubt see that it is making
secondly we're already seen to
the license plate reader point
hi Julie solutions a private
corporation is house in a lot
of this data for a lot of law
enforcement agencies right they
were sharing that information
with science writing so the
unintended consequences of
bringing in the private sector-
to sell this data and to
collect this data because law
enforcement is unable to I
think has we've already seen
that issue right and how that
plays out. Right there is no
regulations on the way that
these corporations have access
to it the. Web of data sharing
whereas the public's concern
has always been to limit it and
we've seen even ice on trial I.
switches and trolley and the
ACLU case the couple last month
I want to say two months ago
maybe- and that the ruling of
that case- specific ice the
trainers and databases. Which
speaks to the fact that Rabin
was that the status C. these
databases are ill equipped and
are unable to establish a just
cause or probable cause even
I'm in these cases where are
the specific database issue was
that US citizens who at some
point were entered into this
database- as and documented
immigrants in the past Websense
adjusted status has some sort
of relief were being detained
and does what it does for the
plaintiffs right these are
folks so there's already been
sort of- really really
widespread issues with the way
that the status been collected.
In the way that it's being used
right and so just want to. As a
cautionary sort of examples of
the way that data has been used
and it's- I totally agree with
with- stone bridge point about.
Right these are concerns of
privacy right and so I think
they do need to be dealt with
very seriously thank you. Thank
your- others. Anyone else wish
to comment all right. If
there's anyone else- indicia
this gentleman- if you'd line
up just so I can get a sense of
how many folks want to comment
that would be great. Go ahead.
I have a morning my name is
tomorrow but my love you sent
the I'm currently a resident.
Of Santana I'm also a source
just as fellow Canadians
resources fellowship- and pan
PVC serve two terms on the C.
Berkeley's police you
commission. So I have
previously worked on policies
relating to a I. and
surveillance technology. I
think first and foremost what
other folks have emphasized I
think it's really important for
the committee to be aware of
income is events technologies.
That are gonna be inputted into
the police department I would
like one recommendation is you
know being allowed to host a
community forum. Where the
communities can allow to have
input- I went to and what type
of technologies that local
enforcements want to your life
in order to enforce and- M.
police these communities- I
think also employed in a
conversation and important
message. Is I feel like all of
us should feel safe especially
in our own neighborhoods. I
think that's why public safety
and that the Joel era must
include transparency oversight
and accountability for
technology that allow police to
track and detain us. As we go
about our daily lives make
false arrest interrogate us
regarding our protective for
some M. activities. And portray
activists groups are for let's
let's take the deportation
machine- and as other folks
have been seen in- transparency
and accountability. Is there
any. I think it's very
important for me to have it.
Community folks and surrounding
cities in Orange County thank
you. Thank you. Hello my name
is Jordan Roger I am currently
reside in Irvine and I am a
member of senator done on a I.
frontier course at UCLA school
of law- one thing that I just
really wanted to urge both of
you to consider and all the
senators and representatives of
the state of California- kind
of goes to one of the
testimonies today about the
protections of facial
recognition and thumbprint
recognition on phones- so judge
west more of the Northern
District. Of California
actually has written an opinion
and five that those should be
protected under the Fifth
Amendment and because it is a
passcode and- the Supreme Court
has ruled previously. Thought
passcode are considered they
can't be used against you went
in a police interrogation or-
for the like past thought I'm
just because- that is self
incriminating- and so I think
that going forward we need to
make sure that we are being
protected by her Fifth
Amendment. And she said that
face ID and the fingerprinting.
Is could be self incriminating
and so. She blocked that for
being used- but just the future
policy makers need to make sure
that we are being affected.
Amendment thank you miss
welcher our- his last comment
go ahead ma'am hi good morning
or good afternoon at this time
many miss Kathy doubt I am also
a student with senator done at
U. C. I. law I'm our concern as
process prospect of attorneys
and you know this growing world
of a I. Is at present a I. has
a problem with implicit bias as
we have touched upon in
testimony today on the topic of
transparency in I hope the
Orange County Superior Court-
shares with the community how
they propose to reduce bias
Dodd being fed into the court
used a I. systems- and also how
a I will be used to victory
process more effective. Among
the other processes being
created this touches upon I
believe the need for perhaps
are more education to the
public and there are members of
the public are desperately
compacted- and who also don't
have access to fuller fully
understanding the rights to
publications that bring. I'm
into this world so you just
would hope the consent that
thank you. Thank you what with
you all. So for example just
taking the last comment is that
I could certainly see if you're
trying to create a jury pool
that you you want to make sure
that you have jurors who can
participate two or three week
trial that would tend to
indicate they want retirees
they'll query whether by just
by focusing on retirees you
biasing the jury pool in an
appropriate way I don't know
the answer that but I do know
that that that creates.
Interesting dilemma court.
Queen those who can put ten
server. North thirty year old
who's hourly employee may not
may not be able to serve so or
you want to respond Jackson J.
sure say that the public to
federal beer the presiding
judge to because. Go ahead Klay
involved in any type of
considerations that we make
with respect for jury pull. Up
we already received a letter
from Richard purpose you know
just about a week ago-
enquiring whether the court is
going to make sure that-
convicted felons who are now. A
part of our jury pool as long
as they're not probation are.
Included in- the our summons
and- but we- definitely will
use up with the district
attorney as well as- and- in
any topic Senate races we- we
make in terms of making sure
that the- all is representative
of the population as we are
required to do but- thank you I
and I think that's it that's
that is illustrative of a
difficult situation I. Retired
to may have more time versus
thirty. Least my view of a bias
so heavily in favor of retirees
but we say it's often we want
pre qualified jurors- because
it makes trees like a process
much more. Hey can expedited
but often times the public
federal defense will object to
that which is the right sure
and we have to- exceed two well
good luck with that judge I
know you'll come up with the
system that do it is fair about
honoring it and we use that
term- so the any. You could fit
I not read the case. Fifth
Amendment violation. Which is
interesting burst forth ma'am
but many of it so appreciate
you bring that to our
attention- the purchase of
systems. A I. systems I hadn't
thought about that but that
that's another. Important point
to is this the how we're going
to provide that kind of
transparency but still- not
reveal certain investigative
techniques that may alert bad
guys and gals. As to where
we're we're going that's that's
another tension that that has
been illustrated here today-
and then sailed the sale that's
why I was asking the question
about. You know the collection
of data so whether or you know
somebody collects data. And
then can sell it that is very
very concerning to me and I
think probably the other.
Legislators as well so- I'm
gonna turn the person tanker
for her concluding comments
and- an- agency in just a
second but this is been more.
Valuable for me thanks for step.
Again the arms wrapped on this
is incredibly important issue
that were- as we will
straighten the last hearing
that we had we're way behind as
a state in in in addressing it
so let me let me turn it over
to send a check thank you and
again I want to think Mr men
for convening. It is. Important
to make an I am definitely
going to be paying attention to
the transparency aspects of
the- the selling of data- and I
already have some ideas in
terms of. What we can do to
ensure transparency especially
of how they are collecting the
data. And being sold and- you
know what platforms are being
used to collect data I think
that- you know in and I'm just
thinking out loud. Some people
like only guy slings anything.
But in terms of how the data is
gathered who's selling it and-
I think we need to have some
type of the trail so that we
can. I'm sure that it is being-
gathered in nothing up way- but
it is a very complex issue and-
I'm hoping that Mister chair
will be convening a more of
these hearings because I think
it's important to gather public
input as well so. Thank you so
much. M. with that we're going
to conduct. Thank you so much
specially to members of the
panel Preciado thank you Sir.
One thank reporter there yeah