►
Description
Join us to learn about the Incident Accuracy Reporting System (IARS), a Call for Code for Racial Justice Open Source project that captures eyewitness accounts and police reports and compares them to verify accuracy. IARS seeks to increase transparency & accountability in policing and to reduce police behavioral infractions by empowering eyewitnesses to submit their firsthand accounts. The Incident Accuracy Reporting System team will outline their project which utilizes blockchain and machine learning to flag inconsistencies between incidence accounts from eyewitnesses and police reports. They will discuss the motivation for this project and how you can contribute.
Sign up here to join the community and get started in Github: https://ibm.biz/OSTVJuly27
Twitch: https://red.ht/twitch
A
Good
morning
good
afternoon
good
evening
and
welcome
to
our
very
special
call
for
code
for
racial
justice,
miniseries
here
on
red
hat,
live
streaming.
I
am
chris
short
host
and
showrunner
of
all
the
things
on
this
channel.
It
feels
like
sometimes
but
I'm
joined
by
the
wonderful
team
from
ibm
working
on
call
for
code
for
racial
justice,
and
I
will
hand
it
off
to
sabine
to
do
the
introductions
and
such
thank
you
sabine.
Awesome.
B
B
So
in
the
past
I
haven't
given
a
whole
lot
of
huge
background
on
call
for
code,
or
even
you
know,
call
for
co-facial
justice,
but
I
really
wanted
to
you
know
level
that,
especially
at
this
time,
because
call
for
code
as
a
whole
is
a
program
that
exists
outside
of
ibm.
You
know
it
is
hosted
by
david
clark,
cause
and
ibm
is
really
the
founding
partner
of
this
initiative
and
provides
really
the
technical
expertise.
B
Alongside
you
know
the
linux
foundation
and
united
united
nation
human
rights,
so
this
year
we
actually
have
you
know
our
annual
competition,
which
is
coming
to
a
close
and
focuses
again
on.
You
know,
tech
for
good
and
being
able
to
develop
open
source
solutions
that
are
supposed
to
support
communities
in
need
next
slide.
B
So
it
really
focuses
on
inspiring
and
motivating
developers
to
upskill
themselves
and
to
use
applications
specifically
to
solve
some
of
the
real
world
problems.
You
know
that
we're
seeing
in
society,
so
the
projects
right
now
again
are
open
source
and
the
focus
is
that
everyone
should
be
able
to
access
it
but
again
being
able
to
use.
You
know
different
technologies
to
to
kind
of
critique
how
the
problems
can
be
solved
and
we're
really
excited
to
you
know
show
what
that
looks
like
on
the
next
slide.
B
So
call
for
code-
and
this
also
really
you
know-
speaks
to
our
community
as
a
whole-
is
around
being
able
again
to
produce
solutions
that
affect
communities
in
need
and
the
priorities
that
we
see,
but
it
also
is
really
emphasizing.
How
can
you
build
skills
around
development?
How
can
we
engage
community
members?
B
Sorry,
my
dog
engaged
community
members
to
to
want
to
participate
in
this
work,
but
also
collaborating
with
them
so
that
they
can,
you
know,
be
a
part
of
the
solutions
and
taking
ideas
into
action.
So
most
hackathons
or
even
you
know
typical
hackathons
focus
on
all
right,
we're
giving
you
this.
You
know
use
case
go
at
it.
You
know
we're
gonna,
give
you
maybe
a
week
or
you
know
a
couple
of
days,
and
you
know
best
of
luck.
It's
not
just
about
being
able
to
produce
a
solution.
B
It's
saying
at
the
end
of
this.
How
can
we
actually
implement
this
in
the
world
as
it
stands,
and
so
for
this
year,
there's
a
200
000
grand
prize
that
comes
from
the
global
challenge,
where,
if
your
solution
is
selected,
you
are
able
to
actually
build
it
out
along
the
right
type
supports
with
ibm
and
a
couple
of
other
partners
that
we
always
have
on
hand
slide.
B
So
submissions
again,
are
you
know,
coming
to
a
close
july
31st
if
you
have
been
working
on
a
project
already
get
really
geeky
and
excited
to
submit
it,
and
you
can
explore.
You
know
how
you
submit
your
your
application
right
now,
but
we
really
want
to
just
focus
again.
This
session
is
on
climate
change
and
you
should
be
able
to
register
in
the
steps
that
you
see
below
it's
pretty
simple
we've,
given
you
three
of
them
and
you
should
be
ready
to
to
participate
all
right
next
slide.
B
And
so,
if
you
go
to
the
next
slide,
you'll
get
a
little
bit
more
of
an
understanding
of
how
we
got
here
and
what
are
kind
of
our
guiding
principles
or
pillars,
as
we'd
like
to
call
it
for
this
period
of
time
that
we
were
in.
So
we
have
police
judicial
reform
and
accountability,
diverse
representation
and
policy
and
legislation
reform
that
really
helped
to
frame
how
the
solutions
that
are.
You
know
in
the
series
right
now
as
well.
B
As
you
know,
further
ones
that
are
being
developed
really
are
addressing
some
of
the
critical
needs
within
these
segments
and
so
incident
accuracy.
Reporting
system
actually
falls
within
police,
judicial
reform
and
accountability,
and
we're
excited
to
share
a
little
bit
more
overview
of
the
solutions
that
are
to
come
next
slide.
B
So
here
we
are
so
far
we
have,
you
know,
been
able
to
see
a
couple
of
our
of
previous
solutions
and
right
now
again,
instant
accuracy.
Reporting
system
is
a
platform
that
allows
for
collaboration
and
corroboration
for
witnesses
and
victims
who
have
evidence
during
a
police
encounter
and
we're
gonna
dive
right
into
it.
I'm
not
gonna,
you
know,
try
and
do
it
any
injustice
by
giving
you
a
quick
summary.
We're
gonna
have
a
really
good,
deep
dive.
B
You
know
by
the
rest
of
the
team
and
they'll
also
be
able
to
introduce
themselves.
You
know
as
we're
moving
further,
so
you
can
see.
Yes,
we've
been
work.
This
is
actually
I
don't
even
wanna
call
it
a
volunteer
effort,
but
an
active
project
that
everyone
on
this
call
is
working
on.
In
addition
to
your
day,
jobs,
and
if
you
know
anything
about,
you
know
the
work
that
you
do
in
your
daily
life,
especially
in
tech.
It's
a
lot.
So
definitely
wanna.
B
You
know,
congratulate
and,
and
always
I'm
thanking
the
team
for
their
committed.
You
know
dedication
to
these
work
to
this
project
and
I
think
that
that
is
all
of
the
talking
I'll,
probably
interject,
with
some
more
questions.
Comments
and
you
know
clarifying
statements
as
we
move
through
here,
but
I'm
going
to
go
ahead
and
hand
it
off.
C
However,
what
we
notice,
of
course,
due
to
the
media.
The
recent
media
reports
is
that,
despite
some
of
this
being
captured
on
cell
phone
cameras
or
some
of
these
incidents
even
being
captured
by
the
officer's
own
equipment,
like
their
body
camera
or
their
dash,
cam
footage,
we
see
that
incidents
and
information
surrounding
this
isn't
always,
as
reported,
isn't
always
reported
as
a
witness.
I
should
say
so.
Let
me
give
an
example.
C
C
So,
unfortunately,
when
we
see
these
kind
of
things,
we're
wondering
well
what
exactly
goes
into
the
final
police
report
that
is
given
that
is
given
by
the
police
and,
ultimately,
how
do
we
have
information
that
is
contradictory
from
what
was
actually
captured
from
the
final
police
report.
So
this
has
left
many
people
asking
for
transparency
on
how
incident
reports
are
even
generated
and,
furthermore,
the
accuracy
of
those
written
reports,
as
ultimately
only
officers
have
the
final
say
so
over
what's
captured
in
the
report.
C
So
after
last
summer's
call
for
coding,
a
call
for
code
for
racial
injustice
came
in.
We
all
decided
that
we
want
to
answer
the
call
and
be
able
to
tackle
issues
just
like
this.
So
this
is
the
members
of
our
team.
All
the
members
unfortunately
aren't
here
today,
but
the
ones
that
are
here.
I
think
we
can
all
introduce
ourselves.
C
So
start
myself
highlight
it.
This
is
myself
shonda
witherspoon
and
I
was
a
user
research
first
user
researcher
on
this
project.
So
I
was
tasked
with
being
able
to
do
interviews
with
police
officers,
police
complaint
officers
and
various
members
of
the
police
community
in
order
to
get
a
better
understanding
of
how
incident
reports
were
generated
and
how
our
system
could
be
able
to
handle
the
correct
pipeline
and
develop
a
solution
that
would
be
able
to
add
some
transparency
and
validate
accuracy
that
goes
into
a
police
report.
C
C
Yes,
hi,
I'm
felicia
willispoon.
I
also
contributed
on
the
project
I
assisted
as
a
as
a
system
architect
and
also
helped
with
user
testing.
So
I
helped
with
designing
diagrams
for
the
components
of
the
system,
as
well
as
seeing
how
users
can
interact
with
a
prototype
of
the
application
next
I'll
pass
it
over
to
aviola,
highlight
here
in
the
powerpoint,
so
aviala,
can
you
introduce
yourself
in
your
role,
hi.
D
E
C
Here
we
have
our
leader
osai,
so
he
responsible
for
the
team
meetings
and
having
everyone
organized
presenting
weekly
on
our
progress
to
the
call
for
co
team
and
getting
feedback
that
we
could
work
on
through
each
week's
iteration
and
then
finally,
in
our
team
was
debra.
Zebra's
role
was
a
generalist
which
means
that
she
worked
on
whichever
aspect
that
we
needed
help
with.
So
if
we
needed
assistance
with
user
research
or
working
on
diagrams,
then
we
could
rely
on
denver
for
that.
C
C
Any
accuracy
checks.
So
this
led
to
us
developing
hills
which
eventually
turn
into
solutions
which
is
the
following.
We
want
to
make
a
way
so
that
internal
affairs
and
civilians,
such
as
witnesses,
could
both
contribute
to
incident
reports
by
creating
a
tamper-proof
record
with
all
accounts
of
the
incident.
C
F
F
So
we
conducted
a
first
hand
interviews
and
they
were
based
on
our
scenario
with
police
complaint
officers,
dispatchers
and
other
members
of
the
police
force.
This
research
allowed
us
to
build
a
workflow
of
the
reports,
but
also
to
understand
the
feasibility
of
our
solution.
We
interviewed
a
police
officer
who
used
to
work
in
internal
affairs
to
see
how
our
solution
will
work
in
the
real
world
and
lastly,
and
not
the
and
the
most
important
part
was
to
understand
how
civilians,
big
team
witnesses
would
interact
with
our
application.
F
So
we
have
an
interfaith
that
it's
for
the
individuals,
the
witness
the
victim
to
report,
information
or
data
related
to
the
incident,
and
then
we
have
the
manual
or
automated
flagging.
That
is
for
inconsistencies
and
inaccuracies
that
are
in
the
initial
reports,
based
on
all
the
collected
data.
Also,
it
girls
reference
the
record
data
with
the
user,
with
the
officer
history
and
misconduct
and
a
backend
to
a
blockchain
intent
that
has
the
actual
document
store
in
an
object
store.
F
F
So
how
do
we
picture
all
these
things
flow
together?
We
imagine
that
a
victim
calls
9-1-1,
so
pco
questions
about
the
emergency
asks
of
what
they
saw.
What
happened
and
if
there
is
any
missing
information
in
that
part,
is
going
to
be
reported
and
the
recording
is
not
going
to
be
uploaded
to
the
system.
Then,
when
the
police
officer
arrives
to
the
scene,
they
enter
the
information
in
the
computerized
dispatch
when
the
police
interviews
the
victim
and
the
witnesses
they
take
notes
and
also
records
information
in
that
system.
F
So,
lastly,
the
systems
with
all
the
upload
information
is
processed
and,
for
instance,
if
the
interview
with
the
witness
conflicts
with
the
written
report
or
any
incontinence
inconsistencies
are
shown,
this
is
going
to
be
sent
to
the
supervisor
or
sergeant
so
the
alerts
are
or
shown
in
the
dashboard
are
shown
in
the
dashboard,
but
are
also
sent
by
email
or
other
things,
and
these
allow
us
to
present
all
these
information
to
the
people
that
can
do
something.
So
this
is
how
we
imagine
the
workflow
of
our
solutions
going
to
work.
B
C
Yes,
actually
so,
this
was
developed
in
many
different
iterations
before
we
came
up
with
this
ultimate
solution,
workflow.
So
first
I
was
able
to
interview
with
various
roles
in
the
miami-dade
police
department,
as
I
mentioned
earlier,
and
what
they
basically
helped
me
work
with
was
a
flow
diagram
that
had
many
different
scenarios
based
on
what
happens
when
a
person
calls
9-1-1
how
that
leads,
when
a
police
complaint
officer
takes
the
call
and
assigns
it
an
incident
number,
what
happens
when
an
officer
arrives
on
the
scene?
C
How
was
the
incident
reports
written,
etc?
And
after
we
went
through
those
different
iterations?
I
would
speak
to
another
member
of
the
police
department,
such
as
a
police
dispatcher,
who
would
add
on
to
that
work,
direct
flow
diagram,
and
once
we
had
that
information
captured,
I
confirmed
that
information
with
a
police
supervisor
which
ultimately
led
to
the
next
step,
which
would
be
once
the
report
is
written.
F
Now
and
when
we
interviewed
the
police
officer
that
used
to
be
working
he's
an
ibm,
but
he
used
to
work
in
general
affairs.
He
also
explained
to
us
what
it
was
needed,
so
the
the
inconsistencies
or
the
problems
or
the
misconduct
of
the
police
officer
could
be
tackled
in
real
life.
So
we
also
knew
that
little
bit
of
information
to
help
our
application.
D
D
D
D
We
use
blockchain
to
ensure
that
the
the
information
that
is
stored
is
is
secure
and
and
then
we
did
some
post-processing
before
storing
the
information
right
like
if
the.
If
it
was
a
call
that
was
logged
in
or
a
video
or
audio,
you
would
use
speech
to
text
to
start
to
convert
that
or
a
language
processing
if
it's
non-english.
D
D
It
shows
the
flow
of
how
this
would
work
right,
so
a
user
would
input
since
information
into
the
system
documents
audio
video,
this
this
video
audio
is
processed.
Like
I
mentioned
earlier
speech
to
text
language
translation,
we
we
used
the
technologies.
We
used
open
source
technologies
like
for
blockchain.
We
used
hyperledger
fabric.
D
This
also
shows
an
operational
model
where
a
witness
or
victim
how
they
engage
with
the
system
right,
interacting
with
the
police
calling
directly
to
log
information
into
the
system,
uploading
their
video
audio
and
then
the
technologies
that
were
mentioned
earlier.
So
this
shows
a
high
level
of
our
system
operational
model,
wise,
and
that
should
be
all
of
it.
Next,
we'll
have
the
demo.
Unless
there
are
any
questions.
C
G
G
G
So
if
I
go
on
and
navigate
to
set
review,
so
this
is
a
different
section
of
the
dashboard.
So
essentially,
this
is
a
witness
report
form
and
basically
this
section
will
be
publicly
accessible
and
while
for
it
anyone
to
report
a
particular
incident
they
may
have
had
with
a
with
a
police
officer,
so
they
can
specify
the
location,
the
officer
name
or
badge
number
and
they
can
describe
the
incident
in
their
own
words.
G
G
So
after
clicking
submit,
then
the
audio
file
will
then
be
transcribed
through
the
watson
speech
to
text
service,
and
then
we
can
also
see
details
for
each
report,
so
we
can
see
the
transcript
and
then
we
can
also
and
then
also
in
the
background.
The
raw
file
is
stored
in
the
ibm
cloud
object,
storage
service.
G
So
essentially
we
will
take
a
hash
of
that
audio
file
and
store
that
hash
and
all
associated
metadata
on
the
blockchain.
So
if
there's
any
case
where
the
original
file
is
at
all
tampered
within
the
cloud
object,
storage
section,
then
that
hatch
wouldn't
match
anymore
and
that
would
raise
a
flag
anyway.
So
that
concludes
our
demo
and
I
hope
you
enjoyed
watching.
E
E
The
ibm
technologies
had
the
watson
speech
to
text,
as
demonstrated
just
now
by
kalanji,
and
then
we
have
the
watson
language
translator,
because
we
could
have
witnesses
who
are
speaking
languages
other
than
english.
Then
we
also
have
the
watson
studio,
ibm
watson,
studio
for
the
machine
learning
side
of
the
project.
G
E
Then
these
are
the
open
source
technologies,
the
vue.js
for
the
front-end
development
we
have
docker
for
containerization.
We
have
ffmpeg
for
video,
recording
and
video
audio
processing.
Then
we
have
blockchain
to
make
the
system
tamper.
Proof
then
circuit
land
for
the
for
developing
the
machine
learning
algorithm.
Our
next
slide.
E
So
what
happens?
Is
that
when
we
have
all
these
statements
submitted
by
the
witnesses,
so
we
want
to
bring
them
together
and
we
want
to
see
if
there
are
statements
from
if
there
are
witnesses.
There
are
consistent
that
have
consistent
statements.
You
know
so
in
a
particular
location.
There
could
be
multiple
events,
so
we
want
to
identify
those
who
are
talking
about
the
same
thing
and
you
know
bring
them
together
and
when
we
identify
those
who
are
reporting
the
same
issue,
then
we
compare
that
to
the
police.
E
Reports,
as
the
case
may
be
so
what's
happening
here,
is
that
we
extract
certain
features
from
those
statements
features
that
will
help
us
to
identify
how
similar
those
statements
are.
So
we
use
a
feature
called
tf
idf
that
same
frequency
inverse
document
frequency
that
helps
us
to
bring
the
main
important.
E
You
know
things
out
of
those
statements,
then
we
after
we
remove
the
stop
words,
convert
them
to
word
vectors
and
centroids
are
initialized
and
the
ones
that
are
closer
to
a
particular
centroid
will
go
to
that
cluster.
So
I
believe
I've
explained
that
if
you
have
more
questions,
we
can
look
at
that
later.
Next
slide.
E
So
here
we
see
some
of
these
things
have
been
described
by
kalanji,
so
we
have
a
report.
It
goes
into
the
database.
Then
we
identify
those
reports
with
matching
metadata.
What
are
the
metadata
collected,
the
date,
the
location
and
probably
the
kind
of
incident
that
occurred
so
when
we
see
those
we
bring
them
together
and
then
you
know
we
put
them
into
the
cluster
and
see
if
their
descriptions
are
also
saying
the
same
thing.
So
here
this
yellow
circle
is
talking
about
a
cluster.
Those
events
are
talking
about
the
same
thing.
E
E
E
So
this
tells
us
that
the
statement
of
the
police
is
not
you
know
is
not
is
far
away
from
what
the
witnesses
are
saying.
So
the
centroid
of
these
clusters
will
be
at
the
middle.
That
gives
you
an
idea
of
how
far
the
police
report
is
from
the
centroid
of
each
cluster.
The
centroid
of
the
cluster
is
at
the
center,
so
so
this
is
an
experiment.
E
It
doesn't
mean
that
this
is
the
way
it
is
always.
You
know
the
police
report
could
fall
inside
of
the
cluster.
You
know,
meaning
that
the
police
report
is
consistent
with
what
the
witnesses
are
saying,
but
in
this
case
of
this
simulation
for
this
report,
which
we
got
of
course
was
clearly
not
related
to
the
statements
even
by
visual
observation
and
the
k-means
algorithm
is
confirming
that.
So,
with
this
we
can
be
able
to.
You
know,
detect
some
of
those
inaccuracies
in
the
reports
submitted
by
the
weaknesses.
E
C
C
C
We
also
worked
with
the
office
of
the
state
attorneys
of
and
florida,
and
we
also
worked
with
the
united
way
of
nyc,
which
is
a
non-profit
organization,
that's
dedicated
to
helping
low-income
members
of
the
community
and
build
in
building
relationships
between
law
enforcement
and
the
community
so
to
design
thinking.
We
worked
with
an
empathy
map
to
help
us
more
identify
with
our
adopter,
based
on
the
help
of
the
subject
matter,
expert
organizations
and
antenna.
C
So
if
we
look
at
our
adopter,
which
could
be
police
departments
law
enforcement,
we
have
to
identify
four
different
areas
such
as
what
do
they
say?
What
do
they
think,
how
they
feel
and
what
do
they
do
and
so
for
summarizing?
What
we
found
when
working
with
the
smes
is
that
maybe
they
may
feel
that
this
system
could
be
expensive
due
to
the
the
ai
that's
embedded
in
our
system,
but
they
also
would
see
that,
of
course,
this
could
make
the
department
more
transparent.
C
C
So
what
we
think
we
imagine
the
doctors
would
think
would
be
that
it
could
help
towards
investigations
and
make
them
more
thorough
through
automation,
but
some
other
thoughts
might
be
how
to
determine
if
the
inconsistencies
are
from
transcription
errors-
maybe
not
just
from
you
know
intentionally
done
by
their
officers
and
then
one
of
those
important
things
to
consider
was
them
the
supervisors
thinking.
How
can
they
convince
the
officers
that
this
is
beneficial
for
them
and
not
a
tool?
That's
strictly
used
against
them,
so
that
way,
law
enforcement
would
be
our
main
adopter.
C
So
we
want
to
show
that
this
is
a
tool
that
can
help
them
help
them
clear
out
the
inconsistencies
that
may
be
in
their
port
either
intentionally
or
not,
and
can
help
them
do
this
automation
through
channel
affairs.
That
way
they
can
see
that
this
tool
would
be
useful
on
their
end.
So
that's
something
we
want
to
consider
in
our
empathy
map.
B
Question
yes
around
this,
so
how
did
you
think
about
you
know,
especially
when
we're
talking
about
and
being
inspired
by?
You
know,
I
don't
know
if
inspired
is
the
word
or
being,
I
guess,
prompted
by
the
george
floyd
incident,
wherein
the
the
young
lady,
who
recorded
the
incident,
you
know,
was
kind
of
attacked
after
the
fact
right
did
you
think
about
you
know
how
people
who
end
up
submitting
evidence
or
are
trying
to
you
know
be
upstanders
in
that
sense,
could
also,
you
know,
be
protected.
C
Yes
and
our
in
our
empathy
map-
that
was
definitely
one
of
the
highlights
that
we
had
considered,
as
I
mentioned
this
idea
of
the
privacy
being
protected
as
they
as
they
upload
this
information,
even
when
we
were
talking
when
we
did
when
me
and
lucia
did
use
their
research.
That
happened
to
be
one
of
the
many
topics
that
came
up
with
the
users.
They
definitely
enjoyed
the
system,
but
they
wanted
to
know.
C
You
know
the
feasibility
of,
however,
of
being
somewhat
anonymous
through
their
reporting,
so
so
so
yeah.
I
think
that
you
mentioned
a
very
important
part,
and
that
was
actually,
I
think
that
was
the
major
feeling
that
was
captured
in
this
in
in
our
empathy
map.
Was
this
the
privacy
to
the
through
the
citizens?
Will
they
be
believed
by
submitting
this
report?
It
would
even
would
this
still
be
enough
in
order
for
there
to
be
accountability
through
our
system,
right
agreed,
so
yeah,
so
very
good
question.
C
Okay,
so
now
we
want
to
work
on
the
prioritization,
which
basically
lists
on
axis
the
level
of
importance
of
some
of
the
features
that
we
want
in
our
system
and
then,
but
also
shows
the
feasibility
of
of
of
the
proposed
features
and
additions,
but
we
had
in
our
mvp
solution,
so
we
came
so
during
this
exercise
with
the
smes.
We
came
up
with
a
few
dimensions
that
we
recognized
that
we
took
based
on
the
empathy
map.
C
Then
we
looked
at
the
alerts
or
automation
of
their
internal
processes,
so
some
so,
for
example,
the
report
comes
in
and
there's
having
notifications.
If
there
are
inconsistencies,
we
want
that,
of
course,
to
be
automated,
and
then
we
want
the
ease
of
use
for
the
police,
such
as,
of
course,
seeing
the
value
about
this
automation
for
eternal
affairs,
but
also
one
of
the
big
things
we
want
to
highlight.
C
Of
course,
that
came
from
the
empathy
map
was
building
the
trustport
police
so
that
they
can
see
this
tool
how
this
school
can
benefit
them
and
once
again
not
be
a
tool
against
them
and
then
and
then
work
on
the
potential
of
the
cost
savings
based
on
the
technology.
That's
used,
that's
not
open
source
such
as
the
ai.
C
C
The
next
we
had
was
a
trust
per
citizen,
so
that's
the
the
orange
blocks
and
that's
basically
their
data
protection.
So
basically
what
sabine
had
just
asked
about
the
citizens
being
able
to
have
some
control
about
whether
they
could
be
anonymous
in
this
reporting
or
just
some
way
to
have
this,
the
trust
in
having
their
data
in
the
hands.
C
I
guess
of
the
police,
the
law
enforcement
in
their
system,
so
for
here
we
thought
about
some
secure
identifiers
for
witnesses
so
that
there's
no
pi
and
then
some
and
and
then
have
login
systems.
So
if
they
can
see
it,
so
we
can
see
if
law
analysis
reporting
is
feasible.
In
some
situations-
and
then
lastly,
we
want
but
not,
but
also
when
the
most
important
is
building
the
trust
for
the
police,
so
we
so
for
this.
We
looked
at
the
idea
of
having
clear
ai
explainability
of
why
or
report
it
consistent.
C
So
when
we
just
have
the
flagging
of
reports
and
this
sent
to
the
system,
we
want
to
have
the
you
know
the
the
ai
clearly
list
why
the
information
was
inconsistent,
so
a
police,
you
know,
supervisor
can
review
this
and
then
see.
You
know
highlight
see
what
the
ai
highlighted
and
be
rest
assured
that
it
wasn't
through
something
like
such
as
typos
or
consistencies,
and
if
it
was,
then
they
can
clearly
check
that
as
well.
C
C
C
Lastly,
this
goes
to
the
we
have
the
ai
explainability
and
transparency.
So,
for
example,
going
off
of
the
last
example,
which
was
building
trust
for
police.
We
want
a
clear
way
of
when
the
inconsistencies
are
done.
How
can
we
highlight
specific
areas
of
why
the
system
flag
these
inconsistencies
or
inaccuracies
that
were
reported
so
for
this?
We're
looking
at
some
details
of
our
scores
based
on
the
calculation
to
use
as
a
threshold,
we're
thinking
of
ways
to
make
a
link
part
of
the
recordings
of
where
they
ever
happened
or
flagging?
C
B
We
can
see
that
we've
done.
You
know
our
due
diligence
and
contacting
community
members,
police
officers
and
understand
kind
of
like
the
background
and
how
a
solution
like
this
would
be
useful,
but
we're
really
emphasizing
that
anyone
who
wants
to
learn
how
to
do
how
to
accomplish
some
of
the
the
items
in
the
roadmap
there's
opportunities
for
you
to
learn
to
do
that
through
our
community
and
there's
also
ways
for
you
to
be
able
to
teach.
B
So
if
you
have
some
of
the
skill
set
that
you
know
we're
requesting,
we
really
encourage
you
to
to
contact
us
again,
scan
this
qr
code
and
it'll.
Tell
you
how
you
can
begin
to
you
know,
get
to
know
us
a
little
bit
more.
You
can
join
our
slack
communion.
You
know
I'm
in
there
demi
aj
who's,
our
community
manager
is
also
you
know,
active
there
as
well.
B
B
About
it,
that's
all
we
have
for
today.
I
think
the
team,
usually
you
know,
peppered
a
lot
more
questions,
but
I
knew
that
they
were
gonna
get
to
especially
a
lot
of
the
thinking
that
went
into
creating
this,
which
I
think
is
always
going
to
be
the
core
to
you
know,
solutions
that
are
really
supposed
to
provide
transparency
and
accountability.
It's
like
who
have
you
talked
to.
How
have
we
validated
this?
Is
this
serious?
And
yes,
that's
all
those
questions.
C
A
Thank
you,
sireen.
Thank
you,
everybody
for
participating
in
this
project.
It
it
can
help
change
lives
right,
like
and
a
lot
of
outcomes
could
be
different
as
a
result
of
it.
So
please
I
encourage
you
to
join
if
you're.
If
you
have
time,
if
you
you
know,
want
to
get
started
in
open
source
software,
this
is
a
great
place
to
start.
If
your
experience
in
open
source
software,
this
is
a
great
place
to
continue.
A
B
C
Yes
and
then,
lastly,
all
the
the
the
road
map
that
I
mentioned,
all
the
rules
that
we
needed
in
the
coming
days
we'll
be
adding
those
as
issues
to
github.
So
all
of
anyone
who
wants
to
contribute
can
clearly
see
how
and
what
what
they
can
do
to
do
it
to
make
it
more
easier.
So
we
look
forward
to
as
much
help
as
we
can
get
and
new
contributors
to
this
project.
A
All
right,
folks,
that's
all
the
live
streaming
we
have
today,
but
be
sure
to
tune
in
if
you're
in
emea
it'll
be
around
10
a.m.
Your
time
tomorrow
for
the
openshift
coffee
break
and
then,
when
the
east
coast
wakes
up.
Please
join
me
for
the
level
up
hour
at
9
00
a.m.
Eastern,
and
with
that
I
will
bid
everyone
farewell
and
see
you
all
again
soon.