The
potential
for
AI
is
growing,
but
technology
that
relies
on
real-live
personal
data
requires
responsible
use
of
that
technology,
says
the
International
Association
of
Privacy
Professionals.
The
use
of
AI
is
predicted
to
grow
by
more
than
25%
each
year
for
the
next
five
years,
according
to
PricewaterhouseCoopers.
“It
is
clear
frameworks
enabling
consistency,
standardization,
and
responsible
use
are
key
elements
to
AI’s
success,”
the
IAPP
wrote
in
its
recent
Privacy
and
AI
Governance
report.
Responsible
AI
is
a
technological
practice
centered
around
privacy,
human
oversight,
robustness,
accountability,
security,
explainability
and
fairness.
However,
80%
of
surveyed
organizations
have
yet
to
formalize
the
choice
of
tools
to
assess
the
responsible
use
of
AI.
Organizations
find
it
difficult
to
procure
appropriate
technical
tools
to
address
privacy
and
ethical
risks
stemming
from
AI,
the
IAPP
wrote
in
the
report.
While
organizations
have
good
intentions,
they
do
not
have
a
clear
picture
of
what
technologies
will
get
them
to
responsible
AI.
In
80%
of
surveyed
organizations,
guidelines
for
ethical
AI
are
almost
always
limited
to
high-level
policy
declarations
and
strategic
objectives,
IAPP
said.
“Without
a
clear
understanding
of
the
available
categories
of
tools
needed
to
operationalize
responsible
AI,
individual
decision
makers
following
legal
requirements
or
undertaking
specific
measures
to
avoid
bias
or
a
black
box
cannot,
and
do
not,
base
their
decisions
on
the
same
premises,”
the
report
said.
When
asked
to
specify
“tools
for
privacy
and
responsible
AI,”
34%
mentioned
responsible
AI
tools,
29%
mentioned
processes,
24%
listed
policies,
and
13%
cited
skills.
-
Skills
and
policies
include
checklists,
using
the
ICO
framework,
developing
and
following
playbooks,
and
using
slack
and
other
internal
communication
tools.
GRC
tools
were
also
mentioned
in
these
two
categories. -
Processes
include
privacy
impact
assessments,
data
mapping/tagging/segregation,
access
management,
and
record
of
processing
activities
(RoPA). -
Responsible
AI
tools
included
fairlearn,
InterpreML
LIME,
SHAP,
model
cards,
Truera,
and
questionnaires
filling
out
by
the
users.
While
organizations
are
aware
of
new
technologies
such
as
privacy
enhancing
technologies,
they
have
likely
not
yet
deployed
them,
according
to
the
IAPP.
PETs
offer
new
opportunities
for
privacy-preserving
collaborative
data
analytics
and
privacy
by
design.
However,
80%
of
organizations
say
they
do
not
deploy
PETs
in
their
organizations
over
concerns
over
implementation
risks.
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