An Introduction to Responsible AI

Artificial
intelligence
has
emerged
as
one
of
the
major
driving
forces
for
transformation
of
apps
and
services
available
on
different
platforms.
Billions
of
AI
users
across
different
fields,
ranging
from
education
to
business
and
healthcare,
have
capitalized
on
the
potential
of
artificial
intelligence
for
digital
transformation.
What
is
the
reason
to
look
for
a
responsible
AI
guide
at
a
time
when
the
AI
revolution
is
gaining
momentum?

Therefore,
leading
companies
working
with
AI
must
find
ways
to
ensure
that
the
benefits
of
AI
outweigh
the
negative
consequences.
It
can
ensure
that
AI
offers
helpful,
trusted,
and
safe
experiences
for
businesses
as
well
as
customers.
Responsible
AI
focuses
on
the
societal
impact
of
the
development
and
scalability
of
AI,
including
the
potential
advantages
and
setbacks.
Let
us
learn
more
about
responsible
artificial
intelligence
and
how
you
can
implement
it.

Definition
of
Responsible
Artificial
Intelligence

Responsible
AI
guide
is
a
unique
approach
to
development
of

AI
applications
from
a
legal
and
ethical
perspective.
The
answer
to
‘What
is
a
responsible
AI?’
revolves
around
employing
artificial
intelligence
in
an
ethical,
safe,
and
trustworthy
way.
The
responsible
use
of
artificial
intelligence
can
boost
transparency
and
reduce
issues
such
as

AI
bias.
Standard
approaches
for
governance
of
AI
best
practices
can
help
businesses
ensure
that
AI
programming
is
explainable,
human-centric,
and
interpretable.
Responsible
artificial
intelligence
systems
can
ensure
transparency,
fairness,
and
reliability.

The
onus
of
creating
credible
AI
standards
comes
on
developers
and
data
scientists
responsible
for
developing
and
deploying
the
AI
models
of
a
business.
Most
of
the
responsible
artificial
intelligence
examples
show
that
the
steps
for
ensuring
transparency
and
preventing
discrimination
in
AI
systems
differ
from
one
company
to
another.

In
addition,
the
implementation
approaches
can
vary
from
company
to
company.
For
instance,
the
team
responsible
for
development,
implementation,
and
monitoring
of
an
organization’s
responsible
AI
framework
can
be
different
across
various
companies.
Best
practices
for
responsible
AI
implementation
ensure
that
organizations
document
how
they
implemented
responsible
AI
and
addressed
the
requirements
of
accountability.

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to
learn
the
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of
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Business
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Importance
of
Responsible
Artificial
Intelligence

The
next
important
highlight
in
guides
to
learn
responsible
AI
is
the
importance
of
responsible
artificial
intelligence.
As
of
now,
responsible
AI
is
still
a
concept
that
is
being
considered
for
refining
AI
governance.
It
is
an
umbrella
term
that
involves
AI
democratization
and
ethics.
You
must
know
that
the
training
data
for

machine
learning
models
may
incorporate
bias
in
AI
systems.

It
can
be
a
result
of
faulty
or
incomplete
data
or
due
to
the
biases
of
the
individuals
responsible
for
training
the
model.
Biased
AI
programs
can
have
a
negative
impact
on
humans.
For
example,
AI
models
in
healthcare
can
present
inaccurate
diagnoses
of
patients.
Similarly,
AI
models
used
in
financial
services
can
reject
people’s
loan
applications
without
any
specific
reason.

As
the
adoption
of
solutions
with
AI
capabilities
becomes
more
common,
it
is
important
to
expand
the
scope
of
responsible
artificial
intelligence.
With
responsible
AI
guide,
businesses
can
not
only
reduce
AI
bias
but
also
increase
trust
and
transparency
in
AI
systems.
The
necessity
of
responsible
artificial
intelligence
is
clearly
visible
in
the
transformative
impact
of
AI
on
different
industries
and
how
it
solves
crucial
real-world
challenges.
It
is
important
to
come
up
with
AI
systems
that
work
for
everyone.

Responsible
AI
encourages
a
culture
of
responsible
innovation
by
empowering
AI
developers
and
decision-makers
to
follow
ethical
considerations.
It
can
help
them
find
new
and
innovative
ways
to
help
you
make
the
most
of
AI.
Most
important
of
all,
businesses
can
use
responsible
AI
as
a
value
proposition
for
enhancing
trust
of
customers
in
AI.

The
biggest
threat
to
the
adoption
of
AI
systems
in
business
use
cases
is
the
lack
of
trust.
If
businesses
choose
AI
products
that
align
with
principles
of
responsible
AI,
then
they
can
garner
the
trust
of
users.
The
most
prominent
advantage
of
using
responsible
AI
is
the
assurance
of
safer
and
more
accountable
products.

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to
understand
the
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What
are
the
Most
Noticeable
Principles
of
Responsible
AI?

Artificial
intelligence
and
machine
learning
models
might
have
to
follow
different
principles
that
vary
from
one
business
to
another.
However,
it
is
reasonable
to
have
doubts
regarding
responsible
AI
principles
and
identifying
the
best
ones
for
your
business.
For
example,
you
can
come
across
a
distinct
set
of
principles
for
responsible
artificial
intelligence
by
Microsoft
and
Google.

On
top
of
that,
the
National
Institute
of
Standards
and
Technology
(NIST)
has
also
published
an
AI
Risk
Management
Framework
that
includes
most
of
the
principles
of
Microsoft
and
Google.
Here
is
an
overview
of
the
top
principles
recommended
by
Google
for
responsible
artificial
intelligence
development.

• Social
  Utility

The
foremost
highlight
among
principles
in
a
responsible
AI
guide
is
the
social
utility
of
AI.
Advancements
in
artificial
intelligence
would
have
a
major
transformative
impact
across
different
industries,
including
healthcare,
manufacturing,
energy,
entertainment,
transportation,
and
security.
It
is
important
to
consider
the
social
and
economic
factors
in
the

use
cases
of
AI
technologies
to
check
whether
the
potential
benefits
outweigh
the
setbacks
and
risks.

Artificial
intelligence
also
offers
improvements
in
the
ability
to
understand
the
meaning
of
content.
Developers
must
ensure
that
users
get
high-quality
and
accurate
information
while
respecting
the
cultural,
legal,
and
social
norms
of
the
country
where
they
have
to
deploy
AI
systems.

• Bias
  Mitigation

Another
notable
principle
for
responsible
AI
is
bias
mitigation.
Most
of
the
responses
to
“What
is
a
responsible
artificial
intelligence?”
revolve
around
reducing
bias.
AI
algorithms
and
training
data
can
reinforce,
reflect,
and
reduce
unfair
biases.
However,
it
is
not
simple
to
differentiate
between
fair
and
unfair
biases
as
they
can
differ
significantly
across
different
societies
and
cultures.

Therefore,
it
is
important
to
pay
attention
to
avoiding
unjust
impact
of
AI
on
people,
particularly
the
ones
who
have
sensitive
characteristics.
AI
systems
must
be
designed
to
treat
people
equally
and
positively
irrespective
of
race,
gender,
nationality,
ethnicity,
sexual
orientation,
political
beliefs,
religious
beliefs,
and
income.

The
list
of
principles
for
designing
responsible
AI
systems
also
includes
safety.
Developers
must
implement
strong
security
and
safety
practices
to
address
the
concerns
of
unintended
results
that
may
increase
the
risks
of
harm
to
users.
Most
of
the
responsible
AI
examples
show
that
developers
must
create
AI
systems
with
caution
in
accordance
with
best
practices
for
AI
security.
On
top
of
that,
developers
must
also
verify
the
security
of
AI
technologies
in
constrained
environments
and
ensure
the
monitoring
of
their
operation
after
deployment.

• Accountability

The
development
of
AI
systems
must
also
align
with
the
principle
of
accountability.
Developers
can
create
the
designs
of
AI
systems
with
an
emphasis
on
responsible
AI
by
enabling
relevant
opportunities
for
feedback
and
appeal.
The
AI
system
must
also
provide
a
detailed
explanation
of
its
working
mechanisms.
Responsible
AI
systems
must
also
be
subject
to
direct
human
control
and
direction.

Discussions
about
responsible
AI
would
be
incomplete
without
drawing
attention
to
the
implications
of
privacy.
It
is
one
of
the
most
noticeable
responsible
AI
principles
for
developing
and
using
AI
technologies
in
different
sectors.
Developers
must
create
AI
systems
that
offer
the
opportunity
to
obtain
consent
of
users
and
notify
them
about
the
data
being
collected.
In
addition,
AI
systems
must
also
adopt
architectures
that
feature
privacy
safeguards
and
offer
comprehensive
control
over
data
usage.

• Scientific
  Excellence

The
list
of
principles
for
responsible
AI
development
by
Google
also
includes
the
necessity
of
safeguarding
standards
for
scientific
excellence.
Technological
innovation
in
the
domain
of
AI
is
possible
only
through
commitment
to
collaboration,
open
inquiry,
integrity,
and
intellectual
rigor.

AI
tools
have
also
shown
the
potential
to
open
new
avenues
of
scientific
research
and
knowledge
in
prominent
fields
such
as
biology,
medicine,
environmental
sciences,
and
chemistry.
Therefore,
AI
systems
must
commit
to
the
highest
levels
of
scientific
excellence
to
ensure
that
they
work
according
to
people’s
expectations.

• Availability

Another
notable
entry
in
the
principles
for
responsible
AI
is
availability.
Anyone
who
wants
to
learn
responsible
artificial
intelligence
must
recognize
that
AI
has
multiple
use
cases.
It
is
important
to
limit
the
negative
or
potentially
harmful
applications
of
AI.
What
are
the
factors
you
must
consider
to
have
responsible
AI
systems
available
to
users?
It
is
important
to
check
for
the
primary
objective
and
utility
of
AI
systems
alongside
their
nature
and
unique
highlights.
On
top
of
it,
you
must
also
consider
the
scale
of
the
technology
and
its
impact
on
users.

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Understand
What
Responsible
AI
Does
Not
Mean

The
scope
of
responsible
AI
is
not
all
about
adhering
to
the
principles
and
standards
followed
in
a
specific
business
or
industry.
It
is
also
important
to
ensure
that
responsible
artificial
intelligence
also
avoids
the
activities
it
was
never
meant
to
do.
In
some
cases,
technology
can
cause
some
form
of
harm.
However,
responsible
AI
design
ensures
that
the
benefits
of
AI
systems
would
outweigh
the
risks.

AI
should
never
be
used
for
weapons
or
other
technologies
which
aim
at
injuring
people.
Artificial
intelligence
should
not
be
used
for
technologies
that
collect
or
use
information
for
surveillance
by
breaking
internationally
accepted
norms.
AI
should
never
be
used
for
technologies
whose
objectives
can
contradict
the
widely
accepted
standards
for
international
human
rights
and
laws.

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Best
Practices
for
Designing
and
Implementing
Responsible
AI

Businesses
can
browse
through
long
lists
of
principles
from
different
sources
to
determine
the
ideal
path
for
designing
responsible
AI.
However,
the
scope
of
a
responsible
AI
guide
would
also
focus
on
defining
the
best
practices
for
designing
and
implementing
responsible
artificial
intelligence.
At
the
ground
level,
responsible
AI
design
must
follow
development
standards
that
align
with
design
of
responsible
AI.
The
principles
can
differ
from
one
organization
to
another,
and
it
is
important
to
account
for
the
differences.
It
is
important
to
develop
AI
with
resources
that
align
with
a
uniform
development
standard.
The
development
standard
must
focus
on
using
the
following
elements.

• Verified
  model
  architectures.
• Sharing
  of
  code
  repositories.
• Stability
  standards
  to
  support
  active
  machine
  learning
  models,
  thereby
  achieving
  desired
  functionality.
• Sanctioned
  variables.
• Proven
  bias
  testing
  methodologies
  that
  help
  in
  determining
  validity
  of
  testing
  for
  AI
  systems.

Artificial
intelligence
models
should
be
developed
with
concrete
goals
that
focus
on
development
in
an
ethical,
trustworthy,
and
safe
manner.
Here
are
some
of
the
best
practices
for
implementation
of
responsible
AI.

Most
of
the
responsible
artificial
intelligence
examples
emphasize
the
necessity
of
ensuring
the
use
of
explainable
data.

It
is
important
to
develop
a
diverse
work
culture
alongside
promoting
constructive
discussions
to
support
bias
mitigation.

Responsible
AI
systems
must
also
tailor
their
documentation
design
and
decision-making
processes
in
a
way
that
reverse
engineering
can
help
identify
sources
of
mistakes.

The
development
of
responsible
artificial
intelligence
must
also
emphasize
a
rigorous
development
process
that
prioritizes
visibility
in
the
latent
features
of
each
application.

Businesses
must
also
prioritize
responsible
artificial
intelligence
principles
and
utilize
interpretable
features
that
help
in
creating
human-understandable
data.

Another
noticeable
best
practice
to
ensure
the
development
of
responsible
AI
is
the
need
for
elimination
of
general
black-box
AI
model
development
methods.
On
the
contrary,
AI
systems
must
follow
a
white-box
development
model
that
provides
a
clear
explanation
for
all
AI
system
decisions.

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Need
for
Standardization
of
Responsible
AI

You
can
come
across
multiple
responses
to
“What
is
a
responsible
artificial
intelligence?”
as
the
answer
would
differ
from
company
to
company.
However,
the
lack
of
standardization
in
definition
of
responsible
AI
and
AI
regulations
is
a
formidable
challenge
for
AI
adoption.
Therefore,
it
is
important
to
bring
standard
frameworks
that
can
govern
the
use
of
AI
systems
by
businesses
and
consumers
worldwide
without
conflicts.

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first
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artificial
intelligence
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Flashcards

Final
Words

The
review
of
important
principles
associated
with
responsible
artificial
intelligence
and
its
significance
reveals
that
it
is
an
inevitable
necessity.
Responsible
AI
can
help
in
improving
the
trust
of
customers
in
AI
systems.
It
can
also
help
businesses
improve
transparency
of
AI
systems.
Businesses
can
create
responsible
AI
systems
to
address
the
concerns
of
accountability,
privacy,
bias,
and
safety
in
use
of

artificial
intelligence.

Tech
giants
such
as
Microsoft
and
Google
have
come
up
with
their
distinct
sets
of
principles
for
responsible
AI.
In
the
long
run,
businesses
should
expect
a
standard
governance
framework
for
developing
responsible
artificial
intelligence
systems.
Find
more
details
about
the
use
of
responsible
AI
and
its
implications
for
the
future
right
away.