Assessing the business risk of AI bias
AI
doesn’t
get
better
than
the
data
it’s
trained
on. This
means
that
biased
selection
and
human
preferences
can
propagate
into
the
AI
and
cause
the
results
that
come
out
to
be
skewed.
AI
doesn’t
get
better
than
the
data
it’s
trained
on. This
means
that
biased
selection
and
human
preferences
can
propagate
into
the
AI
and
cause
the
results
that
come
out
to
be
skewed.
In
the
US,
authorities
are
now
using
new
laws
to
enforce
instances
of
discrimination
due
to
prejudicial
AI,
and
the
Consumer
Financial
Protection
Bureau
currently
investigates
housing
discrimination
due
to
biases
in
algorithms
for
lending
or
housing
valuation.
“There
is
no
exception
in
our
nation’s
civil
rights
laws
for
new
technologies
and
artificial
intelligence
that
engage
in
unlawful
discrimination,”
said
its
director
Rohit
Chopra
recently
on
CNBC.
And
many
CIOs
and
other
senior
managers
are
aware
of
the
problem,
according
to
an
international
survey
commissioned
by
Swedish
software
supplier
Progress.
In
the
survey,
56%
of
Swedish
managers
stated
they
believe
there’s
definitely
or
probably
discriminatory
data
in
their
operations
today,
while
62%
also
believe
or
think
it’s
likely
such
data
will
become
a
bigger
problem
for
their
business
as
AI
and
ML
become
more
widely
used.
Elisabeth
Stjernstoft,
CIO
at
Swedish
energy
giant
Ellevio,
agrees
that
there’s
a
risk
of
using
biased
data
that’s
not
representative
of
the
customer
group
or
population
being
looked
at.
“It
can,
of
course,
affect
AI’s
ability
to
make
accurate
predictions,”
she
says.
“We
have
to
look
at
the
data
on
which
the
model
is
trained,
but
also
at
how
the
algorithms
are
designed
and
the
selection
of
functions. The
bottom
line
is
the
risk
is
there,
so
we
need
to
monitor
the
models
and
correct
them
if
necessary.”
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