Legislation to rein in AI’s use in hiring grows
Organizations
are
rapidly
adopting the
use
of
artificial
intelligence (AI)
for
the
discovery,
screening,
interviewing,
and
hiring
of
candidates.
Organizations
are
rapidly
adopting the
use
of
artificial
intelligence (AI)
for
the
discovery,
screening,
interviewing,
and
hiring
of
candidates.
It
can
reduce
time
and
work
needed
to
find
job
candidates
and
it
can
more
accurately
match
applicant
skills
to
a
job
opening.
But
legislators
and
other
lawmakers
are
concerned
that
using
AI-based
tools
to
discover
and
vet
talent
could
intrude
on
job
seekers’
privacy
and
may
introduce
racial-
and
gender-based
biases
already
baked
into
the
software.
“We
have
seen
a
substantial
groundswell
over
the
past
two
to
three
years
with
regard
to
legislation
and
regulatory
rule-making
as
it
relates
to
the
use
of
AI
in
various
facets
of
the
workplace,” said
Samantha
Grant,
a
partner
with
the
law
firm
of
Reed
Smith.
States,
including
California,
Maryland,
and
Washington,
have
enacted
or
are
considering legislation to
put
rules
around
using
AI
for
talent
acquisition.
The
European
Union’s
EU
AI
Act
is
also
aimed
at
addressing
issues
surrounding
automated
hiring
software.
Congress
is
considering
the
federal
Algorithmic
Accountability
Act,
which,
if
passed,
would
require
employers
to
perform
an
impact
assessment
of
any
automated
decision-making
system
that
has
a
significant
effect
on
an
individual’s
access
to,
terms,
or
availability
of
employment.
In
addition,
the
US
Equal
Employment
Opportunity
Commission
(EEOC)
recently
announced
that
it
intends
to
increase
oversight
and
scrutiny
of
AI
tools
used
to
screen
and
hire
workers.
As
part
of
that
effort,
the
EEOC
held
a
public
hearing
Jan.
31
to
explore
the
potential
benefits
and
harms
of
AI
in
hiring
situations,
according
to
Grant.
“The
current
swell
of
laws
and
regulations
related
to
AI
in
HR
is
like
a
wave
under
the
water
—
building,
gaining
momentum,
and
getting
ready
to
come
ashore,”
said
Cliff
Jurkiewicz,
vice
president
of
Global
Strategy
at
Phenom,
an
AI-enabled
hiring
platform
provider.
“The
new
laws
are
necessary
and
welcomed
as
technology
has
outpaced
existing
regulations
for
protecting
underrepresented
groups.”
New
York
City
makes
a
move
One
of
the
latest
attempts
to
wrangle
AI-based
automated
employment-decision
tools
is
New
York
City’s
Local
Law
144,
slated
to
go
into
effect
in
April.
The
law,
originally
passed
in
2021,
was
postponed
due
to
the
“high
volume
of
public
comments”
during
the
rule-making
process.
It
prohibits
employers
from
using
automated
employment
selection
tools
unless
an
organization
institutes
specific
bias
auditing
and
makes
the
resulting
data
publicly
available.
A
company
must
also
disclose
its
use
of
AI
to
job
candidates
who
live
in
New
York
City.
The
New
York
City
law
could
be
a
catalyst
for
other
states
to
adopt
similar
legislation
—
since
so
many
companies
do
business
in
the
city
and
it
is
an
epicenter
of
finance
and
commerce,
Jurkiewicz
said.
“Implementing
such
a
law
will
undoubtedly
influence
similar
laws
throughout
the
US
and
potentially
other
regions,”
he
said.
While
the
city
ordinance
implies
employers
must
conduct
an
audit,
vendors
are
preemptively
doing
them
to
help
companies
they
work
with,
“both
as
security
for
existing
clients,
as
well
as
a
way
to
differentiate/appeal
to
potential
prospective
clients,”
said
Ben
Eubanks,
chief
research
officer
with
Lighthouse
Research
&
Advisory.
“I
think
everybody’s
holding
their
breath
and
watching
to
see
what’s
going
to
happen
in
New
York,
partially
because
the
rules
around
these
tools
[require
them]
to
be
audited
[and]
evaluated
and
the
vendor
has
to
prove
they’ve
passed
some
approved
checklist,”
Eubanks
said.
“At
this
point,
it’s
hard
to
know
what
it’s
going
to
look
like
rolling
out.
I
have
lots
of
companies
within
the
vendor
community
that
have
been
watching
this
closely.”
Companies
offering
AI-based
recruitment
software
include
Paradox,
HireVue,
iCIMS,
Textio,
Phenom,
Jobvite,
XOR.ai,
Upwork,
Bullhorn
and
Eightfold
AI.
For
example,
HireVue’s
service
includes
a
chatbot
that
can
hold
text-based
conversations
with
job
seekers
to
guide
them
to
jobs
that
best
fit
their
skills.
Phenom’s
deep-learning
algorithm
chatbot
sends
tailored
job
recommendations
and
content
based
on
skills,
position
fit,
location,
and
experience
to
candidates
so
employers
can
“find
and
choose
you
faster.”
Not
only
does
it
screen
applicants,
but
it
can
schedule
job
interviews.
AI
talent
acquisition
software
uses
numerical
scores
based
on
a
candidate’s
background,
skills,
and
video
interview
to
deliver
an
overall
competency-based
score
and
rankings
that
can
be
used
in
employer
decision-making.
Talent
acquisition
software
and
services
have
touted their
AI-based
platforms as
offering
greater
diversity,
inclusion
and
equality
(DEI)
because
the
computer
software
can
be
programmed
to
be
gender
and
ethnicity
neutral;
the
goal
is
to
eliminate
as
much
human
bias
as
possible.
The
problem:
humans
program
the
software.
The
challenges
inherent
in
AI
As
with
any
“disruptive
technology,”
Jurkiewicz
said
AI
brings
challenges
that
should
be
considered
and
planned
for
by
hiring
organizations.
They
include:
-
Algorithmic
bias. -
Lack
of
transparency. -
Legal
and
ethical
concerns. -
Over-reliance
on
AI. -
Privacy
and
data
security. -
Dehumanization
in
the
hiring
process. -
Misalignment
with
organizational
culture
and
values.
A
US
and
EU joint
report released
this
year on
the
potential
economic
impact
of
AI
on
the
future
of
workforces
found
that
while
it
can
bolster
workforce
efficiency
and
innovation,
it
can
exacerbate
inequality.
“There
is
substantial
evidence…AI
has
introduced
and
perpetuated
racial
or
other
forms
of
bias,
both
through
issues
with
the
underlying
datasets
used
to
make
decisions,
and
by
unintentional
or
seemingly
benign
decisions
made
by
algorithm
designers,”
the
report
said.
“The
challenge
for
policymakers
is
to
foster
progress
and
innovation
in
AI
while
shielding
workers
and
consumers
from
potential
types
of
harm
that
could
arise.”
The
challenges
are
only
expected
to
grow.
From
35%
to
45%
of
companies
are
expected
to
use
AI-based
talent
acquisition
software
and
services
to
help
select
and
interview
job
prospects
in
the
coming
year,
according
to
two
recent
studies.
Although
there
are
few
AI-related
employment
laws
on
the
books
at
the
moment,
employers
should
expect
that
to
change
as
the
use
of
AI
expands
beyond
just
hiring
and
into
performance
evaluations,
career
projections,
and
promotion/termination
decisions,
according
to
Paul
Starkman,
an
attorney
with
the
Chicago-based
law
firm
Clark
Hill.
“And
[that]
may
ultimately
morph
into
consumer
information
protection
laws,
such
as
the
European
Union’s
GDPR
and
California’s
CCPA/CPRA,”
Starkman
said.
Hiring
algorithms
aren’t
new
While
the
use
of
computer
algorithms
for
screening
prospective
job
candidates
is
not
new
—
simple
text
searches
have
been
used
to
parse
resumes
for
decades
—
the
sophistication
of
the
applications
and
the
breadth
of
their
use
has
rapidly
grown.
Nearly
three
in
four
organizations
boosted
their
purchase
of
talent
acquisition
technology
in
2022
and
70%
plan
to
continue
investing
this
year
—
even
if
a
recession
arrives
—
according
to a
survey by
online
enterprise
hiring
platform
Modern
Hire.
Modern
Hire’s
fifth
annual Hiring
Report found
that
45%
of
companies
worldwide
are
using
AI
to
improve
recruiting
and
human
resource
functions.
Experts
caution
that
AI
recruiting
systems
are
only
as
good
as
the
programmers
who
“feed
the
machine.”
If
an
AI
tool
ingests
data
from
resumes
of
people
previously
hired
by
a
company
—
and
the
recruiting
departments
that
made
those
decisions
harbored
subconscious
biases
and
preferences
—
those
biases
could
be
inherited
by
the
AI
tool.
For
example,
Amazon
spent
a
decade
training
its
applicant
screening
algorithm
using
its
own
hiring
data.
But
once
it
went
live,
it
reportedly
showed
bias
against
women.
Just
the
word
“woman”
would
cause
the
algorithm
to
rank
female
applicants
lower
than
men.
Conversely,
there
are
also
top
tips from
online
job
search
sites instructing
applicants
on
how
to
write
resumes
that
will
pass
automated
screening
software,
making
sure
job
candidates
get
seen.
While
resume
matching
to
job
descriptions
is
the
most
common
use
of
AI,
tools
are
also
being
used
to
analyze
patterns
of
potential
candidates,
including
segmentation
of
candidates
based
on
experience,
education,
skills,
and
their
potential
for
retention
once
hired,
according
to
Bret
Greenstein,
a
PricewaterhouseCoopers
(PwC)
partner
and
Data
Analytics
and
AI
researcher.
To
perform
more
detailed
searches
for
potential
candidates,
AI
platforms
must
collect
massive
amounts
of
data
on
prospective
candidates
without
their
expressed
permission,
according
to
Eubanks,
author
of
the
book
Talent
Scarcity:
How
to
Hire
and
Retain
a
Shrinking
Workforce.
That
information
can
include
facial
recognition
software
and
video
interviews
companies
may
keep,
share,
and
filter
with
AI
to
determine
favorable
candidates.
In
2019,
the
Illinois
Artificial
Intelligence
Video
Interview
Act
(“AIVI
Act”)
was
signed
into
law,
making
the
state
the
first
to
regulate
automated
“interview
bots”
and
other
forms
of
AI
to
analyze
applicants’
facial
expressions,
body
language,
word
choices,
and
vocal
tones
during
video
interviews.
And
in 2020,
Maryland enacted
a
similar
law
prohibiting
employers
from
using
facial
recognition
algorithms
in
hiring
unless
the
applicant
agreed
to
it.
In
a
2019
blog,
Starkman
wrote
that
AI
has
been
used
on
video
interviews
to
determine
whether
an
applicant
shows
characteristics
of
“successful”
candidates. “Multi-state
employers
should
take
note
of
the
AIVI
Act,
as
it
explicitly
applies
‘when
considering
applicants
for
positions
based
in
Illinois,'”
he
wrote.
The
built-in
bias
problem
In
an
email
response
to
Computerworld,
Starkman
said
that
without
proper
development
and
use,
“AI
systems
can
be
biased,
either
because
of
bias
in
the
data
itself
or
in
how
the
algorithm
processes
the
data,
and
that
may
result
in
the
unintended
elimination
of
certain
disabled
candidates,
foreign-born
candidates,
and
others
in
discriminatory
ways,
if
no
safeguards
are
in
place.”
For
example,
he
said,
AI-driven
chatbots
that
communicate
with
job
candidates
should
be
monitored
to
limit
the
inadvertent
receipt
of
information
about
disabilities
and
other
personal
characteristics
that
could
lead
to
discrimination
claims.
“Another
algorithm-assisted
hiring
and
performance
evaluation
system
ultimately
had
to
be
scrapped
because
it
was
based
on
past
hiring
practices
and
could
not
be
trained
to
unlearn
its
programmer’s
bias,”
he
said.
In
another
illustration,
Starkman
said
software
designed
to
disregard
candidates
with
gaps
in
their
resumes
may
have
unduly
impacted
women
candidates
because
they
were
statistically
more
likely
to
leave
the
workforce
than
men.
“I
understand
where
their
hearts
are
at
—
they
want
to
make
things
safer
and
more
equitable
for
the
candidate
population
out
there,”
Eubanks
said.
“But
the
challenge
is
the
rules
they’re
making
[aren’t]
always
aligned
with
how
companies
hire.”
For
example,
Eubanks
said,
the
Illinois
law
requires
companies
to
delete
any
video
interviews
after
30
days.
Many
employees
quit
a
short
time
after
being
hired.
So,
by
the
time
the
company
goes
back
to
look
at
a
second-choice
candidate,
their
video
interview
is
deleted.
“Some
of
those
nuances
they
put
into
laws…,
[they’re]
put
in
place
by
people
who
don’t
always
understand
how
hiring
works,”
Eubanks
said.
“They’re
not
doing
the
day
to
day
[work].
And
because
of
that,
it
creates
some
complexities,
challenges,
and
headaches.”
To
address
the
challenges,
organizations
should
adopt
a
balanced
approach
that
combines
the
strengths
of
AI
with
human
judgment
or
a
human
in
the
hiring
process
loop
that
has
expertise,
Jurkiewicz
said.
“It
is
vital
to
ensure
that
AI-driven
employment
tools
are
explainable,
unbiased,
tested
and
compliant
with
applicable
laws
and
ethical
guidelines,”
he
said.
When
developed,
tested,
monitored
and
implemented
responsibility,
AI-powered
tools
can
significantly
increase
diversity
and
inclusiveness
in
the
workplace.
Studies
have
shown
that
many
underrepresented
communities
either
lack
the
skills
or
do
not
understand
the
impact
of
not
promoting
their
favorable
attributes
(skills,
behaviors,
competencies
and
experiences)
as
other,
systemically
well-trained
communities
can
and
have
[done]
historically,
according
to
Jurkiewicz.
“AI
can
surface
individuals’
positive
attributes
and
encourage
underrepresented
groups
to
compete
for
work
they
may
not
have
thought
possible,”
he
said.
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