AI
innovation
often
depends
on
partnerships.
Whether
it
is
a
cloud
provider
offering
infrastructure,
a
niche
developer
supplying
a
specialized
model,
or
a
data
vendor
providing
essential
inputs,
these
relationships
move
products
forward.
They
also
carry
risk.
If
a
vendor’s
system
malfunctions,
violates
a
regulation,
or
misuses
data,
the
consequences
land
at
your
company’s
door.
For
in-house
counsel,
the
vendor
agreement
is
the
tool
to
turn
uncertainty
into
clear,
enforceable
expectations.
It
is
not
just
about
legal
protection.
It
is
about
setting
the
tone
for
how
the
AI
will
be
developed,
maintained,
and
governed
throughout
the
life
of
the
relationship.
Defining
Responsibility
Clearly
Every
AI
contract
should
start
with
an
unambiguous
allocation
of
responsibility.
If
the
system
produces
harmful
results,
fails
accuracy
tests,
or
violates
applicable
laws,
the
agreement
should
state
who
is
accountable.
This
includes
performance
standards,
quality
controls,
and
obligations
to
fix
problems
promptly.
Regulatory
compliance
cannot
be
assumed.
Vendors
should
commit
to
meeting
relevant
laws
and
notify
you
immediately
if
legal
changes
require
updates
to
the
system
or
its
deployment.
Demanding
Operational
Transparency
To
manage
risk,
you
need
visibility
into
the
AI
system.
That
means
contractual
rights
to
documentation
that
explains
how
it
works,
where
its
data
originates,
and
how
it
reaches
its
conclusions.
This
might
take
the
form
of
technical
summaries,
training
data
disclosures,
and
change
logs
for
updates.
Without
this
information,
you
may
be
left
unprepared
when
a
regulator
asks
for
details
or
when
a
customer
challenges
the
product’s
decisions.
Clarifying
Ownership
And
Use
Rights
In
AI
projects,
intellectual
property
rights
are
rarely
straightforward.
The
contract
should
specify
who
owns
the
model,
who
owns
the
outputs,
and
whether
the
vendor
can
use
your
data
to
improve
its
technology
for
other
clients.
Clear
terms
prevent
misunderstandings
about
licensing
scope,
exclusivity,
and
the
limits
on
reusing
your
proprietary
information
or
derived
datasets.
Without
this
clarity,
disputes
can
arise
long
after
the
product
is
in
market.
Setting
Data
Governance
Standards
Data
is
the
lifeblood
of
AI
and
the
source
of
many
legal
risks.
Contracts
should
set
explicit
rules
for
how
the
vendor
will
handle
your
data,
from
storage
security
to
deletion
protocols.
Decide
in
advance
whether
production
data
can
be
used
for
further
training
or
testing
and
under
what
safeguards.
Strong
governance
clauses
help
maintain
compliance
with
privacy
regulations
and
align
with
your
company’s
own
data
policies.
Managing
Change
Over
Time
AI
systems
are
not
static.
Vendors
may
update
models,
integrate
new
datasets,
or
alter
processing
methods.
The
contract
should
require
notice
of
any
significant
changes
and
your
right
to
approve
them
before
deployment.
Termination
rights
are
also
critical.
You
should
be
able
to
exit
the
relationship
if
changes
compromise
compliance,
safety,
or
business
fit.
These
protections
are
far
easier
to
secure
at
the
start
than
in
the
middle
of
a
problem.
Contracts
As
Strategic
Tools
An
AI
vendor
contract
is
more
than
a
risk-allocation
exercise.
Done
well,
it
ensures
that
the
vendor’s
operations
support
your
regulatory
obligations,
ethical
commitments,
and
business
priorities.
It
gives
you
the
insight
and
control
needed
to
deploy
AI
responsibly,
even
when
the
core
technology
comes
from
outside
your
organization.
For
in-house
counsel,
moving
from
standard
boilerplate
to
tailored
AI
clauses
means
building
agreements
that
safeguard
trust
and
foster
collaboration.
A
strong
contract
does
not
just
protect
the
company
from
harm.
It
helps
the
partnership
deliver
AI
that
is
reliable,
compliant,
and
aligned
with
the
goals
of
the
business.
Olga
V.
Mack is
the
CEO
of TermScout,
an
AI-powered
contract
certification
platform
that
accelerates
revenue
and
eliminates
friction
by
certifying
contracts
as
fair,
balanced,
and
market-ready.
A
serial
CEO
and
legal
tech
executive,
she
previously
led
a
company
through
a
successful
acquisition
by
LexisNexis.
Olga
is
also
a Fellow
at
CodeX,
The
Stanford
Center
for
Legal
Informatics,
and
the
Generative
AI
Editor
at
law.MIT.
She
is
a
visionary
executive
reshaping
how
we
law—how
legal
systems
are
built,
experienced,
and
trusted.
Olga teaches
at
Berkeley
Law,
lectures
widely,
and
advises
companies
of
all
sizes,
as
well
as
boards
and
institutions.
An
award-winning
general
counsel
turned
builder,
she
also
leads
early-stage
ventures
including Virtual
Gabby
(Better
Parenting
Plan), Product
Law
Hub, ESI
Flow,
and Notes
to
My
(Legal)
Self,
each
rethinking
the
practice
and
business
of
law
through
technology,
data,
and
human-centered
design.
She
has
authored The
Rise
of
Product
Lawyers, Legal
Operations
in
the
Age
of
AI
and
Data, Blockchain
Value,
and Get
on
Board,
with Visual
IQ
for
Lawyers (ABA)
forthcoming.
Olga
is
a
6x
TEDx
speaker
and
has
been
recognized
as
a
Silicon
Valley
Woman
of
Influence
and
an
ABA
Woman
in
Legal
Tech.
Her
work
reimagines
people’s
relationship
with
law—making
it
more
accessible,
inclusive,
data-driven,
and
aligned
with
how
the
world
actually
works.
She
is
also
the
host
of
the
Notes
to
My
(Legal)
Self
podcast
(streaming
on Spotify, Apple
Podcasts,
and YouTube),
and
her
insights
regularly
appear
in
Forbes,
Bloomberg
Law,
Newsweek,
VentureBeat,
ACC
Docket,
and
Above
the
Law.
She
earned
her
B.A.
and
J.D.
from
UC
Berkeley.
Follow
her
on LinkedIn and
X
@olgavmack.