What is a car without brakes and
your seatbelts?
I'm going to talk about one of my
favorite cars, the DeLorean
from Back to the Future movies.
John DeLorean, the owner, was a
rising star in the automotive
industry.
However, he felt some of the
practices were unethical
because he really wanted to change
that. He wanted to build a
fuel efficient and safe car.
So he started to add some innovative
features like a large tail
and brake light so that the car
is visible at night.
There were a few more things he
wanted to add, like airbags, for
instance, but he ran
into production issues, so he
actually pulled those features out..
And that was okay
during the 1980s.
However, fast forward to today,
that is not acceptable.
Airbags, as an example, is
a mandatory feature and pretty basic
in all cars here.
So there is a distinct
before and after in terms
of how the landscape is changing in
the car industry.
If you draw the paddle to the
A.I. industry, that's exactly
what's happening today in the A.I.
industry as well.
Now, keeping that landscape
in mind and thinking about how
the A.I. Industry, or at least
the governance around that industry
has changed, let's take a look at
a timeline of sorts and see
what's happening.
So
if you look at the 1990s,
there was the emergence
of basic metadata
governance.
Then came the 2000s,
where really the big data exploded.
There was a lot of data and there
was a lot that had to be done beyond
just basic metadata.
So this era saw
lineage plus big data
governance.
Together, if you think about this,
this was the era of what is
popularly known as data
governance.
Now when we think about the
2015 until
now as an example,
what really happened was the
emergence of open data governance.
And now if you really think about
now and beyond,
what is happening is
in addition to the open
data governance, there
is the A.I.
governance era.
So this era is popularly
now known as the
data governance
plus A.I.
governance.
So now we see the emergence of
data plus A.I.
governance.
So just like we've
come to know and love the principles
in data governance, the same set
of principles can can be applied
to the A.I. governance area as well.
Priya. I've come back from 2040.
A.I. governance is really important
in the future and it's really
important that we tell everyone to
like and subscribe to our channel.
If this is clear, essential,
and the promise of A.I.
is clear, what is really
stopping us?
There are three main concerns
that we see.
The first one is
how do I confidently
operationalize my
A.I. models?
So today you might
have thousands
of models that are sitting
in your enterprise.
Do you actually know what those
models are? Where they are?
Do you even know what is inside
those models?
So first, to be able
to govern all
these models that have been built
with various tools and technologies
and by different people in the
enterprise, some of them no longer
even with you.
That's the first problem that we
see.
The second one is there
is no way to monitor
what happens to these models
as they move along the A.I.
lifecycle.
There is no monitoring and there is
no cataloging of these models.
The third one here is that
even if all this is set
up, are you able to actually
open up and explain how
the A.I. made the decision that
it did.
So transparency
is another issue.
So these are the problems today
that stop your
A.I. models from moving
into production.
The second problem
is around reputation.
Nobody wants to be in
the news for the wrong reasons.
And we see a lot of that happening
today.
How do I make sure that
I do not inadvertently
introduce bias
in my models as an example when
I'm doing fair hiring
practices?
How do I ensure that I manage
this process across
all my stakeholders?
And finally, how
do I ensure
adherence to different
business controls within my
enterprise?
The third problem that we are seeing
is the rise of regulation.
In fact, in the past,
regulations were strategies, but
now they are becoming actual
policies.
And noncompliance with regulations
is costing billions
of dollars of fine for enterprises.
Some of the regulations you might
have heard of, the SR 11-7,
the A.I. Act In
European Union, even the United
States recently
introduced the US A.I.
Bill of Rights, which mandates
really fair hiring practices
and for A.I.
models to be explainable and not be
a black bot.
So how do you overcome these
challenges?
This is where a good A.I.
governance solution comes into play.
A good governance solution has three
facets to it.
The first one
is lifecycle
governance.
What does lifecycle governance
really mean?
It is being able to monitor
catalog and govern all
your models, irrespective of where
they are built and how they are
built.
And you will be able to
automatically collect facts
about the model and explain
what happens to the model throughout
the end to end lifecycle.
That's the first step of a good
governance solution.
The second one is
risk management.
The third piece,
is the regulatory
compliance.
Are you sure that your models adhere
to external regulations?
And if they do, can you confidently
explain them every single time?
The third piece of A.I.
governance address is exactly that.
To be able to futureproof
your company against regulations
and to be able to automatically do
that at scale.
Now, these are the three facets of a
good A.I. governance solution.
Lifecycle governance for monitoring
an end to end lifecycle, risk
management to be able to take
that and put it across a workflow
management for business controls;
and finally, adherence and
compliance to regulations outside.
Now,
if these are the solutions,
there are a few key facets
of a good A.I.
governance solution.
The first one is it needs
to be open.
Everybody has chosen the tool of
choice. A good A.I.
governance solution needs to come
and augment what's already there and
be flexible to plug and play.
The second one is,
it definitely needs to be
end to end across the
A.I. lifecycle.
The third one
is it needs to be completely
automated.
Can you imagine if you're a data
scientist and all your time
is actually spent manually
writing what your model does
and being able to hand it over to
different groups?
That's not a really good use of your
time. So automation is
key for all stakeholders
across the enterprise.
And the last one
is very important is
that your A.I.
governance solution needs to be
future proof in order
to comply with regulations that
will be coming in the future more
and more.
All right. what you now saw
is the technology confidence of a
good A.I.
governance solution.
However, for A.I.
governance to be successful,
there needs to be a people process
technology trifactor that needs to
come together.
So make sure that you engage
the right stakeholders so
that you have an effective A.I.
governance solution.
So let's close it out.
Just like the brakes in your car
and the safety, seatbelts and
airbags are designed to
help you move to your destination
faster. They're not designed to slow
you down. They're actually helping
you get faster to where you
need to safely.
The same way A.I.
governance is
a set of guardrails to help
you get value from your A.I.
Very quickly.
Thanks for watching.
If you like this video and want to
see more like it, please like and
subscribe. If you have questions,
please drop them in the comments
section below.