Carahsoft's Government Innovation Exchange Keynote explored tech trends, challenges, and opportunities for federal and state agencies.
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0:00
And Gordon Bitco with the Information Technology Industry Council, I'm going to
0:08
ask each of
0:09
our panelists to just introduce themselves really briefly.
0:12
And then to, by way of introduction, just to talk for a few minutes about what
0:16
are some
0:17
of the key challenges that you've learned over the last few years?
0:19
You've all been doing government IT in one way or another.
0:22
Some of you still are.
0:23
What are those big challenges?
0:24
So, Cashant, why don't we start with you since you're your closest?
0:27
Sure.
0:28
Thank you, Gordon.
0:29
Hi, everyone.
0:30
Cuz she's Pandia.
0:31
I am the Chief Technology Officer for Internal Revenue Service.
0:34
Hi, good afternoon.
0:37
Karen Howard.
0:38
I'm the Director of the Office of Online Services with the Internal Revenue
0:42
Service.
0:43
Hello, I'm Owen Casey.
0:45
I, formerly with government and DOD, but I came out a year and a half ago to be
0:49
the VP
0:50
of Cybersecurity Strategy and Product Development at OWN.
0:53
All right.
0:54
Thank you.
0:55
And I'm back with you again.
0:56
Andrew Nielsen.
0:57
I'm the Director of the Government and Wide IT Accessibility Program that sits
1:02
within the
1:03
Office of Government and Wide Policy at GSA.
1:05
And I'm also representing kind of a sub office, the Office of Technology Policy
1:09
So, my colleagues who also work and provide guidance in identity and credential
1:14
ing and
1:15
access management, cloud center optimization and emerging technologies.
1:20
Great.
1:21
Thanks, Andrew.
1:22
And so, why don't we actually start that first question with you.
1:25
What are the couple of key lessons that you've really learned over the last few
1:29
years?
1:30
Yeah, I think, you know, on my, you know, more narrow perspective on
1:35
accessibility, you
1:38
know, I kind of, we talked a little bit about it this morning as part of the
1:42
customer experience
1:44
panel.
1:45
But more and more, of course, more and more of our services are moving to the
1:49
digital
1:50
sphere.
1:51
And so, I think, you know, certainly a trend that we're seeing with the advent
1:57
of, and
1:57
development of low code, no code environments, that obviously the advent of
2:02
artificial intelligence,
2:04
generative AI, just more and more customization, more technologies, specialized
2:10
technologies
2:11
for specific line items of business.
2:15
And so, I think, you know, we're seeing that.
2:17
We'll see only more of that as we make better and more intelligent use of gener
2:23
ative AI's
2:25
and as they get better.
2:27
And so, you know, along with that, you know, of course, the challenges.
2:31
I won't speak to all of them.
2:33
But the challenges that come with that in making sure that we are making
2:37
intelligent
2:38
use of those and that we're safeguarding privacy, we're safeguarding the
2:42
security of our applications
2:44
and our digital services.
2:47
And then again, maybe Naramine, again, on my accessibility focus, ensuring that
2:52
as we
2:53
-- and I think there's probably a question later.
2:55
I'm skipping ahead to the question on innovation.
2:58
But as we innovate, that we don't leave people behind, and that, again, on my
3:03
accessibility
3:04
focus, that we need to ensure the legitimacy of our government, the services
3:12
that we provide
3:13
by ensuring inclusion and ensuring that we meet the needs of all of the public,
3:21
the citizens
3:21
of the U.S.
3:23
So, yeah, sorry.
3:24
>> No, no, great, Andrew.
3:25
I think all super helpful.
3:26
I'll note, you know, not just the eclipse but an earthquake going on here at
3:29
the same
3:30
time, too.
3:31
So, we're full-futures, full services.
3:33
Oh, and same question to you to start with.
3:36
You know, what are the key lessons over the last few years that you've really
3:39
taken away?
3:39
>> So, I have to balance innovation and security.
3:44
So I came from a background of dealing with security breaches and investigation
3:47
, but moved
3:47
into more of a proactive state.
3:50
But trying to translate, I think, some of the lessons we've learned from the
3:54
past -- from
3:55
the past, I think there's been a lot of good discussion today about data being
4:01
so critical
4:02
now in everything that we do, and making effective use of that, and doing it in
4:07
a more
4:08
speedy manner, we have to keep pace with the changes in technology and the
4:12
advances that
4:13
are coming with stronger analytics and more advanced AI, but balancing that
4:19
with proactive
4:20
security from the outset.
4:22
And so I come in with that integration of the innovation and the protection.
4:28
We can't just let the data go ahead of us and try and catch up.
4:34
We will not catch up.
4:36
I can guarantee it.
4:37
>> Yeah, for sure.
4:38
I think that's a great point.
4:40
And Karen, I'd like your thoughts as well.
4:42
But one of the themes that we just got from both folks is the importance of
4:46
people in
4:47
all of this.
4:48
And given your role, I'm sure you've got some thoughts there as well.
4:51
>> Sure.
4:52
I think one of the things that I think we're really starting to understand is
4:57
the importance
4:58
of user experience design in the design and putting that first, letting that
5:05
drive the
5:06
design of products, the design and the needs of the business so that then the
5:11
technology
5:12
follows behind what's learned in the UX research and the data that helps us
5:17
understand that.
5:19
I think that's one of the things that you're seeing a really good focus on now
5:23
that you
5:23
didn't before.
5:24
Typically, UX was kind of an afterthought.
5:28
It's like, oh, now that we've designed this, now let's get designers to look at
5:32
it.
5:32
And it was about colors of call to action button.
5:36
And now it's really about partnering with our IT partners and making sure UX
5:41
design is
5:42
in the forefront of development.
5:44
And not an afterthought.
5:45
I think the other thing that these two gentlemen have already really spoken to
5:50
is data and
5:51
data-driven decisions and data that supports business cases.
5:56
Sometimes we think we know what our users want and what our customers want.
6:02
But using data to kind of validate or help us understand anomalies that we can
6:06
address
6:07
in design.
6:08
And I would end with saying that letting business needs and business problems
6:13
and business use
6:14
case drive technology solutions and not going for technology and then looking
6:20
to retrofit
6:21
it into the business.
6:22
Yeah, I think that's a great lesson because I'd certainly like to get your key
6:27
lessons
6:28
from the last few years as well.
6:29
So I'm going to add on a spin to that around that point.
6:32
You've got obviously the IRS tons of data.
6:36
And how do you think about those exact issues?
6:38
We've got all this data.
6:39
We've got these really challenging use cases for Americans who expect certain
6:44
behaviors
6:45
on the IRS.
6:46
And so how do you balance those?
6:48
Yeah.
6:49
No, data certainly is and we have more than our fair share of it.
6:56
Over the last couple of years what we have really observed and the pandemic hit
7:01
this
7:01
home is that we need to find better, more intuitive ways to engage with our
7:07
taxpayers.
7:09
And our taxpayers in turn want to engage with us in a similar manner.
7:13
Perhaps you've seen these unfortunate pictures of us having volumes of paper
7:17
stacked in our
7:18
cafeterias and all over our IRS campuses.
7:23
We have really focused on digital transformation.
7:26
So digital transformation, we cannot stop paper from coming in.
7:30
However, we can convert paper and digitize it and digital transformation has
7:35
been a pioneer
7:37
of a program for us.
7:39
However, what we observed is while we were converting our paper to digital
7:45
images internally,
7:46
our people were printing them out.
7:49
Of course they were.
7:51
Because in order for them to get access to the data and if it wasn't
7:55
necessarily the
7:56
highest degree of accuracy, so they're printing out all the paper, we were
7:59
digitally transforming
8:00
at our doorsteps.
8:02
We then realized that the reason they were printing this out is because they
8:05
needed to
8:06
key in the data manually into the systems.
8:09
Our focus then shifted to not just converting them to digital format but really
8:14
honing in
8:14
on extracting the data and feeding it downstream to our systems so that our
8:19
people are focused
8:20
on running the necessary analytics, doing all the work that is required to
8:25
assist taxpayers
8:26
versus printing out and punching in that same data that we were collecting.
8:30
Yeah, I think that's a great example that highlights Karen's point about
8:33
understanding
8:34
the business case and really knowing what the technology is for.
8:37
But at the same time, it also highlights one of the challenges I always had in
8:41
these
8:41
transformations is the people and the cultural aspect of they're used to doing
8:45
something
8:45
the same way for so long or you as a federal agency have compliance
8:49
requirements where
8:50
you have to do things a certain way or the agency's made a risk management
8:53
decision to
8:54
do things a certain way.
8:56
And so Karen, if I could go back to you, how do you deal with changing that
9:00
environment?
9:01
Well, I think when it comes to changing an environment where you've got a lot
9:06
of legacy
9:07
practices and comfort in doing things the same way, you've got to really find
9:13
the win-win
9:14
for the employees or the people doing the work.
9:18
I think part of it is going to involve upskilling and how it adds value to
9:23
their role sometimes
9:24
to their bank account.
9:25
Wouldn't that be nice?
9:27
Just giving them more skills and showing them the benefit.
9:31
Also making them a part of the solution.
9:33
I think when you talk about change management, first of all, you've got to make
9:37
sure the
9:37
business is ready so there's business readiness or organizational readiness and
9:43
then it's
9:44
really making them part of the decisions around what you're planning to do and
9:49
getting their
9:50
feedback along the way, giving them ownership in the change and also showing
9:55
them the benefit
9:57
to them.
9:58
Not everyone will grasp that and be excited about it, but most people will and
10:03
then the
10:04
outliers, you know, you handle those in the best way you know how and help them
10:08
find their
10:09
happy place.
10:10
But one of the things I can say, change management, it's easy to say and it's
10:14
hard to do.
10:16
And I think organizations need to understand where they are from a cultural
10:21
standpoint
10:22
in the maturity for change and begin to implement change based on that.
10:28
Some organizations are ready for it and some organizations need to move at a
10:33
slower pace.
10:34
But when it comes to change management, if you build it, they will come, does
10:39
not apply,
10:40
especially when you have a lot of legacy employees.
10:43
You've got to figure out how to make it affect them in a positive way.
10:48
Yeah, thanks, just to follow up on that and, Andrew, I'm going to go to you
10:53
with this
10:53
question.
10:55
How do you balance that change management challenge with the potential of all
10:58
these emerging technologies,
11:00
which I would imagine when it comes to accessibility, have the potential for
11:04
huge benefits, right?
11:04
AI can really transform a lot of the challenges that we have in access and
11:09
accessibility.
11:10
Yeah, yeah, I mean, particularly with regard to artificial intelligence, gener
11:16
ative AI,
11:18
maybe narrowing in on that world of accessibility and then broadening.
11:23
There has been some argument in our little world of accessibility that AI will
11:28
just solve
11:29
all of our problems.
11:30
AI as an assistive technology can just help anyone with a disability regardless
11:37
of the
11:38
type of disability.
11:39
When it comes to interacting in the digital sphere, we don't need to worry
11:43
about making
11:44
our stuff accessible anymore because you could just use AI as an assistive
11:48
technology and
11:49
then that fixes everything.
11:52
And then, by the way, it's also, that's to our benefit because it costs too
11:57
much and
11:57
it's just too much effort and there's too much cognitive load.
12:02
I have to train people to know how to make their stuff accessible.
12:05
And that's just kind of wrong-headed in the first place.
12:11
It's subscribed to the model of looking at an individual and their differences
12:15
as differences
12:16
when not recognizing that differences are really dependent on environment and
12:24
context.
12:25
And especially in our broad human experience, everyone has differences and when
12:32
it comes
12:33
to disabilities, rather than relying on somebody to fix their difference, we
12:39
should adapt our
12:40
environment.
12:41
We should adapt our context to meet the needs of individuals and that can go
12:46
for any type
12:46
of difference.
12:48
And so there is still fantastic promise.
12:51
I'm more excited about the use of AI on the intent, on that side, on that
12:57
really side of
13:00
development, whether it's development of a process or development of technology
13:03
, and
13:04
using AI to help as a copilot, if you will, or a tutor or a partner, I want to
13:11
do a thing
13:12
I might not know how and so help me do that thing.
13:15
And so, again, with what we're talking about, low code, no code environments or
13:21
a developer
13:22
who knows all of the bells and whistles but just doesn't know accessibility,
13:28
that is a
13:29
very positive, I think, a positive development we can look forward to.
13:37
And then expanding that out to other spheres outside of just accessibility,
13:44
there's so
13:45
much more where it's applicable.
13:47
Not only when I'm looking to meet the needs of individuals with disabilities,
13:50
but when
13:51
I'm looking to meet broad customer needs, again, in the digital sphere, to
13:56
better understand
13:57
that I don't know everyone's circumstances or all of their needs, but I can use
14:03
AI to
14:04
inform that and to broaden my context and to rely on that rather than only on
14:09
my knowledge.
14:10
Great.
14:11
That's, I think, a really helpful set of insights into the ways that AI can
14:15
help.
14:16
But when there's a flip side, AI introduces threats, changes the threat
14:20
landscape.
14:21
So I'm sure you've got some thoughts about how we should be trying to
14:24
incorporate that
14:25
into our understanding of the threat environment.
14:28
Well, I think there's the traditional threats just applied to the new
14:32
technology which we
14:33
have the disruption by adversaries.
14:36
But I think one other factor that we have to consider is bias.
14:40
And this is something that came up in roundtable discussions, what we also see
14:44
in across sectors
14:45
is the potential for some of the training that's been done or even just the
14:50
data that's available,
14:53
introducing certain biases.
14:54
So if you think of, in a hospital setting, doctors are being approached by
14:59
pharmaceutical
15:00
companies to promote a particular prescription, that's then setting a trend, a
15:06
historical
15:07
trend that could ultimately bias any automation that we have to help with
15:12
prescription management,
15:13
prescription efficiencies.
15:15
So we have to, I think, be careful of the risks that are unintended of the use
15:20
of the
15:21
technology also.
15:22
And one of the things that I think pulls us all together is if we have humans
15:27
in the loop
15:27
for sure who are able to more quickly, if you think about the speed of
15:31
iteration that
15:32
we now can achieve with this technology of getting, it's the Uda loop from, I
15:40
guess,
15:41
the military context but applied here, we're talking about observe, orient,
15:44
decide, and
15:45
act.
15:46
We can do that more quickly now and make adjustments and see problems more
15:49
quickly and adapt.
15:51
I think that that's some of the potential that I see where we can avoid some of
15:56
the problems,
15:57
we can be more proactive and avoid some of the problems from a security, a bias
16:01
perspective
16:02
and perhaps come up with better solutions in the long run.
16:04
So we're not getting rid of people is what you're saying.
16:07
I think that's given.
16:09
So because I think that aligns really well with one of the use cases we were
16:13
talking
16:13
about outside around how you're looking at helping the IRS workforce with AI
16:20
tools,
16:20
not replacing them.
16:22
Correct.
16:23
Yes.
16:24
We are being very cautious, naturally so, about how we leverage the
16:29
technologies.
16:30
AI is quite prevalent and what people equate AI to is chat GPT.
16:36
I mean, that is what is typically known.
16:39
However, we want to be much more deliberate about how we apply it.
16:44
And we're being cautious on how we apply it for external facing.
16:47
We want to be very, very careful of how our taxpayers engage with us and
16:52
leveraging AI
16:53
and we don't want to create those hallucinations or have any misleading
16:57
information.
16:59
If we were focused on or populate our data models with scour the internet for
17:05
any and
17:05
all tax related questions, somebody on Reddit might say you don't need to pay
17:09
taxes and
17:10
all of a sudden when a taxpayer asks if I should or not, that's the answer that
17:15
pops
17:15
up.
17:16
We're looking more internally and there are a plethora of opportunities
17:20
internal to IRS
17:22
on how we can apply AI.
17:24
For example, we have a legacy or I would say an aging workforce that is leaving
17:32
and they
17:32
have been very accustomed to reviewing manuals that are physical, that are
17:37
paper and they're
17:38
looking across these lengthy and thick workbooks on how to apply or how to
17:44
evaluate or how to
17:46
assess.
17:47
However, as the newer generation comes in, they are much more open to and
17:52
wanting the
17:53
digital equivalent of it.
17:55
But the volume of data contained, however, hasn't changed.
17:58
In fact, it's done nothing but grow.
18:01
How can we assist our internal employees to not have to figure out which page
18:06
to look
18:06
for and we are applying AI against that?
18:11
Convert our internal information and make it more conversational so that our
18:14
internal
18:15
employees are able to simply ask a question, to go and find that information
18:19
rather than
18:20
have to figure out which book contains what part of the tax law.
18:24
I'm disappointed to hear that Reddit is not in the favor too of a source.
18:29
I might have to sell my stock.
18:32
That's a great example though of how do you use technology to actually drive
18:36
change in
18:37
the organization.
18:38
So I'm going to go back around and ask each of you that question, Karen,
18:41
starting with
18:42
what's the thing where you think there's the best opportunity for technology
18:46
and your
18:47
workforce together to be transformative in that way?
18:50
I think if we can leverage technology to address business problems, customer
19:00
problems,
19:01
user problems, and to bring efficiency in addressing those either from a
19:06
customer facing
19:07
perspective or behind the scenes from an operational perspective.
19:12
If we can look at, you know, to, because she's point using artificial
19:17
intelligence to help
19:19
us with a fine anomalies or to fine patterns that help us identify scams and
19:24
schemes which
19:25
is, you know, really a big thing right now and usually is around tax filing
19:30
times.
19:31
We can use it to find pain points in arduous processes that take a long time
19:37
and help eliminate
19:39
those.
19:40
I think if we can leverage platforms, workflow management to really identify
19:45
bottlenecks and
19:46
workflow and really hold people accountable.
19:49
I think right now with a lot of manual processes in place and not knowing where
19:54
the black
19:55
holes are when we're waiting for a response or looking to understand where a
20:00
document
20:01
that, you know, it's going to drive a business decision or a action is located.
20:07
There's just so much if you just look at what's causing problems in the
20:11
organization and focus
20:13
on those and that is serving the team, making your employees involved in what
20:18
are your biggest
20:19
pain points if they say it's, you know, procurement or hiring or onboarding
20:27
employees and kind
20:29
of mapping that out in a value stream way and then saying, okay, how can we
20:33
leverage
20:34
technology to do some of this stuff?
20:36
That's where technology begins to pay off.
20:39
And again, like I said before, that is using business needs to drive technology
20:44
and not
20:44
the other way around.
20:46
Great, can we maybe afterwards just have a purely intellectual conversation
20:50
about how
20:50
you're using models to find scams and tax filings?
20:53
We absolutely can.
20:55
Just hypothetical, like I said.
20:58
Oh, and same question over to you.
21:00
I'd really like to just continue that.
21:03
I completely agree, Karen.
21:04
And I'm happy to say I had a very positive experience with the IRS and working
21:08
with
21:08
someone who knew a lot of the systems in place.
21:11
And I think what we really have a potential for here is to take some of those,
21:16
we might
21:17
call it a pain point, but I also can think of this as finding the things that
21:22
makes us
21:22
successful and amplifying those across the organization.
21:25
So it's using the historical successes or the factors that contribute to
21:29
success historically
21:31
and amplifying that with the technology to help people across our organizations
21:35
be successful.
21:36
Andrew?
21:37
Yeah, I think, you know, broadening it.
21:41
Remember my comments on specific to accessibility.
21:44
While there again, just some fantastic, I think, things to look forward to
21:50
using advancing
21:51
technologies, AI, to help us as partners, as mentors, as co-pilots, to make our
21:59
content
21:59
and our digital tools and services accessible to people.
22:04
I think another thing to look forward to is that while, in some cases, it may
22:10
also be
22:11
perceived as a threat, but the hyper-customization of my experience as a
22:15
customer, especially
22:17
in the digital sphere.
22:19
I think most of us are accustomed to seeing custom ads and maybe get a little
22:25
bit worried
22:25
that they're overhearing, my computer's overhearing, my conversation, how did
22:29
you know I was talking
22:31
about that earlier?
22:33
And so there's some advanced algorithms, but there also, and so we need to be
22:36
concerned
22:37
about privacy and security, but there's also a great opportunity for hyper-
22:41
customers.
22:42
More relying on cues that you provide as a user or as a customer to customize
22:49
your experience
22:51
and to choose your own experience, if you will.
22:54
And so I think, again, technology will only continue to quicken our pace and
23:03
our ability
23:05
to adjust and to provide new technologies that's only going to increase,
23:11
especially with AI.
23:13
And I think, again, more and more, we'll be able to customize our experience to
23:16
our needs
23:18
and excited about that.
23:20
So Andrew, just to stick with that comment for a second, because I think that
23:24
the hyper-customization
23:25
thing is certainly really fascinating in government.
23:28
It's something that we all see and experience in our own personal uses of
23:32
technology.
23:33
Can government policy, can government compliance, can government regulation
23:35
keep up with the
23:37
ability to do things like that?
23:39
You look at how long it takes an executive order to come out or guidance to
23:42
come out
23:43
from OMB to agencies, and it can be years from the time that it's discussed
23:48
until it's
23:49
in place.
23:50
Yeah, I think that there is still room for that.
23:54
I mean, of course, that can be hard.
23:57
I think a starting point, I think, first of all, let's not start from scratch.
24:01
Let's rely on best practices.
24:04
Let's rely on design systems, for instance.
24:06
The agencies, hopefully, are all using the US Web Design System for your web
24:10
presence.
24:13
Start there, and then customize on top of that.
24:16
So start with what you have.
24:21
Don't necessarily start from scratch.
24:23
Build on best practices.
24:25
Yes, absolutely, I think our pace will still be slower in government as we
24:30
adapt regulation
24:32
and law and rely on our legislators to do that as well.
24:37
So I don't know that I have a silver bullet answer.
24:40
There will be some lag.
24:42
But I think that there is still a lot of room based on a lot of really great
24:45
work and tools
24:46
that we have available.
24:47
Yeah, I don't think anybody expects government technology to be the same as
24:52
what it is at
24:52
home.
24:53
We'd like that, right?
24:55
But if we can converge, right, if those things can get closer.
24:59
And Karen, one of the things that you said about the role of some of these
25:02
technologies
25:03
in the procurement process, I think, is that the core of trying to do that
25:07
better and smarter
25:08
and faster.
25:09
So I don't know if that's something that IRS has started to think about yet.
25:12
We have, and probably because she can probably speak to this better than I can.
25:16
I know with the funding, the influx of funding from the Inflation Reduction Act
25:21
, we are addressing
25:23
a lot of the historic, iconic processes that we all know and love.
25:30
But I do want to speak to one thing.
25:32
I always function on the mindset of there are some things you can't control or
25:37
that
25:38
are going to be what they're going to be.
25:40
What are the things that I can control?
25:42
So when we talk about customization, part of that is personalization.
25:47
So really looking at one of the things that I really have worked hard on with
25:52
my IT partners
25:54
are online account.
25:56
And how do we make that so people are getting more of what they want after they
26:01
authenticate?
26:02
So to answer your question, I really think that to this point, there is still
26:07
room to
26:08
grow.
26:09
But we can continue to look at the things we can impact while we wait for the
26:14
wheels
26:14
of government to continue to turn and we continue to educate ourselves,
26:18
leveraging our industry
26:20
partners in the private sector to know what's coming and to be ready.
26:25
Is she, did you have more to talk about procurement?
26:29
Absolutely.
26:30
First of all, we have a lot of witnesses who did hear Owen say he had a
26:33
pleasant experience
26:34
with the IRS.
26:38
So procurement is one of those areas that we have found that we can apply
26:43
intelligence
26:44
against to expedite how we procure and how we deliver, how we award contracts.
26:49
In fact, we have, to Karen's earlier points, we really are targeting business
26:55
cases.
26:56
And when we talk about business cases, we're saying what can we do to help
26:59
improve our
27:00
internal business users to do their jobs better, but also our customers as a
27:05
whole, all of
27:05
the taxpayers, ourselves included.
27:08
It is looking beyond technology and thinking about what problem exists and how
27:12
can we apply
27:13
technology to solve it versus saying here's a technology, now let's go find the
27:18
problem.
27:19
The procurement one is really interesting one.
27:21
We have a procurement office where individuals were spending hours and hours,
27:27
if not longer
27:28
than that, and perhaps days scouring the right websites to determine whether or
27:35
not a vendor
27:36
we are going to engage with is tax compliant.
27:39
And if they're not tax compliant, we naturally can't engage with you.
27:43
And this would be a manual effort.
27:45
People would go on websites and do research.
27:48
We applied intelligence against it, and now we've cut down from multiple days
27:53
to minutes,
27:53
five minutes currently to find whether or not a vendor is tax compliant.
27:58
That's a real case example of how fast we can now perhaps determine whether
28:03
somebody's
28:04
qualified or not.
28:05
That's some of the really quick use case.
28:07
So that's our internal team that's focused on that we've been thinking about
28:10
how to solve
28:11
for.
28:12
The other one is again, all of us, the taxpayers.
28:15
When you file a tax return today, it gets submitted, it gets processed within
28:19
hours.
28:20
However, if you have to amend a tax return, if you had to modify it, it takes
28:25
months.
28:26
And the reason for that is because when it gets modified, somebody internal to
28:32
IRS has
28:32
to evaluate your tax return to your new amended return and make a judgment call
28:38
on whether
28:38
or not this is something that needs further scrutiny or it should pass muster.
28:43
What we are doing is applying intelligence to say you don't have to look at
28:47
everything.
28:48
Here are the biggest discrepancies.
28:49
So you really have a decision to make on these three focus areas.
28:54
Now it's cut down the time that our internal representatives spend on
28:59
evaluating the whole
29:00
form versus the three most typical or the three most questionable areas.
29:07
And now it's just their judgment.
29:08
I'm going to pick amongst the three versus spending days and evaluating one by
29:13
one between
29:14
the two.
29:15
You would think that there would be a self-selection rate that companies that
29:19
are not tax compliant
29:20
aren't going to look to do business with the government, with the IRS in
29:23
particular.
29:23
But apparently that's a bad assumption.
29:26
So I'm glad to know that you guys check that.
29:28
Oh, and from the outside, what does modernized procurement look like?
29:34
How is technology helping?
29:36
I was hearing some of this and was thinking the modernization and some of these
29:40
efficiencies
29:41
are to be encouraged.
29:44
But the data that we're using as we move into some of these modernized
29:49
capabilities and
29:50
we can talk about sales force here, the lessons that we've learned from the
29:55
past about securing
29:56
that data, having continuity of operations as we move forward with that new
30:01
technology
30:02
and new solution, we have to translate.
30:05
And we're running behind in terms of our ability to do that.
30:09
So I think one of the key factors that I would say is as we move to these
30:14
solutions,
30:15
we don't forget the lessons that we learned from failures in the past.
30:19
And just keep in mind, for example, the lessons that I learned in my dealing
30:24
with insider
30:25
threat and APT threats, the zero trust approach has to be applied in these new
30:31
environments
30:32
as well.
30:33
And that's very data centric.
30:35
It's least privilege access management.
30:37
It's making sure that people who have access to data, they need it.
30:40
It's not broadly accessible.
30:42
And that we're monitoring some of the activities so that we, if we do see a
30:46
problem of somebody
30:47
doing something that they shouldn't in that environment, we're aware of it and
30:50
we can
30:50
be proactive.
30:51
And that applies also to the automation and the AI where auditability, I think,
30:56
is something
30:56
that we've lost our -- we've kind of lost the threat a little bit in these new
31:00
environments.
31:01
Automability of AI applications is critical.
31:03
To be able to figure out, when you do make a decision, if it's comparing two
31:08
tax returns,
31:10
that decision can be tracked back through some of the processing that led to it
31:15
so that
31:15
it's not just a black box and potentially a liability if somebody then made the
31:20
wrong
31:20
decision.
31:21
Do you think the -- something like auditability, though, depends on the use
31:27
case and the particular
31:29
risk.
31:30
Clearly, taxpayers are going to be very upset if their filings are wrong
31:35
because of an
31:36
AI.
31:37
But there are other cases where government agencies and others can use AI where
31:41
the impact is
31:41
different, right?
31:42
I'd say kind of going back to the keynote with a speaker, Nataka, was talking
31:47
about some
31:48
of the innovations.
31:50
We can use technology to help, but I don't think that there are many situations
31:54
where
31:54
we won't, at some point, want to -- or need to explain to a lawyer how that
32:00
decision was
32:01
made.
32:02
And we can't just say that the AI told me to do it.
32:05
So auditability is going to come in once it's medical malpractice.
32:09
It's maybe in the higher risk.
32:10
It will be more necessary and more detailed.
32:15
But if we can't at least explain to some degree how the decision was aided by
32:19
the technology,
32:22
we're opening ourselves to the problems.
32:24
I just had a Megan flashback.
32:26
Did you all see the movie Megan?
32:29
But the AI went -- oh, so I digress.
32:32
Okay.
32:33
I saw the trailer for it.
32:36
And the accountability was they didn't know because the AI did it, and they
32:40
didn't know
32:40
what it was doing.
32:41
And so you just kind of brought that back to my mind.
32:44
That's a scary thought.
32:45
There it is.
32:46
Yeah.
32:47
So I want to be respectful of time, but I did want to get into that point just
32:51
a little
32:51
bit more because one of the issues that -- break when it comes up in settings
32:55
like this is
32:56
how do you balance those new additional government requirements?
33:00
Some people might call them compliance, but whatever we call them, with the
33:03
desire to
33:04
move quickly on the technologies, to innovate, to compete with other nation-
33:08
state adversaries,
33:10
all of those things.
33:11
And it's a balancing act.
33:12
So I don't know -- let me start with you if you've got thoughts on how to do
33:14
that.
33:14
Well, I'll just remind you of mine, the one who asked Taka how he got the ATO
33:18
process down
33:19
to two or three weeks because I was in the DOD and I couldn't figure out how to
33:23
get it
33:23
done faster.
33:24
So it's a problem, but obviously Taka has solved it, so go ask him.
33:30
No, it's something we have to strive for, and I think one of the key factors is
33:35
a forum
33:36
like this where we can share solutions and figure out how to do better.
33:39
So I'm glad that we're -- thank you for the opportunity to join you, and I hope
33:43
we can
33:44
learn more after.
33:45
Yeah, Andrew?
33:46
Yeah, you know, I made a comment earlier about safeguarding or protecting the
33:52
legitimacy of
33:53
our government.
33:55
And I think that there's just some acknowledgement for us bureaucrats that we
34:01
rely on, we need
34:03
some of that red tape.
34:05
And that is, again, to safeguard to the legitimacy of our government.
34:10
And so, yes, there is a balance.
34:13
We absolutely want to innovate.
34:15
We want to make things easier.
34:16
We want to make things quicker, but we also need to slow down sometimes and
34:19
make sure that
34:21
we are compliant, that we're complying with the law, conforming to technical
34:25
standards
34:26
for whether it's security or for accessibility, and that is necessary.
34:32
And I think that we can also look forward to technologies that can help us
34:36
navigate that
34:37
more easily.
34:38
And so, yes, there, again, still will be a lag, but a necessary one.
34:44
And we still can look for ways to improve those.
34:46
And our challenge, of course, is, again, as bureaucrats to determine what red
34:52
tape can
34:53
I eliminate and what needs to stay and for what reasons.
34:57
And that's our job.
34:59
Great.
35:00
Thanks, Karen.
35:01
Yeah, I think that no one expects us to move at the pace that industry does,
35:06
private
35:07
sector, because we have more to lose.
35:10
We have more threat that has a more radical implication if those cyber surfaces
35:19
are, you
35:20
know, attacked.
35:22
So I think while we do have the desire to move fast, and I think we can move
35:28
faster by learning
35:29
from industry, seeing, letting them, you know, run with and learning from them
35:34
in events
35:35
like this, learning what not to do, what to do, what lessons learned.
35:39
But also, I like the idea of test and learn, you know, piloting MVPs.
35:46
And let's try a little bit and let's learn, let's dip our toe in the water and
35:51
see what
35:51
happens and then scale up as we get comfort as legislation changes.
35:57
And actually using those tests to drive changes in legislation and increase
36:02
compliance.
36:03
I think the risk for government far exceeds, especially with the IRS, what
36:08
would be tolerated
36:10
for a, you know, private sector organization.
36:13
I mean, how many times have you all received letters where there was a data
36:17
breach and
36:17
you get a subscription to monitor and life's good?
36:23
So if IRS sent you one of those, we would be tired and feathered on the steps
36:28
of the
36:29
White House.
36:30
So we just don't have the tolerance, but we can learn and we can test and grow.
36:35
Well, I did get one of those from OPM once upon a time.
36:38
I did just think we just let them.
36:39
I just let some people being tired and feathered in congressional hearings.
36:42
Yeah.
36:43
No, I, so Karen is absolutely right.
36:49
Our primary responsibility is to protect all of your data, all of our data.
36:55
Really we see about two and a half billion unauthorized attempts against us,
37:00
two and a
37:00
half billion.
37:01
We're talking about multiple millions a day.
37:05
And these are bad actors who are trying to access information that they know we
37:10
possess.
37:10
So we are extremely careful.
37:13
We are very risk averse and naturally so because we have a very big
37:17
responsibility.
37:18
However, there are two things that were said here is the approach that we've
37:22
taken.
37:23
One is exactly what Karen said.
37:25
Let's try it out.
37:26
Let's do it on a smaller scale.
37:28
We don't have to do this for everyone all at once.
37:31
You know, of a population of 400 million, we have over 200 million that are
37:36
paying taxes.
37:37
We don't have to target and find a solution for applied technology that
37:41
addresses the
37:41
needs of 200 million.
37:43
Perhaps we look at 100,000 and testing out those waters.
37:47
The other thing is what Owen said, there is a great opportunity to engage with
37:51
all of
37:51
you, with our other partners, other agencies, and learn how they're doing it.
37:57
One of my areas of responsibilities that I've now started to expand on is not
38:02
just containing
38:02
it within the United States, but frequently speaking with the tax agencies of
38:08
UK, Australia,
38:09
Canada, the ones who also have a similar scale and size of ours, how are they
38:15
addressing
38:15
some of these challenges while keeping that risk at bay and keeping it minimal
38:21
at best,
38:22
because we don't have that luxury to, again, be the one that sends out the
38:26
letter that
38:27
tells you that we're going to help you now monitor your credit for the next 12
38:30
months.
38:30
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