WEBVTT

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Dot Comsideanalysis dot com, and now
here's your host to Eric Kavanaugh and Flare.

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All right, ladies and gentlemen,
Hello and welcome back once again on

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the only coast to coast radio show
in the US of ATA. It's all

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about the information economy Inside Analysis.
Yours truly, Eric Kavanaugh here, and

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folks, I'm very excited to be
dialed into our nation's capital. We're going

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to talk to an industry visionary today. We've got mister Chris Moore on the

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line from the Software and Information Industry
Association or SIIA. We're going to talk

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all about data and the data life
cycle and protecting the data life cycle and

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frankly being responsible stewards of data.
And that's good for lots of different reasons,

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one of which happens to be this
explosion of artificial intelligence. So it's

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not just people using data and people
processing data, and now there are machines

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that are using data to train on
and to do all sorts of different things.

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And that is very, very disruptive, and I think it's going to

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continue to be disruptive. So we
want to talk also about responsible AI and

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how to get there and the short
answers a lot of it has to do

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with data. And with that,
let me welcome Chris More to the show.

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Thanks for your time today, tell
us a bit about yourself and about

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the SIIA. Sure, my pleasure. It's good to be with you,

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and thank you for the invite.
Thank you, Ben. We are What

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we like to say is we're a
place for folks who are in the business

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of information. And what we do
is we look to preserve and protect a

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healthy information life cycle. In other
words, we want to make sure that

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there is a good environment for its
creation, dissemination, and productive use.

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And our members range from platform companies
you're probably well familiar with, as well

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as educational publishers in news organizations and
database publishers and financial data firms. Okay,

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and you get into a lot of
different policy areas obviously. I mean

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there's intellectual property, there's privacy,
there's cybersecurity, and just the use of

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data. So when you talk about
the data life cycle, I presume you're

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talking about data from its creation to
its grave, basically, from its inception

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through its use all the way to
its deprecation, which in many cases doesn't

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even happen because it just sits around
forever. But a lot of cases,

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companies will follow process and procedure and
after X period of years, whatever the

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regulation is, they will delete that
data and move on. Can you talk

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about how that works? I mean, you are an association, so you

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have leaders from many of these big
companies I guess over eight hundred members who

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come together to forums and really talk
about this stuff and kind of hash out

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what to do. Because I know
from experience policy is hard, and policies

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need to be understandable, they need
to be known, and they need to

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be reasonable in order for people to
follow them. So tell us a bit

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about how you go about doing that
and maybe give us some examples. Uh.

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Sure, I mean I think there
there are areas, for example,

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uh, where there's a pretty good
bit of consensus within our membership, which

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is the easiest place to take policy
rights. And so for example, let's

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talk about let's talk about privacy.
That's right, that's a hot topic.

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And is there an AI wrinkle?
Absolutely there is. Uh. But that's

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an area where we saw a bunch
of you know, we saw state laws

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coming from California in their in their
signature law, and uh we looked at

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the text and we saw, okay, you know, there's a looking at

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it from the standpoint of a United
States model, which it was likely to

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be initially, this has a real
First Amendment problem. And the way that

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Europe approached GDPR and the way that
we as the United States approach the use

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of information are very, very different. And so there we looked at that

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and we said, look, we
want there to be a privacy law.

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We're not opposing the idea that you
pass a privacy law, but you need

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to carve out publicly available information because
if you don't, the whole thing will

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go down in claims, right,
And so we successfully on behalf of our

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members and they were uniformly, I
think behind this advocated for language in that

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first in the CCPA and then in
the ballot initiative that carves out the use

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of publicly available data. So you
can't tell somebody who publishes, as a

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for example, of database of news
articles to stop selling information about you or

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to delete information about you, which
under GDPR is you know, theoretically it

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can have and certainly in the business
to business publishing space, which is another

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division of ours, that that kind
of thing is really important. Yeah.

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Well, and you know, I
remember when GDP R came out and they

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had this whole thing. I think
you might like this. They had this

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whole thing about the right to be
forgotten, which is where they're saying that

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if you're an EU citizen and you
want some corporation to delete whatever data it

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has about you, that you have
a right to do that, and the

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corporation has to follow through and go
ahead and delete your data. Well,

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as soon as I heard that,
I was like, good luck with that

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one. First of all, data
about someone in a company gets lots of

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places. It could be in this
database, it could be in that database,

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it could be in email exchanges between
people. There are lots of places

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where that data can live, and
quite frankly, in many organizations, there

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aren't too many people who know all
the places where that data can live,

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and there it's hard to query your
entire information landscape. I mean these days.

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Just this morning I did a call
with Justin Borgman, who's the CEO

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and co founder of a company called
Starburst, and they do federated queries like

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on a data lake, basically where
they can query all sorts of different systems,

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all sorts of different databases through one
abstraction layer and that's very cool,

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and it's also very new, right, So, and not every company has

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Starburst or Dramio or these kind of
tools. So point being, it puts

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a tremendous onus on the organizations to
be able to do all that. Besides

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which it's very difficult to know.
Is this the John Brown who lives at

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one on one Main Street of the
John Brown who lives at four o one

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First Street or something like? Just
knowing identity resolution is a challenge in and

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of itself. So my point is
GDPR pretty owner is stuff. I mean,

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I understand it's not always prosecuted.
So the idea is to have these

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sort of goals to strive toward.
But there's something I want to throw out

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and maybe just get your take on
all this stuff. I came up with

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this concept. I call it the
right to be respected. And what that

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means is I want anyone who's using
my data to respect my data and to

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respect my preferences. So if you
ask me how do I want to be

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contacted, I say by email,
not by phone. I would like for

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your operational system to reflect that and
for people to not call me and said

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to email me. Right, that
requires an architecture with policies that are baked

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in and adhered to. But I
think that's a more reasonable approach is to

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say to the X y Z organizations, yes, of course you need my

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data when I buy things or if
I sign up for stuff. That's all

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fine, but just you know,
be a careful and responsible steward of information

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and have some policy around that that
makes sense. But what do you think

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about all that? I mean to
me, that sounds very sensible, and

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I think you know the question that
where it becomes where it becomes sticky,

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I think is depending on who you
talk to. The idea the word respected

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carries a lot of weight, right
right, That does a lot of work.

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And so you know, for some
people respecting data men, you know,

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the second, the second that I
have conduct finished conducting business with your

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organization, I want you to take
all my data, put it on a

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hard drive, smash it into bits, and throw it in the sea so

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I know that it's gone. You
know, that's probably not terribly practical,

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right right. And other folks may
say, no, that's not generally,

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not how our well not generally,
it's not how our members roll. But

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they won't say, well, we
respected it we respected you enough to get

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the data from you by consent in
the first place, Right, are we

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done here? That's not Yeah,
that's that's not that's not practical either,

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and it's Look, nobody wants to
get spammed with tons of messages from a

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company that they just did business,
would want or God forbid called, right,

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And I think are there are some
natural market checks on that stuff.

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And that's an excellent point, right, is that the companies that do respect

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your privacy, that do respect your
data and you as a person, well,

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they're going to have a good brand, they're going to have a good

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reputation. And that's where you let
the market guide things. But to your

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point, you do need some kind
of policy, and I think the good

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news is that the technologies we have
today enable robust policy management. Ten years

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ago or twenty years ago, all
you can really do is govern access at

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the database level or at the application
layer, for example through a log in

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or something like that. But now
you really can't have policies that are baked

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into cloud based systems that are usually
role based. If this person is an

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accountant, they have access to accounting
data. If they're a and your executive,

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the access to a lot of different
data. All those kind of things

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are possible today, and that that
makes the process of policy design and implementation

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and enforcement a whole heck of a
lot easier, right, Oh for sure,

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I mean, I think, and
that's something even as a small organization,

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you know, we have where there
are people, you know, there

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are people who need to have the
keys to everything, but there ain't there

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ain't many of them, right right, right, And there are there are

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folks who need to be able to
access Microsoft Office. And that's essentially about

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it, right Uh, And it's
really important internally. I think technologies come

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a long way to allowing internal companies
to exercise those controls as well as I

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mean in terms of what our members
do. They even when they're dealing with

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customers, they there will be a
range of controls on what a particular customer

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might be able to do with what
they access and how that's determined. So

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that could be everything from refs and
warranties in a contract in terms in terms

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of use in the subscription agreement,
to IP monitoring and clearance, to sometimes

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even site visits to be sure that
this place that is licensing access to information

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is you know, in fact exists, right, and it's not some you

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know, it depends, it's they
adopt a risk based approach to all of

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this stuff. Yeah, that's a
good point. And you make another good

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point about having the need for people
who understand how the systems work. Right,

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if something is a black box,
well it's kind of hard to understand

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a black box. And now,
at the risk of getting ahead of ourselves

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a little bit here, artificial intelligence
large language models, right, these companies

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that build some of the first ones, Open AI, for example, I

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know a lot of people who know
about that whole process. And what I'm

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told is that early in the game
they realize they cannot just set these engines

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loose on the Internet at large,
because there's a lot of nonsense on the

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Internet. There is a lot of
true stuff, and there's a lot of

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stuff it's not true. There's speculation, there's sarcasm, they're all kinds of

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things, and so they had to
be very careful about how they trained the

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models. But they clearly train the
models on a corpus of data on the

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Internet. And so where do you
run into how do you even delineate copyright

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on something like that? And what's
fun is. If you ever ask chat

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GPT or Gemini, which is what
I use about copyright, it will give

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you a pretty thoughtful answer. I'll
say, well, actually, you know,

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you might want to talk to your
attorney or something like that, but

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what are your high level thoughts about
that? I realized it's a very sensitive

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subject, but I'm sure you guys
are talking about that, right we do.

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And this is one This is an
area I had mentioned. There are

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areas where members are agree, and
this is an area where they don't.

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But there are there are a few
points. And I'm you know, I'm

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a recovering copyright lawyer. Uh and
so you know, perfectly happy to have

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a relapse. But just so you
know, uh, you've been there,

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I've been there. So there are
there are definitely two sides to this story.

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And the on the one side,
you have a technology that at its

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core is looking for statistical relationships between
words and series of words. Basically,

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right, that's what it's interested in. Right, So a word, you

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know, quick brown, the next
likely word is fox is a lot more

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likely than ruda, bega. And
that's it's a physics model of sorts for

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language, and so in determining those
relationships. You are abstracting out a particular

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non fact, if you like.
And the argument on the one side would

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point to reverse engineering cases that have
held that type of life, that type

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of activity to be lawful. Right. That's one piece of it. The

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other piece of it is that on
the one hand, the work may be

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copied. The work was copied,
there's no doubt about that. It is

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being used to create works that may
not be literally similar but are nonetheless competitively

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substitutive for the works of the original
originating authors. That's an issue, and

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there may be issues. There may
be other issues in there around the weights

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assigned to particular kinds of content.
There may be issues around whether or not

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any of the content was parradical,
There may be issues around any number of

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things. But the thing is is
that these are issues that are uniquely poorly

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situated to have a legislature deal with
them, because they're fact specific, and

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that's how the courts resolve these things, one case at a time, and

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that's where our members are. So
even though they disagree about these things,

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and they think in some circumstances the
law to play one group will say,

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well, in this circumstance, the
law to apply in a particular way versus

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a different circumstance. That kind of
salami slicing is what needs to happen before

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there can really be any policy solution. That's very interesting. So for example,

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I remember, well, I guess
in Canada they just hit passed this

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law. In California, I think
they're doing something similar where the government is

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saying that if you link to a
bunch of news articles from media companies,

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you have to pay those people based
on the clicks and things of this nature.

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And so you know, what's the
law of undertended consequences doing here.

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It's saying, well, finally,
we just won't do that, so we

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won't disseminate the news anymore. We
won't disseminate this information. And it gets

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it kind of gets sticky, very
very quickly, and you start asking yourself,

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all right, does this make sense? And like, how would you

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compensate people for these things? Would
you have to open the kimono and show

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what you paid as an organization to
the author? I mean, wow,

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that conversation gets goes sideways pretty quickly. And I remember news aggregation sites,

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for example, where you just have
a bunch of links to other articles they're

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saying, oh, now you'd have
to pay those people. This has coming

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gone a few times. We've got
our first break coming up here in just

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a second, but picked this up
after the break. It is a bit

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of a stifling question to wonder,
well, hyperlinks are what the Internet is

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all about, Like at the very
core, it's just links to other stuff

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that you can then excit floor and
learn about and and have discovery and do

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all these kinds of educational things.
So if you start trying to track that,

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how would you even do that?
I mean, there's no one standard

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for being able to report on that
is everyone have to download a certain kind

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of software to that. I mean, it just it goes sideways so quickly,

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and I don't think that many people
really understand that a lot of these

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aspirations are either untenable or going to
have unintended consequences that are just so dire,

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and it really undercuts the whole mission
in the first place, of trying

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to protect the authors of some particular
piece of content. Somewhere it gets wild

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and wely real fast. But folks
don't touch that. Dell will be right

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back with Chris Moore. You were
listening to Inside Analysis. Welcome back to

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Inside Analysis. Here's your host,
Eric Tavanaugh. Right, folks, take

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us to the future, indeed,
talking all things information, information, life

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cycle, responsible information management. So
we're talking to Chris Moore of the Software

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and Information and Industry Association, Right
that right, then does it? So?

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S I I a dialing in from
our nation's capital, Washington, d

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C. And you mentioned in the
last segment for radical meaning pirateed and so

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if someone has trained their model on
pirated software, pirated movies for example,

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that's a whole separate area that needs
to be handled differently from a policy perspective.

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Right, do you want to talk
about that for a second. Sure,

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I mean I think I'm talking about
when I talk about that, what

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I'm talking about is in the context
of a copyright lawsuit, which is where

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all this is now. There is
uh there's there's a defense called fair use,

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which is kind of copyrights. If
you like copyrights, golden rule.

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It's not due unto others and run
it's it's to others as you would have

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them do uh do to you.
And it's designed to be a rule of

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reason, right, and so,
but it's one that's based in equity or

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fairness, and so your risk looks
different if you had kind of lawfully acquired

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a bunch of stuff, a bunch
of legitimate copies, if you legitimately got

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access to legitimate copies and use them, versus you've got access to illegitimate copies,

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infringing copies and use those, the
fairness balance looks different. It's the

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risk. The risk analysis is simply
different, you know. And that's saying

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it without opining on how the case
may turn out, right, because you

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there's lots of other facts that could
swing it one way or the other.

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But the risk looks different, right. Yeah, that's a very good point.

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And so what we're trying to do
is to find reasonable policies and I

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know all about fair use. It
basically says, look, I mean you

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can read something and remember it,
and then if you happen to quote it

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and you didn't realize you're quoting it
verbatim, well you're not doing something bad.

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You're just reflecting back. And what's
interesting is that's really kind of what

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these AI models are doing, the
large language models. The big AHA moment

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for me was understanding that when you
train one of these models on information,

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it's not actually persisting the verbatim text. Rather, it is adjusting. The

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engine adjusts its own weights and biases
and its parameters, which is kind of

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like how humans learn. I mean, these are neural nets that were designed

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to attempt to reflect how human beings
behave. But the point is it's not

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like copying and pasting later on.
It's absorbing this information, adjusting its parameters

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accordingly. And there are billions of
AMers in some of these models. And

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then it's reflecting back to you text
based upon your prompt. And it's a

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predictive engine. Basically, it's a
predictive engine that gives you what it thinks

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you're looking for based upon your prompt, which is a very different thing from

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just copying text and then pasting it
under a different name. Right, yeah,

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I mean, look, I had
a similar similar insight for me was

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that these engines are essentially a particular
kind of artists, which I wouldn't describe

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on a family show. I would
say. What I mean by that is

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they say, I mean, you
know, act as if you were so

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if you ask them a question and
say, you know, I would like

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the answer to this to a particular
question, can you explain the fair use

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defense. To me, you were
saying, act as if you are an

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expert on fair use, what would
you tell this person right? And then

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the quality of the data will determine
that answer. Right, it could be

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right, it could be wrong.
And when you get into more sensitive questions

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around security, you know, around
for example, I don't know, creating

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chemical weapons and stuff like that,
it requires a lot of thought and care

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to make sure that the engine doesn't
do what it's supposed to do. So

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to speak right well, And of
course there's dual use technologies. That's very

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common thing to watch out for in
the government, because you can use the

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stuff for fertilizer or to make a
bomb, right, And so you have

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to be able to understand the difference
between the two. And you know you're

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right that this comes into question where
rubber meets row when there's a lawsuit somewhere,

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and then you have to hash out
all the different issues and try to

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understand. But I think you're you're
on target to say that the fair use

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doctrine is meant to be fair,
as the name would indicate, it's meant

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to say, all right, there
are lines we can draw about how you

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can responsibly use these things, and
I understand it. In the music industry,

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there's some pretty precise rules around that, like how many notes in a

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row can be the same, and
things of this nature when push comes to

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shove in a court of laws somewhere. But just generally speaking, it's very

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interesting to me to see how these
large language models have triggered all these conversations

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around ethics and ethical AI and what
you can do. And I think the

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answer to most of that is solved
in two different ways. One is data

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governance and the other is transparency.
And the transparency argument. I'd be curious

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to hear your thoughts on that,
because of course you got LAMA two and

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LAMA three from Meta, which are
open source. Open AI used to be

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open source, it's not now it's
not a black box. And what do

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you think from your perspective, how
big a difference is that? How does

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that change things when it's a black
box versus an open source model? So

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that is I'm going to give you
an answer that's accurate and both technically accurate

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and practically useless. The answer is
that it depends right. I think you

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know because we've seen we have seen
these debates before, and by that I

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mean, you know, there was
a time when those who made and i'll

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put them in scare quote, put
it in scare quotes black Box software,

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were scared to death of open source, viewed it as a virus, one

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that needed, you know, a
proportionate response, right, and it turned

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out to be a really good way
of developing secure and interoperable systems in certain

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circumstances. There are times when you
will want that, there are times when

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you won't. I think here we
are seeing to some degree, a similar

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kind of we're seeing a similar drama
play out. I think it is a

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little different around the edges, but
there it really has to go. The

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way that it's different is that the
engines themselves are so flexible and so there.

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In other words, if you build, if you're building I don't know,

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you know, an open source video
rendering tool just looking at this screen,

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that's what it is. Whereas an
open AI, an open sourced AI

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model is different. So what is
the right approach to that The right approach

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to dealing with something like that is
going to be based on risk. In

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other words, you have to be
sure that as and this is the way

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that the US is progressing, this
is the way the White House is made

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clear that it wants to go.
They want a risk based approach to this.

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To give you a really simple answer, in our experience, I mean,

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we've been around for going on we're
creeping on fifty, which is a

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long time for a quote technology close
quote association. So you know, we've

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seen a few rodeos, and in
our experience anyway, technology neutral regulation tends

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to be the best way to approach
these things. In other words, you

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don't look so much. You look
at the risks of the technology creates.

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So fraud is still going to be
fraud. It doesn't matter if it's a

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scammer calling you in real life,
where if it's a bot scammer using AI

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to create a voice. They're both
okay, both of those things are scary.

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They are both. It's a dual
use technology, right. They will

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use it in movies, for example, to create a character that no longer

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exists or an actor's voice that can
no longer use his right. Right,

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that's dual use technology. But it's
still fraud, right, and there are

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remedies for that, And if there
are technology specific angles, that's what we

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need to look into as opposed to
saying you know, okay, and this

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is typical of the EU sort of
where they started was it's not coming out

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unless we approve it, right,
and that's that's treats you know, your

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Spotify algorithm the same way that it
would treat you know, power grid management.

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Mm hmm. That's just not that
doesn't make sense. Yeah, and

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you're reminded me of something too,
because deep fhase are definitely an issue.

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Certainly we're going to see this in
the political sphere. I've seen a couple

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that were very funny. I'm not
gonna lie. I won't say which one

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it was or who it was,
but it was like, what what did

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she say? We had to laugh
but were Okay, it's a deep fake,

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that's what's happening here. But I
think that probably and i'd be curious

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to your thought on this. There
are some very practical ways you can leverage

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technology to know if something is genuine
because most cameras are digital cameras these days,

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and they have a whole host of
metadata that's baked into that device about

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you know, what device it is, what time this thing was taking,

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what the location was, a lot
of them have geolocations, so if you

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pass all those markers, then that
looks good. Then you could say this

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photograph was of the wreckage from a
tornado in Kansas, and it checks out.

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Yep, it was in Kansas.
Yep, it was this date.

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Okay, that looks pretty good.
And you're starting to see I think it

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was meta and someone else say maybe
it was Google. They're going to try

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to create some watermarks on digitally altered
photography videos things of this nature. Also,

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I think that's I think that's a
very practical way to go, because

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then you'll be able to sense that
or your browser can sense that, and

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it'll say and you can turn it
on or off, like is this real

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or is this generated? I think
that's some pretty good stuff. Even when

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someone calls you on the phone,
is it a real voice? I mean,

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you know, I get calls and
I know the technology enerity, so

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I'm curious to see and like they'll
wait a second, Oh, how are

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you doing anyway? This is Jane
from such and such Up up, I'm

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like, okay, sure it is, like this is a computer voice talkingy

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But what do you think about all
that? About the practical ways we can

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leverage technology and existing formats and protocols
to kind of shepherd ourselves towards getting to

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the truth. So there's one of
the ideas I tend to be optimistic about

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the role of technology in this space. In other words, we will deploy

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things and then there will be there
will always be some kind of unintended consequence

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right right one way or another,
and then we figure out a way to

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fix it. So in this particular
space, content provenance, what you were

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talking about is that's exactly the that's
exactly the kind of thing that can greatly

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ameliorate the harm from from defix because
you'll know where the image came from and

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whether or not it's faltered, right. That's you know, that that kind

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of thing. It takes time.
Standards take doesn't take long to lie right,

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but it does take time to develop
standards for figuring out what the truth

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is, particularly when you have you
have a variety of companies with a variety

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of different kinds of IP getting together
and trying to figure out something that's going

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to work for everybody. That's how
the standards process works, and it's an

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incredibly valuable one, but it takes
time right, right, And we've seen

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we've seen the same kinds of things
happen in all kinds of areas, you

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know, content moderation, I know, it's a it's what they call a

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fraught topic, right, right,
But those algorithms get a lot of bad

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stuff down, They get a lot
and yes, is there are people doing

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bad things? Yes, are they
doing bad things on the internet? For

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sure? And it takes a and
it doesn't take the scale is so the

391
00:31:14.880 --> 00:31:18.720
numbers are just so big that even
a small failure rate is an enormous amount

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of available bad stuff. And it's
and it's hard to you know, it's

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hard to persuade people that they're people
are trying really hard when the numbers are

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00:31:36.079 --> 00:31:41.319
so big. But it's you know, it's kind of like from the oldest

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00:31:42.400 --> 00:31:45.960
proving your value as a council through
all of the bad things that didn't happen.

396
00:31:47.359 --> 00:31:49.880
Yeah, that's funny, right,
Like you can't you can't prove a

397
00:31:49.960 --> 00:31:56.519
negative, right the age old argument
or if we if they did, things

398
00:31:56.519 --> 00:32:00.160
would get really really bad. Right. Well, there you go with the

399
00:32:00.240 --> 00:32:04.960
law of the law of unintended consequences. And you know, boyd, can

400
00:32:05.039 --> 00:32:09.400
it come just screeching down the pike
if you throw down some mantra or some

401
00:32:09.480 --> 00:32:13.839
dictate about what can and cannot be
done. Again, I get to what

402
00:32:13.880 --> 00:32:17.119
I call the black market effect.
You know, when when rules and regulations

403
00:32:17.160 --> 00:32:22.599
are unreasonable and significantly unreasonable, you
can rest assured that people are going to

404
00:32:22.640 --> 00:32:23.599
go around them. They're gonna find
all the ways to do it. They're

405
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gonna offshore it, they're gonna get
deputies to do it, they're gonna pay

406
00:32:27.880 --> 00:32:30.279
people other ways to you know,
have them do it. I mean,

407
00:32:30.319 --> 00:32:32.440
there's just like a thousand things you
can do, and getting to the bottom

408
00:32:32.519 --> 00:32:36.240
of it is a very difficult thing. You know. I had a guy

409
00:32:36.519 --> 00:32:38.480
on this show long long time ago, I think in two thousand and nine,

410
00:32:38.640 --> 00:32:43.039
a guy I never forget his name, Chuck Nice from the Insurance Institutes

411
00:32:43.079 --> 00:32:45.799
of America, and he had this
great quote. He said, let me

412
00:32:45.799 --> 00:32:50.160
tell you, if an insurance company
wants to hide something, you're not gonna

413
00:32:50.160 --> 00:32:53.680
find it. It's like, wow, you know, I hadn't thought of

414
00:32:53.720 --> 00:32:55.640
it that way. He had a
great quote too, because we were talking

415
00:32:55.680 --> 00:33:00.240
about it. Could better governance have
prevented the housing in two thousand and eight

416
00:33:00.279 --> 00:33:04.759
of the market crash, basically the
money markets that went down and all that

417
00:33:04.759 --> 00:33:07.440
stuff. And he said, yeah, you know, I don't think so

418
00:33:07.559 --> 00:33:09.559
he goes, who would have known
that lending money to people who couldn't afford

419
00:33:09.559 --> 00:33:13.720
it was a bad idea? You
know? So you get back to that

420
00:33:13.799 --> 00:33:16.680
practicality argument, right. I mean, you can talk all day about theories

421
00:33:16.720 --> 00:33:20.000
and policies and all these kinds of
things, but at the end of the

422
00:33:20.079 --> 00:33:22.200
day, it has to be reasonable, it has to make sense. Well,

423
00:33:22.200 --> 00:33:24.680
folks, second segment is up here. Stand by, we'll be right

424
00:33:24.680 --> 00:33:36.440
back. You were listening to Inside
Analysis. Welcome back to Inside Analysis.

425
00:33:36.960 --> 00:33:43.480
Here's your host, Eric Tabanac.
All right, folks, back here talking

426
00:33:43.519 --> 00:33:49.960
to Chris Moore with the Software and
Information Industry Association or sii A. And

427
00:33:50.319 --> 00:33:54.039
Chris, here's a topic that I'm
dying to learn about data brokers. There

428
00:33:54.079 --> 00:33:58.640
is so much data being bought and
sold. There are all these contracts out

429
00:33:58.640 --> 00:34:00.559
there, A lot of good stuff
is happening. A lot of people have

430
00:34:00.599 --> 00:34:04.839
no idea that this is happening or
what's going on with it. But give

431
00:34:04.839 --> 00:34:08.280
me your take on data brokers and
what the policymakers are trying to achieve in

432
00:34:08.320 --> 00:34:15.559
that whole world. So I think, well, what they're trying to achieve

433
00:34:15.599 --> 00:34:19.760
I think is the question is not
what they're trying to achieve, because we

434
00:34:19.800 --> 00:34:23.360
share those goals, is really a
question of method, and they're very much

435
00:34:23.719 --> 00:34:28.000
at times it feels like they're very
much at the world Peace stage of thinking

436
00:34:30.320 --> 00:34:37.400
the World Peace statement. The problem. The problem we have is that the

437
00:34:37.440 --> 00:34:44.039
way they typically define data broker is
that a data broker is somebody that publishes

438
00:34:44.159 --> 00:34:50.360
personal information about someone else with whom
they don't have a direct relationship. That

439
00:34:50.480 --> 00:34:58.119
is incredibly broad, and it is
Our point of view is if you look

440
00:34:58.480 --> 00:35:01.519
at that's also a kind of if
you think you can just come in and

441
00:35:01.559 --> 00:35:06.760
regulate that, that's kind of a
European way of looking at it. In

442
00:35:06.800 --> 00:35:09.760
other words, that this property,
that this information is the property of the

443
00:35:09.800 --> 00:35:17.440
person who you're talking about. That
if you look at things that way,

444
00:35:17.800 --> 00:35:25.239
then the data broker regime makes sense. The problem with that is if you

445
00:35:25.280 --> 00:35:30.760
are talking about an entity that collects
information about other people with whom they don't

446
00:35:30.760 --> 00:35:36.480
have a direct relationship, what you're
talking about is regulating the activity of commercial

447
00:35:36.480 --> 00:35:43.719
publishing, and that has and you
can do that, but you have to

448
00:35:43.760 --> 00:35:45.920
look at it through a different lens, and there's a whole bunch of reasons

449
00:35:46.239 --> 00:35:52.079
why personally, it doesn't make sense
to think of personal information as property.

450
00:35:52.079 --> 00:35:55.360
And I'll give you a couple.
So the first reason is we have a

451
00:35:55.400 --> 00:36:00.000
first Amendment. So there is one
tranche of information that you can't regulate at

452
00:36:00.000 --> 00:36:06.519
all, right, and that's stuff
generally that's in the public domain, news

453
00:36:06.519 --> 00:36:12.360
worthy, public public information, information
that the government voluntarily releases that you're free

454
00:36:12.360 --> 00:36:15.079
to discuss and comment on, even
if it's about people. Especially if it's

455
00:36:15.079 --> 00:36:20.159
about people. You don't get to
yank that back out and say as a

456
00:36:20.199 --> 00:36:23.400
regulator, no, you can't discuss
this anymore. So that's one piece.

457
00:36:23.400 --> 00:36:28.639
So that's not property at all.
That belongs to everybody. So that's one

458
00:36:28.639 --> 00:36:37.480
piece. The second piece is information
that the government could regulate, but it

459
00:36:37.519 --> 00:36:42.440
needs a reason, so in other
words, because the first would get strict

460
00:36:42.480 --> 00:36:46.519
to something called strict scrutiny, which
is the test that if you're the government's

461
00:36:46.679 --> 00:36:50.679
on the government side of the v
you never want to see because you always

462
00:36:50.719 --> 00:36:59.440
lose right, right, the shorthanding, shorthanding centuries of constitutional law. That

463
00:37:00.639 --> 00:37:06.840
the middle pieces information that the government
probably could regulate if it does so in

464
00:37:06.880 --> 00:37:09.599
a thoughtful and measured way. And
plus there may be policy reasons why you

465
00:37:09.639 --> 00:37:16.239
want that information to circulate. Right, So, for example, financial information

466
00:37:17.199 --> 00:37:24.039
about individuals, you would want in
certain in certain circumstances to have people be

467
00:37:24.079 --> 00:37:30.400
able to access that information, for
example, for looking at politically exposed persons,

468
00:37:30.719 --> 00:37:34.440
or for money laundering purposes. You
want to be able to exchange that

469
00:37:34.519 --> 00:37:38.519
kind of information those kinds of things, or for credit. Right, you

470
00:37:38.559 --> 00:37:43.199
could regulate that information. You just
have to do so in a reasonable way.

471
00:37:43.639 --> 00:37:47.519
That's fine. And there's lots of
stuff, like there's lots of stuff

472
00:37:47.519 --> 00:37:52.320
that falls in that bucket where that
if it's done in a measured way that

473
00:37:52.360 --> 00:37:58.840
the potential the government can regulate the
potential for misuse because they're going to look

474
00:37:58.840 --> 00:38:01.559
at the risk from that and decide, okay, no, you can't use

475
00:38:01.599 --> 00:38:06.840
it for this. And then there's
a third tranch that's really easy to regulate,

476
00:38:07.239 --> 00:38:12.880
where the where the harm is so
obvious and intuitive from the use of

477
00:38:12.880 --> 00:38:17.880
that information that it follows the way
night follows day. So for example,

478
00:38:19.400 --> 00:38:24.840
unauthorized disrepute distribution of passwords or account
numbers or full SSNs, et cetera,

479
00:38:25.239 --> 00:38:30.400
like that type of stuff that has
a you know, yes, there are

480
00:38:30.400 --> 00:38:34.239
no ideas there. Really it's a
key of some kind. As an example,

481
00:38:34.559 --> 00:38:38.360
I see, it's it's more functional
than anything else. Right, That

482
00:38:38.480 --> 00:38:44.199
kind of stuff is easy, and
a commercial publisher of that type of information

483
00:38:44.320 --> 00:38:49.880
is probably not serving a wildly useful
purpose. Right, They're probably bad guys.

484
00:38:50.039 --> 00:38:55.280
They're probably bad guys. But there
are there are people who maybe who

485
00:38:55.280 --> 00:39:00.079
are you know, who may be
giving a who may be selling geo geo

486
00:39:00.199 --> 00:39:05.320
location data or other kinds of data
that have a lot of really valuable purposes

487
00:39:05.639 --> 00:39:09.400
that you would want to allow to
continue. And that is true both on

488
00:39:09.440 --> 00:39:15.440
the private sector side. It's also
true on the government side, where the

489
00:39:15.559 --> 00:39:21.599
availability of this kind of information is
a legitimate concern, but it becomes a

490
00:39:21.679 --> 00:39:25.480
proxy war for law enforcement priorities.
So, in other words, rather than

491
00:39:25.519 --> 00:39:35.519
saying, okay, you are using
certain kinds of information to track down folks

492
00:39:35.519 --> 00:39:38.639
who have overstayed their legal welcome in
the country, or if you're using it

493
00:39:38.679 --> 00:39:47.400
to track down pregnant women those you
know, therefore we ought to ban all

494
00:39:47.639 --> 00:39:53.199
law enforcement use of that information.
Right, Right? Do you really want

495
00:39:53.239 --> 00:39:57.719
that? Right? Right? I
mean, do you really not want to

496
00:39:57.760 --> 00:40:01.840
be able to get access to the
car tell operatives? Cell phone is that

497
00:40:02.320 --> 00:40:07.679
the end result here, right,
right, I mean those are that is

498
00:40:07.719 --> 00:40:13.679
a completely legitimate discussion with the cliffs. These the cliffs they talk about,

499
00:40:13.760 --> 00:40:15.159
right from a legal perspective, you
can go off the cliff this way,

500
00:40:15.199 --> 00:40:17.599
you can got the cliff that way. You don't want to go off either

501
00:40:17.639 --> 00:40:21.559
cliff. You want to stay somewhere
in the middle. Somewhere in the middle.

502
00:40:22.719 --> 00:40:25.079
But that you know, it's a
very charged environment around both of these

503
00:40:25.119 --> 00:40:30.119
things, because there is no doubt
there are people, like there are people

504
00:40:30.159 --> 00:40:32.760
doing bad things with data. There's
no doubt about that, right, I

505
00:40:32.760 --> 00:40:37.239
mean, there are people selling this
to people that they shouldn't sell it to

506
00:40:37.440 --> 00:40:40.519
I mean, our members don't.
Our members don't do this that kind of

507
00:40:40.559 --> 00:40:45.400
thing. That's why they I mean
they take know your customer type steps,

508
00:40:45.800 --> 00:40:51.000
right, right, that's different.
That's kind of part of the responsible action.

509
00:40:51.559 --> 00:40:53.880
Yeah, but we view it as
again, if you view it through

510
00:40:53.880 --> 00:40:59.800
a publishing lens, you it makes
to me, it makes more sense.

511
00:41:00.000 --> 00:41:05.760
It's in a sense easier to regulate
because you're focusing on specific risks and then

512
00:41:06.159 --> 00:41:08.280
you drill down on those risks and
you don't have to worry so much about

513
00:41:08.280 --> 00:41:13.639
the First Amendment because you're going to
be narrower and more focused by definition.

514
00:41:14.039 --> 00:41:16.199
Yeah, if you think of it
as property that's being used willy nilly,

515
00:41:17.119 --> 00:41:24.480
it's almost impossible. You're guaranteed your
chance of unintended consequences is it's almost guaranteed.

516
00:41:24.840 --> 00:41:28.920
Yeah. Wow, that's excellent.
Well, we've got time for one

517
00:41:28.960 --> 00:41:31.519
more thread. I think the segment's
ending here in a second, but I'm

518
00:41:31.559 --> 00:41:35.559
going to tease this and we'll pick
it up in the podcast bonus segment.

519
00:41:35.920 --> 00:41:39.119
Okay, this whole concept of alternative
data, which now is everywhere and data

520
00:41:39.119 --> 00:41:43.119
brokers are buying it and selling it
all over the place. And I've done

521
00:41:43.159 --> 00:41:46.280
some pretty good research on this,
and I realized that there are several companies

522
00:41:46.280 --> 00:41:51.159
I've talked to I know that are
capturing pretty much A lot of it is

523
00:41:51.239 --> 00:41:54.320
geared around what they call exhaust data
from credit card companies, but it's kind

524
00:41:54.360 --> 00:42:00.639
of a misleading term because they will
collect how much you spent at this restaurant,

525
00:42:00.639 --> 00:42:02.159
how much you spent at the gas
station. Like, all the detailed

526
00:42:02.159 --> 00:42:09.280
transactions are actually being bought and sold
to investment bankers, to other institutional investors,

527
00:42:10.000 --> 00:42:15.119
and so what happens is did some
of these folks have enough data now

528
00:42:15.519 --> 00:42:21.719
to get a baseline and understand consumer
behavior. And they can't get cash,

529
00:42:21.760 --> 00:42:23.239
but they can get credit cards.
I don't think they can get debit cards,

530
00:42:23.280 --> 00:42:28.119
but maybe some of them can.
And the point being, there are

531
00:42:28.159 --> 00:42:34.480
now organizations that know from the raw
transactional data at scale which companies are going

532
00:42:34.559 --> 00:42:37.519
to meet or beat market estimates on
Wall Street. Well, if you know

533
00:42:37.679 --> 00:42:42.519
that, then how the heck do
you lose when you gamble? Because the

534
00:42:42.559 --> 00:42:45.440
analogy I give is, let's say
there's four of us playing cards at my

535
00:42:45.519 --> 00:42:47.559
house, and the rule is I
can see all your cards, but none

536
00:42:47.599 --> 00:42:51.880
of you can see my cards.
Well, if I lose, I'm an

537
00:42:51.880 --> 00:42:54.920
idiot. I don't know the rules
of gambling, apparently because I have such

538
00:42:54.920 --> 00:42:59.159
an advantage now because I know what
all of you have and I know this

539
00:42:59.199 --> 00:43:01.280
is happening. Argument. I'll let
you think about this as we good to

540
00:43:01.360 --> 00:43:07.039
the break I thought was from a
policy perspective, Let's say that if a

541
00:43:07.079 --> 00:43:09.400
company of credit card company is going
to sell this exhaust data to one of

542
00:43:09.400 --> 00:43:16.679
these brokers, they must also publish
anonymized data to some consumer facing data lake

543
00:43:17.079 --> 00:43:22.599
that then any investor or any interested
party could log into and kind of browse

544
00:43:22.639 --> 00:43:27.519
around and see and better understand,
because that kind of information is very valuable

545
00:43:27.559 --> 00:43:31.519
if I run some kind of retail
operation and I can see at some scale

546
00:43:31.880 --> 00:43:37.559
which products are selling, which products
aren't selling, what's happening in this region,

547
00:43:37.559 --> 00:43:39.679
what's happening in that region. That
helps me do a better job of

548
00:43:39.719 --> 00:43:45.000
buying things that I know my customers
will probably want. Without revealing too much

549
00:43:45.039 --> 00:43:49.440
about where the data came from,
but at least it's useful, and what

550
00:43:49.480 --> 00:43:53.000
I'm really angling at is a way
of leveling the playing field between these insiders.

551
00:43:53.000 --> 00:43:54.760
And I even came up with a
term for what I call it,

552
00:43:54.800 --> 00:44:00.039
outsider trading. So insider trading.
Everyone understands that outsider trading is where from

553
00:44:00.039 --> 00:44:05.760
the outside I've been able to gather
so much information that I know exactly what's

554
00:44:05.760 --> 00:44:07.559
going to happen next. But we'll
pick that up after the break in one

555
00:44:07.840 --> 00:44:14.719
second. Be right back, all
right, folks, were back here with

556
00:44:14.800 --> 00:44:19.599
Chris Moore from SIIA. I just
threw a curveball question that him there.

557
00:44:19.840 --> 00:44:22.159
I'm curious to know what are your
thoughts about all this alternative data stuff and

558
00:44:22.559 --> 00:44:27.440
policies we can define that are reasonable. They will level of playing field.

559
00:44:28.400 --> 00:44:32.960
So I so a couple of things
there. I mean, one is I

560
00:44:34.440 --> 00:44:37.559
hear that word in a bunch of
different that phrase rather in a bunch of

561
00:44:37.639 --> 00:44:43.519
different contexts, and I think we're
talking about so I'm sure we're talking about

562
00:44:43.559 --> 00:44:51.559
the same thing. What you're talking
about is kind of information that's available that's

563
00:44:51.599 --> 00:44:57.320
beyond that's outside of sort of the
four corners of required reporting, right.

564
00:44:57.440 --> 00:45:01.800
That could be everything from as you
say, credit card exhaust credit company exhausts,

565
00:45:04.079 --> 00:45:09.519
credit card exhaust data, to social
media trend data. And so when

566
00:45:09.559 --> 00:45:21.079
this data is I think the way
the rules are now, this information is

567
00:45:21.119 --> 00:45:25.760
scrubbed so that there's no material non
public information in it. So there was

568
00:45:25.800 --> 00:45:30.599
a company called app Annie that got
into a bit of hurt with the FTC,

569
00:45:31.000 --> 00:45:37.840
with the SEC because it wasn't taking
that stuff out and lo and behold.

570
00:45:37.920 --> 00:45:42.599
Like you can imagine, there are
results, right, were pretty good,

571
00:45:44.480 --> 00:45:50.000
But that's not that's not what the
business is. What it is is

572
00:45:51.360 --> 00:45:55.559
finding information from these different sources.
You know, each of these firms has

573
00:45:55.599 --> 00:46:05.039
their own way of balancing and analyzing
it and then providing in as you said,

574
00:46:05.639 --> 00:46:09.719
recommendations, trading recommendations to or data
from which trades could be made to

575
00:46:12.239 --> 00:46:19.280
right to uh, you know,
to different types of investors. There is

576
00:46:19.400 --> 00:46:28.639
at the moment. I mean,
I think I would hesitate to mandate competition

577
00:46:28.840 --> 00:46:36.000
in that space. I would be
intuitively, I mean intuitively, I think

578
00:46:36.599 --> 00:46:42.559
you know, the business is that
business is I think growing. So in

579
00:46:42.599 --> 00:46:49.519
other words, the initially it started
okay, we have we're getting this information

580
00:46:50.480 --> 00:46:52.880
were as you said. I mean, a lot of this comes out of

581
00:46:52.880 --> 00:46:54.159
what you said. Like the first
one was like all right, hey we

582
00:46:54.199 --> 00:46:59.119
can do this. Let's do this
and sell it to hedge funds, right,

583
00:46:59.559 --> 00:47:00.719
and that is like, you know, who else would be interested in

584
00:47:00.800 --> 00:47:07.320
knowing all this stuff is, for
example, a retailer. Nothing wrong with

585
00:47:07.360 --> 00:47:13.039
that either, because all of this
information is de identified, so it's really

586
00:47:13.239 --> 00:47:17.480
just like there's no there's it's just
okay, there were this many sales and

587
00:47:19.280 --> 00:47:22.480
this is the these are the trend
lines, you know, make your bets

588
00:47:23.039 --> 00:47:29.159
right, right. And I think
as the you know, as the business

589
00:47:29.199 --> 00:47:32.440
evolves, my expectation is that it's
going to be that type of know how.

590
00:47:32.519 --> 00:47:37.000
It's going to become more and more
widely available because there's no reason,

591
00:47:37.159 --> 00:47:42.320
there's no reason for it not to
be interesting. That's a good point.

592
00:47:43.719 --> 00:47:46.840
H Yeah, Well we're talking about
the information life cycle right and where it

593
00:47:46.920 --> 00:47:50.039
is and where you can access it
and what you can do with it.

594
00:47:50.360 --> 00:47:52.760
And open data is a big thing
these days. I think that's wonderful news.

595
00:47:52.760 --> 00:47:57.719
There's a lot of information coming out. I was actually an advocate way

596
00:47:57.760 --> 00:48:01.519
back in two thousand and five when
I worked for the Data Warehousing Institute.

597
00:48:01.559 --> 00:48:06.079
I got at a big soapbox and
said, we need transparency in federal spending.

598
00:48:06.119 --> 00:48:09.360
And I did this whole media campaign. I had Bacon's media source at

599
00:48:09.400 --> 00:48:15.039
my access and so I emailed forty
thousand reporters all about the need for transparency

600
00:48:15.039 --> 00:48:20.039
and federal spending. Everyone told me
I was crazy, And then something happened.

601
00:48:20.079 --> 00:48:22.760
There was actually a guy at the
Heritage Foundation, Mark Tapscott, who

602
00:48:22.800 --> 00:48:23.920
picked up on it, and he
was like, why did you send me

603
00:48:23.960 --> 00:48:25.719
this? And I was like,
well, I went off my whole tangent.

604
00:48:25.800 --> 00:48:29.119
He goes, I've been focused on
this for twenty years, Like where

605
00:48:29.119 --> 00:48:31.320
have you been all my life?
And he then he used my article and

606
00:48:31.360 --> 00:48:36.199
really he was the imprimature of the
Data Warehousing Institute that he used to go

607
00:48:36.239 --> 00:48:40.440
testify before Congress and say we need
these citizen auditors to help us find waste

608
00:48:40.760 --> 00:48:45.280
and lo and behold, the House
passed the bill. The Senate passed the

609
00:48:45.320 --> 00:48:47.519
bill, co sponsored by a guy
named Barack Obama who was a Senator from

610
00:48:47.559 --> 00:48:52.000
Illinois. And on September twenty sixth, two thousand and six, then President

611
00:48:52.039 --> 00:48:57.119
George W. Bush signed the Federal
Funding Accountability and Transparency Law. And I

612
00:48:57.159 --> 00:48:59.719
almost had a heart attack on My
friend called me and told me, I

613
00:48:59.760 --> 00:49:01.400
was like, are you kidding me? No, I'm not going to say

614
00:49:01.400 --> 00:49:04.280
I got any credit for or anything, which is fine, you know,

615
00:49:04.679 --> 00:49:07.360
I don't mind, but I was
just absolutely shocked. And now when you

616
00:49:07.400 --> 00:49:10.119
look at it, you think my
theory back then was that, look,

617
00:49:10.199 --> 00:49:15.119
everybody knows something. So even though
some accountant may not know that this line

618
00:49:15.159 --> 00:49:19.639
on looks funky, there's someone who
does. There's a carpenter, someone who

619
00:49:19.679 --> 00:49:22.800
knows this kind of nail should only
cost ten cents, it should not cost

620
00:49:22.800 --> 00:49:24.800
a dollar and ten cents, or
whatever the case may be. And these

621
00:49:24.880 --> 00:49:30.719
days we have the capacity, like
the Amazon Mechanical Turk for example, to

622
00:49:30.960 --> 00:49:37.559
leverage crowdsourcing at scale, and so
if people register with the system, you

623
00:49:37.599 --> 00:49:39.719
know, I'm a citizen. You
know, everything I do in the system,

624
00:49:39.760 --> 00:49:43.559
when I flag something, if I'm
right or wrong, you can see

625
00:49:43.559 --> 00:49:46.119
how you could dynamically score these things
and then really wind up with a whole

626
00:49:46.159 --> 00:49:53.519
cadre of experts greening or information systems
for the government to help everyone know where

627
00:49:53.559 --> 00:49:58.719
the money goes. Because a lot
of times people worry about fraud. That

628
00:49:58.840 --> 00:50:00.760
is true. Bad things do happen, are just mistakes, They're just things

629
00:50:00.760 --> 00:50:05.079
that people overlook too. So being
able to find all that stuff, I

630
00:50:05.119 --> 00:50:07.199
think it's very very interesting. But
final thoughts from you, Chris Moore of

631
00:50:07.320 --> 00:50:12.639
s I I a here in the
show. Uh, you know, like

632
00:50:12.679 --> 00:50:16.239
I said, I'm mostly optimistic about
these technologies. Yeah, you know,

633
00:50:16.519 --> 00:50:22.360
do they need guardrails? Yeah,
they do. They really do. Because

634
00:50:22.159 --> 00:50:29.440
if if what you're trying to do
is mimic human intelligence, you know,

635
00:50:29.880 --> 00:50:32.239
human intelligence, we put guardrails around
that as well. There's things for not

636
00:50:32.280 --> 00:50:36.320
allowed to do and we have good
reasons for doing that. Right, pretty

637
00:50:36.400 --> 00:50:42.079
funny you go to put it.
But the so you know, there's going

638
00:50:42.159 --> 00:50:45.800
to be a need for that as
things develop, and what the type of

639
00:50:45.880 --> 00:50:50.199
thing that you are discussing is applied
to the government is tremendously useful. I

640
00:50:50.199 --> 00:50:53.119
think there are a number of private
businesses and even members who are selling all

641
00:50:53.199 --> 00:50:57.519
kinds of solutions that do the same
thing. Right, they go into your

642
00:50:58.000 --> 00:51:00.719
they go into your into your spreadhe
and they'll say, oh that's weird.

643
00:51:01.320 --> 00:51:05.639
Yeah, you know, for a
year over here and employee changes one year

644
00:51:05.679 --> 00:51:07.400
to the other. Maybe they don't
go back five years. Maybe they're about

645
00:51:07.440 --> 00:51:10.639
one year, right, you know, and chack to see how things have

646
00:51:10.760 --> 00:51:15.840
changed, and suddenly that looks hot
and it gets flat and that's how thanks

647
00:51:15.840 --> 00:51:17.800
to tech fraud too. Yeah right, well, hey, this has been

648
00:51:17.840 --> 00:51:22.679
this has been absolutely fantastic. Chris
more m HR. Look this organization up

649
00:51:22.679 --> 00:51:29.119
online. SIIA doing wonderful work.
You've made me more optimistic too about the

650
00:51:29.119 --> 00:51:30.679
future. So it's very good to
know. And they're doing great work in

651
00:51:30.760 --> 00:51:35.639
policy. Look them up online.
Folks, you've been listening to Inside Analysis,

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shut down in May. Back in
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nine, Mariah Carey, Stevie Wonder
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Radio has openings for one hour talk
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You can skype your show from your
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for details too. Eight one,
five nine nine ninety eight hundred NBC News

729
00:57:54.440 --> 00:57:59.559
Radio. I'm Chris Gragio. President
Biden is leveling some of his harshest criticism

730
00:57:59.639 --> 00:58:02.639
yet the Supreme Court. At a
fundraiser in Los Angeles yesterday. The President

731
00:58:02.679 --> 00:58:07.159
called the conservative majority court out of
step with the rest of the country.

732
00:58:07.360 --> 00:58:09.519
Biden also warned that a former President
Trump is elected, he would appoint more

733
00:58:09.679 --> 00:58:15.079
justices flying flags upside down, a
reference to conservative Justice Samuel Alito, who's

734
00:58:15.119 --> 00:58:20.480
been embroiled in controversy over political flags
flown at his home. The comments were

735
00:58:20.480 --> 00:58:24.960
made at a star studied fundraiser featuring
former President Obama and various A list celebrities.

736
00:58:25.079 --> 00:58:30.480
Biden's campaign says the event raised a
record breaking thirty million dollars from Democratic

737
00:58:30.480 --> 00:58:32.840
donors. Police saying i llegal immigrant
is under arrest for the rape and murder

738
00:58:32.840 --> 00:58:37.400
of a Maryland woman last August.
In a news conference yesterday, Harford County

739
00:58:37.440 --> 00:58:42.039
Sheriff Jeffrey Gaylor said a twenty three
year old Victor Antonio Martinez Hernandez was arrested

740
00:58:42.039 --> 00:58:45.519
Friday night in Tulsa, Oklahoma,
and his waiting extradition. Rachel Moran,

741
00:58:45.719 --> 00:58:47.920
a thirty seven year old mother of
five, had been reported missing on August

742
00:58:47.960 --> 00:58:52.679
fifth of last year, and her
body was found the next day. Maryland

743
00:58:52.679 --> 00:58:55.519
Governor Wes Moore is saying it'll likely
cost close to two billion dollars to rebuild

744
00:58:55.559 --> 00:59:00.000
Baltimore's Francis Scott key Bridge. In
an interview with CBS's Face the Night,

745
00:59:00.639 --> 00:59:02.719
he said, this state needs federal
money to get the project completed in a

746
00:59:02.760 --> 00:59:07.400
timely manner, so we know the
estimated cost could be between one point seven

747
00:59:07.440 --> 00:59:10.039
and one point nine billion dollars.
And the reason that we are urging of

748
00:59:10.079 --> 00:59:14.920
federal cost share and a one hundred
pcent cost share is just simply because we

749
00:59:15.119 --> 00:59:19.320
have to move quickly. The federal
cost share means the federal government will cover

750
00:59:19.400 --> 00:59:22.480
the entire cost. The governor also
praised cleanup crews for their hard work reopening

751
00:59:22.519 --> 00:59:25.920
the shipping channel ahead of schedule,
saying they were able to reopen it in

752
00:59:25.960 --> 00:59:30.519
eleven weeks despite estimates it would take
eleven months. Millions are celebrating Father's Day

753
00:59:30.559 --> 00:59:35.000
today, but its origins are not
well known. It wasn't until nineteen seventy

754
00:59:35.000 --> 00:59:38.480
two that Father's Day was recognized in
America after being signed off by President Nixon.

755
00:59:38.760 --> 00:59:42.920
So Norah Dodd, the daughter of
a Civil War veteran, is credited

756
00:59:42.920 --> 00:59:45.480
with organizing the first ever one back
in nineteen ten to honor her own father,

757
00:59:45.760 --> 00:59:49.599
and wanted the day to be in
June. I'm Chris Karagio, NBC

758
00:59:49.679 --> 00:59:57.519
News Radio, NBC News on CACAA
Lowland, sponsored by Teamsters Local nineteen thirty

759
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two, Protecting the Future of Working
Families Teamster nineteen thirty two dot org.

760
01:00:06.960 --> 01:00:08.280
You're listening to an encore presentation of
this