Dot Comsideanalysis dot com, and now here's your host to Eric Kavanaugh and Flare.
All right, ladies and gentlemen, Hello and welcome back once again on
the only coast to coast radio show in the US of ATA. It's all
about the information economy Inside Analysis. Yours truly, Eric Kavanaugh here, and
folks, I'm very excited to be dialed into our nation's capital. We're going
to talk to an industry visionary today. We've got mister Chris Moore on the
line from the Software and Information Industry Association or SIIA. We're going to talk
all about data and the data life cycle and protecting the data life cycle and
frankly being responsible stewards of data. And that's good for lots of different reasons,
one of which happens to be this explosion of artificial intelligence. So it's
not just people using data and people processing data, and now there are machines
that are using data to train on and to do all sorts of different things.
And that is very, very disruptive, and I think it's going to
continue to be disruptive. So we want to talk also about responsible AI and
how to get there and the short answers a lot of it has to do
with data. And with that, let me welcome Chris More to the show.
Thanks for your time today, tell us a bit about yourself and about
the SIIA. Sure, my pleasure. It's good to be with you,
and thank you for the invite. Thank you, Ben. We are What
we like to say is we're a place for folks who are in the business
of information. And what we do is we look to preserve and protect a
healthy information life cycle. In other words, we want to make sure that
there is a good environment for its creation, dissemination, and productive use.
And our members range from platform companies you're probably well familiar with, as well
as educational publishers in news organizations and database publishers and financial data firms. Okay,
and you get into a lot of different policy areas obviously. I mean
there's intellectual property, there's privacy, there's cybersecurity, and just the use of
data. So when you talk about the data life cycle, I presume you're
talking about data from its creation to its grave, basically, from its inception
through its use all the way to its deprecation, which in many cases doesn't
even happen because it just sits around forever. But a lot of cases,
companies will follow process and procedure and after X period of years, whatever the
regulation is, they will delete that data and move on. Can you talk
about how that works? I mean, you are an association, so you
have leaders from many of these big companies I guess over eight hundred members who
come together to forums and really talk about this stuff and kind of hash out
what to do. Because I know from experience policy is hard, and policies
need to be understandable, they need to be known, and they need to
be reasonable in order for people to follow them. So tell us a bit
about how you go about doing that and maybe give us some examples. Uh.
Sure, I mean I think there there are areas, for example,
uh, where there's a pretty good bit of consensus within our membership, which
is the easiest place to take policy rights. And so for example, let's
talk about let's talk about privacy. That's right, that's a hot topic.
And is there an AI wrinkle? Absolutely there is. Uh. But that's
an area where we saw a bunch of you know, we saw state laws
coming from California in their in their signature law, and uh we looked at
the text and we saw, okay, you know, there's a looking at
it from the standpoint of a United States model, which it was likely to
be initially, this has a real First Amendment problem. And the way that
Europe approached GDPR and the way that we as the United States approach the use
of information are very, very different. And so there we looked at that
and we said, look, we want there to be a privacy law.
We're not opposing the idea that you pass a privacy law, but you need
to carve out publicly available information because if you don't, the whole thing will
go down in claims, right, And so we successfully on behalf of our
members and they were uniformly, I think behind this advocated for language in that
first in the CCPA and then in the ballot initiative that carves out the use
of publicly available data. So you can't tell somebody who publishes, as a
for example, of database of news articles to stop selling information about you or
to delete information about you, which under GDPR is you know, theoretically it
can have and certainly in the business to business publishing space, which is another
division of ours, that that kind of thing is really important. Yeah.
Well, and you know, I remember when GDP R came out and they
had this whole thing. I think you might like this. They had this
whole thing about the right to be forgotten, which is where they're saying that
if you're an EU citizen and you want some corporation to delete whatever data it
has about you, that you have a right to do that, and the
corporation has to follow through and go ahead and delete your data. Well,
as soon as I heard that, I was like, good luck with that
one. First of all, data about someone in a company gets lots of
places. It could be in this database, it could be in that database,
it could be in email exchanges between people. There are lots of places
where that data can live, and quite frankly, in many organizations, there
aren't too many people who know all the places where that data can live,
and there it's hard to query your entire information landscape. I mean these days.
Just this morning I did a call with Justin Borgman, who's the CEO
and co founder of a company called Starburst, and they do federated queries like
on a data lake, basically where they can query all sorts of different systems,
all sorts of different databases through one abstraction layer and that's very cool,
and it's also very new, right, So, and not every company has
Starburst or Dramio or these kind of tools. So point being, it puts
a tremendous onus on the organizations to be able to do all that. Besides
which it's very difficult to know. Is this the John Brown who lives at
one on one Main Street of the John Brown who lives at four o one
First Street or something like? Just knowing identity resolution is a challenge in and
of itself. So my point is GDPR pretty owner is stuff. I mean,
I understand it's not always prosecuted. So the idea is to have these
sort of goals to strive toward. But there's something I want to throw out
and maybe just get your take on all this stuff. I came up with
this concept. I call it the right to be respected. And what that
means is I want anyone who's using my data to respect my data and to
respect my preferences. So if you ask me how do I want to be
contacted, I say by email, not by phone. I would like for
your operational system to reflect that and for people to not call me and said
to email me. Right, that requires an architecture with policies that are baked
in and adhered to. But I think that's a more reasonable approach is to
say to the X y Z organizations, yes, of course you need my
data when I buy things or if I sign up for stuff. That's all
fine, but just you know, be a careful and responsible steward of information
and have some policy around that that makes sense. But what do you think
about all that? I mean to me, that sounds very sensible, and
I think you know the question that where it becomes where it becomes sticky,
I think is depending on who you talk to. The idea the word respected
carries a lot of weight, right right, That does a lot of work.
And so you know, for some people respecting data men, you know,
the second, the second that I have conduct finished conducting business with your
organization, I want you to take all my data, put it on a
hard drive, smash it into bits, and throw it in the sea so
I know that it's gone. You know, that's probably not terribly practical,
right right. And other folks may say, no, that's not generally,
not how our well not generally, it's not how our members roll. But
they won't say, well, we respected it we respected you enough to get
the data from you by consent in the first place, Right, are we
done here? That's not Yeah, that's that's not that's not practical either,
and it's Look, nobody wants to get spammed with tons of messages from a
company that they just did business, would want or God forbid called, right,
And I think are there are some natural market checks on that stuff.
And that's an excellent point, right, is that the companies that do respect
your privacy, that do respect your data and you as a person, well,
they're going to have a good brand, they're going to have a good
reputation. And that's where you let the market guide things. But to your
point, you do need some kind of policy, and I think the good
news is that the technologies we have today enable robust policy management. Ten years
ago or twenty years ago, all you can really do is govern access at
the database level or at the application layer, for example through a log in
or something like that. But now you really can't have policies that are baked
into cloud based systems that are usually role based. If this person is an
accountant, they have access to accounting data. If they're a and your executive,
the access to a lot of different data. All those kind of things
are possible today, and that that makes the process of policy design and implementation
and enforcement a whole heck of a lot easier, right, Oh for sure,
I mean, I think, and that's something even as a small organization,
you know, we have where there are people, you know, there
are people who need to have the keys to everything, but there ain't there
ain't many of them, right right, right, And there are there are
folks who need to be able to access Microsoft Office. And that's essentially about
it, right Uh, And it's really important internally. I think technologies come
a long way to allowing internal companies to exercise those controls as well as I
mean in terms of what our members do. They even when they're dealing with
customers, they there will be a range of controls on what a particular customer
might be able to do with what they access and how that's determined. So
that could be everything from refs and warranties in a contract in terms in terms
of use in the subscription agreement, to IP monitoring and clearance, to sometimes
even site visits to be sure that this place that is licensing access to information
is you know, in fact exists, right, and it's not some you
know, it depends, it's they adopt a risk based approach to all of
this stuff. Yeah, that's a good point. And you make another good
point about having the need for people who understand how the systems work. Right,
if something is a black box, well it's kind of hard to understand
a black box. And now, at the risk of getting ahead of ourselves
a little bit here, artificial intelligence large language models, right, these companies
that build some of the first ones, Open AI, for example, I
know a lot of people who know about that whole process. And what I'm
told is that early in the game they realize they cannot just set these engines
loose on the Internet at large, because there's a lot of nonsense on the
Internet. There is a lot of true stuff, and there's a lot of
stuff it's not true. There's speculation, there's sarcasm, they're all kinds of
things, and so they had to be very careful about how they trained the
models. But they clearly train the models on a corpus of data on the
Internet. And so where do you run into how do you even delineate copyright
on something like that? And what's fun is. If you ever ask chat
GPT or Gemini, which is what I use about copyright, it will give
you a pretty thoughtful answer. I'll say, well, actually, you know,
you might want to talk to your attorney or something like that, but
what are your high level thoughts about that? I realized it's a very sensitive
subject, but I'm sure you guys are talking about that, right we do.
And this is one This is an area I had mentioned. There are
areas where members are agree, and this is an area where they don't.
But there are there are a few points. And I'm you know, I'm
a recovering copyright lawyer. Uh and so you know, perfectly happy to have
a relapse. But just so you know, uh, you've been there,
I've been there. So there are there are definitely two sides to this story.
And the on the one side, you have a technology that at its
core is looking for statistical relationships between words and series of words. Basically,
right, that's what it's interested in. Right, So a word, you
know, quick brown, the next likely word is fox is a lot more
likely than ruda, bega. And that's it's a physics model of sorts for
language, and so in determining those relationships. You are abstracting out a particular
non fact, if you like. And the argument on the one side would
point to reverse engineering cases that have held that type of life, that type
of activity to be lawful. Right. That's one piece of it. The
other piece of it is that on the one hand, the work may be
copied. The work was copied, there's no doubt about that. It is
being used to create works that may not be literally similar but are nonetheless competitively
substitutive for the works of the original originating authors. That's an issue, and
there may be issues. There may be other issues in there around the weights
assigned to particular kinds of content. There may be issues around whether or not
any of the content was parradical, There may be issues around any number of
things. But the thing is is that these are issues that are uniquely poorly
situated to have a legislature deal with them, because they're fact specific, and
that's how the courts resolve these things, one case at a time, and
that's where our members are. So even though they disagree about these things,
and they think in some circumstances the law to play one group will say,
well, in this circumstance, the law to apply in a particular way versus
a different circumstance. That kind of salami slicing is what needs to happen before
there can really be any policy solution. That's very interesting. So for example,
I remember, well, I guess in Canada they just hit passed this
law. In California, I think they're doing something similar where the government is
saying that if you link to a bunch of news articles from media companies,
you have to pay those people based on the clicks and things of this nature.
And so you know, what's the law of undertended consequences doing here.
It's saying, well, finally, we just won't do that, so we
won't disseminate the news anymore. We won't disseminate this information. And it gets
it kind of gets sticky, very very quickly, and you start asking yourself,
all right, does this make sense? And like, how would you
compensate people for these things? Would you have to open the kimono and show
what you paid as an organization to the author? I mean, wow,
that conversation gets goes sideways pretty quickly. And I remember news aggregation sites,
for example, where you just have a bunch of links to other articles they're
saying, oh, now you'd have to pay those people. This has coming
gone a few times. We've got our first break coming up here in just
a second, but picked this up after the break. It is a bit
of a stifling question to wonder, well, hyperlinks are what the Internet is
all about, Like at the very core, it's just links to other stuff
that you can then excit floor and learn about and and have discovery and do
all these kinds of educational things. So if you start trying to track that,
how would you even do that? I mean, there's no one standard
for being able to report on that is everyone have to download a certain kind
of software to that. I mean, it just it goes sideways so quickly,
and I don't think that many people really understand that a lot of these
aspirations are either untenable or going to have unintended consequences that are just so dire,
and it really undercuts the whole mission in the first place, of trying
to protect the authors of some particular piece of content. Somewhere it gets wild
and wely real fast. But folks don't touch that. Dell will be right
back with Chris Moore. You were listening to Inside Analysis. Welcome back to
Inside Analysis. Here's your host, Eric Tavanaugh. Right, folks, take
us to the future, indeed, talking all things information, information, life
cycle, responsible information management. So we're talking to Chris Moore of the Software
and Information and Industry Association, Right that right, then does it? So?
S I I a dialing in from our nation's capital, Washington, d
C. And you mentioned in the last segment for radical meaning pirateed and so
if someone has trained their model on pirated software, pirated movies for example,
that's a whole separate area that needs to be handled differently from a policy perspective.
Right, do you want to talk about that for a second. Sure,
I mean I think I'm talking about when I talk about that, what
I'm talking about is in the context of a copyright lawsuit, which is where
all this is now. There is uh there's there's a defense called fair use,
which is kind of copyrights. If you like copyrights, golden rule.
It's not due unto others and run it's it's to others as you would have
them do uh do to you. And it's designed to be a rule of
reason, right, and so, but it's one that's based in equity or
fairness, and so your risk looks different if you had kind of lawfully acquired
a bunch of stuff, a bunch of legitimate copies, if you legitimately got
access to legitimate copies and use them, versus you've got access to illegitimate copies,
infringing copies and use those, the fairness balance looks different. It's the
risk. The risk analysis is simply different, you know. And that's saying
it without opining on how the case may turn out, right, because you
there's lots of other facts that could swing it one way or the other.
But the risk looks different, right. Yeah, that's a very good point.
And so what we're trying to do is to find reasonable policies and I
know all about fair use. It basically says, look, I mean you
can read something and remember it, and then if you happen to quote it
and you didn't realize you're quoting it verbatim, well you're not doing something bad.
You're just reflecting back. And what's interesting is that's really kind of what
these AI models are doing, the large language models. The big AHA moment
for me was understanding that when you train one of these models on information,
it's not actually persisting the verbatim text. Rather, it is adjusting. The
engine adjusts its own weights and biases and its parameters, which is kind of
like how humans learn. I mean, these are neural nets that were designed
to attempt to reflect how human beings behave. But the point is it's not
like copying and pasting later on. It's absorbing this information, adjusting its parameters
accordingly. And there are billions of AMers in some of these models. And
then it's reflecting back to you text based upon your prompt. And it's a
predictive engine. Basically, it's a predictive engine that gives you what it thinks
you're looking for based upon your prompt, which is a very different thing from
just copying text and then pasting it under a different name. Right, yeah,
I mean, look, I had a similar similar insight for me was
that these engines are essentially a particular kind of artists, which I wouldn't describe
on a family show. I would say. What I mean by that is
they say, I mean, you know, act as if you were so
if you ask them a question and say, you know, I would like
the answer to this to a particular question, can you explain the fair use
defense. To me, you were saying, act as if you are an
expert on fair use, what would you tell this person right? And then
the quality of the data will determine that answer. Right, it could be
right, it could be wrong. And when you get into more sensitive questions
around security, you know, around for example, I don't know, creating
chemical weapons and stuff like that, it requires a lot of thought and care
to make sure that the engine doesn't do what it's supposed to do. So
to speak right well, And of course there's dual use technologies. That's very
common thing to watch out for in the government, because you can use the
stuff for fertilizer or to make a bomb, right, And so you have
to be able to understand the difference between the two. And you know you're
right that this comes into question where rubber meets row when there's a lawsuit somewhere,
and then you have to hash out all the different issues and try to
understand. But I think you're you're on target to say that the fair use
doctrine is meant to be fair, as the name would indicate, it's meant
to say, all right, there are lines we can draw about how you
can responsibly use these things, and I understand it. In the music industry,
there's some pretty precise rules around that, like how many notes in a
row can be the same, and things of this nature when push comes to
shove in a court of laws somewhere. But just generally speaking, it's very
interesting to me to see how these large language models have triggered all these conversations
around ethics and ethical AI and what you can do. And I think the
answer to most of that is solved in two different ways. One is data
governance and the other is transparency. And the transparency argument. I'd be curious
to hear your thoughts on that, because of course you got LAMA two and
LAMA three from Meta, which are open source. Open AI used to be
open source, it's not now it's not a black box. And what do
you think from your perspective, how big a difference is that? How does
that change things when it's a black box versus an open source model? So
that is I'm going to give you an answer that's accurate and both technically accurate
and practically useless. The answer is that it depends right. I think you
know because we've seen we have seen these debates before, and by that I
mean, you know, there was a time when those who made and i'll
put them in scare quote, put it in scare quotes black Box software,
were scared to death of open source, viewed it as a virus, one
that needed, you know, a proportionate response, right, and it turned
out to be a really good way of developing secure and interoperable systems in certain
circumstances. There are times when you will want that, there are times when
you won't. I think here we are seeing to some degree, a similar
kind of we're seeing a similar drama play out. I think it is a
little different around the edges, but there it really has to go. The
way that it's different is that the engines themselves are so flexible and so there.
In other words, if you build, if you're building I don't know,
you know, an open source video rendering tool just looking at this screen,
that's what it is. Whereas an open AI, an open sourced AI
model is different. So what is the right approach to that The right approach
to dealing with something like that is going to be based on risk. In
other words, you have to be sure that as and this is the way
that the US is progressing, this is the way the White House is made
clear that it wants to go. They want a risk based approach to this.
To give you a really simple answer, in our experience, I mean,
we've been around for going on we're creeping on fifty, which is a
long time for a quote technology close quote association. So you know, we've
seen a few rodeos, and in our experience anyway, technology neutral regulation tends
to be the best way to approach these things. In other words, you
don't look so much. You look at the risks of the technology creates.
So fraud is still going to be fraud. It doesn't matter if it's a
scammer calling you in real life, where if it's a bot scammer using AI
to create a voice. They're both okay, both of those things are scary.
They are both. It's a dual use technology, right. They will
use it in movies, for example, to create a character that no longer
exists or an actor's voice that can no longer use his right. Right,
that's dual use technology. But it's still fraud, right, and there are
remedies for that, And if there are technology specific angles, that's what we
need to look into as opposed to saying you know, okay, and this
is typical of the EU sort of where they started was it's not coming out
unless we approve it, right, and that's that's treats you know, your
Spotify algorithm the same way that it would treat you know, power grid management.
Mm hmm. That's just not that doesn't make sense. Yeah, and
you're reminded me of something too, because deep fhase are definitely an issue.
Certainly we're going to see this in the political sphere. I've seen a couple
that were very funny. I'm not gonna lie. I won't say which one
it was or who it was, but it was like, what what did
she say? We had to laugh but were Okay, it's a deep fake,
that's what's happening here. But I think that probably and i'd be curious
to your thought on this. There are some very practical ways you can leverage
technology to know if something is genuine because most cameras are digital cameras these days,
and they have a whole host of metadata that's baked into that device about
you know, what device it is, what time this thing was taking,
what the location was, a lot of them have geolocations, so if you
pass all those markers, then that looks good. Then you could say this
photograph was of the wreckage from a tornado in Kansas, and it checks out.
Yep, it was in Kansas. Yep, it was this date.
Okay, that looks pretty good. And you're starting to see I think it
was meta and someone else say maybe it was Google. They're going to try
to create some watermarks on digitally altered photography videos things of this nature. Also,
I think that's I think that's a very practical way to go, because
then you'll be able to sense that or your browser can sense that, and
it'll say and you can turn it on or off, like is this real
or is this generated? I think that's some pretty good stuff. Even when
someone calls you on the phone, is it a real voice? I mean,
you know, I get calls and I know the technology enerity, so
I'm curious to see and like they'll wait a second, Oh, how are
you doing anyway? This is Jane from such and such Up up, I'm
like, okay, sure it is, like this is a computer voice talkingy
But what do you think about all that? About the practical ways we can
leverage technology and existing formats and protocols to kind of shepherd ourselves towards getting to
the truth. So there's one of the ideas I tend to be optimistic about
the role of technology in this space. In other words, we will deploy
things and then there will be there will always be some kind of unintended consequence
right right one way or another, and then we figure out a way to
fix it. So in this particular space, content provenance, what you were
talking about is that's exactly the that's exactly the kind of thing that can greatly
ameliorate the harm from from defix because you'll know where the image came from and
whether or not it's faltered, right. That's you know, that that kind
of thing. It takes time. Standards take doesn't take long to lie right,
but it does take time to develop standards for figuring out what the truth
is, particularly when you have you have a variety of companies with a variety
of different kinds of IP getting together and trying to figure out something that's going
to work for everybody. That's how the standards process works, and it's an
incredibly valuable one, but it takes time right, right, And we've seen
we've seen the same kinds of things happen in all kinds of areas, you
know, content moderation, I know, it's a it's what they call a
fraught topic, right, right, But those algorithms get a lot of bad
stuff down, They get a lot and yes, is there are people doing
bad things? Yes, are they doing bad things on the internet? For
sure? And it takes a and it doesn't take the scale is so the
numbers are just so big that even a small failure rate is an enormous amount
of available bad stuff. And it's and it's hard to you know, it's
hard to persuade people that they're people are trying really hard when the numbers are
so big. But it's you know, it's kind of like from the oldest
proving your value as a council through all of the bad things that didn't happen.
Yeah, that's funny, right, Like you can't you can't prove a
negative, right the age old argument or if we if they did, things
would get really really bad. Right. Well, there you go with the
law of the law of unintended consequences. And you know, boyd, can
it come just screeching down the pike if you throw down some mantra or some
dictate about what can and cannot be done. Again, I get to what
I call the black market effect. You know, when when rules and regulations
are unreasonable and significantly unreasonable, you can rest assured that people are going to
go around them. They're gonna find all the ways to do it. They're
gonna offshore it, they're gonna get deputies to do it, they're gonna pay
people other ways to you know, have them do it. I mean,
there's just like a thousand things you can do, and getting to the bottom
of it is a very difficult thing. You know. I had a guy
on this show long long time ago, I think in two thousand and nine,
a guy I never forget his name, Chuck Nice from the Insurance Institutes
of America, and he had this great quote. He said, let me
tell you, if an insurance company wants to hide something, you're not gonna
find it. It's like, wow, you know, I hadn't thought of
it that way. He had a great quote too, because we were talking
about it. Could better governance have prevented the housing in two thousand and eight
of the market crash, basically the money markets that went down and all that
stuff. And he said, yeah, you know, I don't think so
he goes, who would have known that lending money to people who couldn't afford
it was a bad idea? You know? So you get back to that
practicality argument, right. I mean, you can talk all day about theories
and policies and all these kinds of things, but at the end of the
day, it has to be reasonable, it has to make sense. Well,
folks, second segment is up here. Stand by, we'll be right
back. You were listening to Inside Analysis. Welcome back to Inside Analysis.
Here's your host, Eric Tabanac. All right, folks, back here talking
to Chris Moore with the Software and Information Industry Association or sii A. And
Chris, here's a topic that I'm dying to learn about data brokers. There
is so much data being bought and sold. There are all these contracts out
there, A lot of good stuff is happening. A lot of people have
no idea that this is happening or what's going on with it. But give
me your take on data brokers and what the policymakers are trying to achieve in
that whole world. So I think, well, what they're trying to achieve
I think is the question is not what they're trying to achieve, because we
share those goals, is really a question of method, and they're very much
at times it feels like they're very much at the world Peace stage of thinking
the World Peace statement. The problem. The problem we have is that the
way they typically define data broker is that a data broker is somebody that publishes
personal information about someone else with whom they don't have a direct relationship. That
is incredibly broad, and it is Our point of view is if you look
at that's also a kind of if you think you can just come in and
regulate that, that's kind of a European way of looking at it. In
other words, that this property, that this information is the property of the
person who you're talking about. That if you look at things that way,
then the data broker regime makes sense. The problem with that is if you
are talking about an entity that collects information about other people with whom they don't
have a direct relationship, what you're talking about is regulating the activity of commercial
publishing, and that has and you can do that, but you have to
look at it through a different lens, and there's a whole bunch of reasons
why personally, it doesn't make sense to think of personal information as property.
And I'll give you a couple. So the first reason is we have a
first Amendment. So there is one tranche of information that you can't regulate at
all, right, and that's stuff generally that's in the public domain, news
worthy, public public information, information that the government voluntarily releases that you're free
to discuss and comment on, even if it's about people. Especially if it's
about people. You don't get to yank that back out and say as a
regulator, no, you can't discuss this anymore. So that's one piece.
So that's not property at all. That belongs to everybody. So that's one
piece. The second piece is information that the government could regulate, but it
needs a reason, so in other words, because the first would get strict
to something called strict scrutiny, which is the test that if you're the government's
on the government side of the v you never want to see because you always
lose right, right, the shorthanding, shorthanding centuries of constitutional law. That
the middle pieces information that the government probably could regulate if it does so in
a thoughtful and measured way. And plus there may be policy reasons why you
want that information to circulate. Right, So, for example, financial information
about individuals, you would want in certain in certain circumstances to have people be
able to access that information, for example, for looking at politically exposed persons,
or for money laundering purposes. You want to be able to exchange that
kind of information those kinds of things, or for credit. Right, you
could regulate that information. You just have to do so in a reasonable way.
That's fine. And there's lots of stuff, like there's lots of stuff
that falls in that bucket where that if it's done in a measured way that
the potential the government can regulate the potential for misuse because they're going to look
at the risk from that and decide, okay, no, you can't use
it for this. And then there's a third tranch that's really easy to regulate,
where the where the harm is so obvious and intuitive from the use of
that information that it follows the way night follows day. So for example,
unauthorized disrepute distribution of passwords or account numbers or full SSNs, et cetera,
like that type of stuff that has a you know, yes, there are
no ideas there. Really it's a key of some kind. As an example,
I see, it's it's more functional than anything else. Right, That
kind of stuff is easy, and a commercial publisher of that type of information
is probably not serving a wildly useful purpose. Right, They're probably bad guys.
They're probably bad guys. But there are there are people who maybe who
are you know, who may be giving a who may be selling geo geo
location data or other kinds of data that have a lot of really valuable purposes
that you would want to allow to continue. And that is true both on
the private sector side. It's also true on the government side, where the
availability of this kind of information is a legitimate concern, but it becomes a
proxy war for law enforcement priorities. So, in other words, rather than
saying, okay, you are using certain kinds of information to track down folks
who have overstayed their legal welcome in the country, or if you're using it
to track down pregnant women those you know, therefore we ought to ban all
law enforcement use of that information. Right, Right? Do you really want
that? Right? Right? I mean, do you really not want to
be able to get access to the car tell operatives? Cell phone is that
the end result here, right, right, I mean those are that is
a completely legitimate discussion with the cliffs. These the cliffs they talk about,
right from a legal perspective, you can go off the cliff this way,
you can got the cliff that way. You don't want to go off either
cliff. You want to stay somewhere in the middle. Somewhere in the middle.
But that you know, it's a very charged environment around both of these
things, because there is no doubt there are people, like there are people
doing bad things with data. There's no doubt about that, right, I
mean, there are people selling this to people that they shouldn't sell it to
I mean, our members don't. Our members don't do this that kind of
thing. That's why they I mean they take know your customer type steps,
right, right, that's different. That's kind of part of the responsible action.
Yeah, but we view it as again, if you view it through
a publishing lens, you it makes to me, it makes more sense.
It's in a sense easier to regulate because you're focusing on specific risks and then
you drill down on those risks and you don't have to worry so much about
the First Amendment because you're going to be narrower and more focused by definition.
Yeah, if you think of it as property that's being used willy nilly,
it's almost impossible. You're guaranteed your chance of unintended consequences is it's almost guaranteed.
Yeah. Wow, that's excellent. Well, we've got time for one
more thread. I think the segment's ending here in a second, but I'm
going to tease this and we'll pick it up in the podcast bonus segment.
Okay, this whole concept of alternative data, which now is everywhere and data
brokers are buying it and selling it all over the place. And I've done
some pretty good research on this, and I realized that there are several companies
I've talked to I know that are capturing pretty much A lot of it is
geared around what they call exhaust data from credit card companies, but it's kind
of a misleading term because they will collect how much you spent at this restaurant,
how much you spent at the gas station. Like, all the detailed
transactions are actually being bought and sold to investment bankers, to other institutional investors,
and so what happens is did some of these folks have enough data now
to get a baseline and understand consumer behavior. And they can't get cash,
but they can get credit cards. I don't think they can get debit cards,
but maybe some of them can. And the point being, there are
now organizations that know from the raw transactional data at scale which companies are going
to meet or beat market estimates on Wall Street. Well, if you know
that, then how the heck do you lose when you gamble? Because the
analogy I give is, let's say there's four of us playing cards at my
house, and the rule is I can see all your cards, but none
of you can see my cards. Well, if I lose, I'm an
idiot. I don't know the rules of gambling, apparently because I have such
an advantage now because I know what all of you have and I know this
is happening. Argument. I'll let you think about this as we good to
the break I thought was from a policy perspective, Let's say that if a
company of credit card company is going to sell this exhaust data to one of
these brokers, they must also publish anonymized data to some consumer facing data lake
that then any investor or any interested party could log into and kind of browse
around and see and better understand, because that kind of information is very valuable
if I run some kind of retail operation and I can see at some scale
which products are selling, which products aren't selling, what's happening in this region,
what's happening in that region. That helps me do a better job of
buying things that I know my customers will probably want. Without revealing too much
about where the data came from, but at least it's useful, and what
I'm really angling at is a way of leveling the playing field between these insiders.
And I even came up with a term for what I call it,
outsider trading. So insider trading. Everyone understands that outsider trading is where from
the outside I've been able to gather so much information that I know exactly what's
going to happen next. But we'll pick that up after the break in one
second. Be right back, all right, folks, were back here with
Chris Moore from SIIA. I just threw a curveball question that him there.
I'm curious to know what are your thoughts about all this alternative data stuff and
policies we can define that are reasonable. They will level of playing field.
So I so a couple of things there. I mean, one is I
hear that word in a bunch of different that phrase rather in a bunch of
different contexts, and I think we're talking about so I'm sure we're talking about
the same thing. What you're talking about is kind of information that's available that's
beyond that's outside of sort of the four corners of required reporting, right.
That could be everything from as you say, credit card exhaust credit company exhausts,
credit card exhaust data, to social media trend data. And so when
this data is I think the way the rules are now, this information is
scrubbed so that there's no material non public information in it. So there was
a company called app Annie that got into a bit of hurt with the FTC,
with the SEC because it wasn't taking that stuff out and lo and behold.
Like you can imagine, there are results, right, were pretty good,
But that's not that's not what the business is. What it is is
finding information from these different sources. You know, each of these firms has
their own way of balancing and analyzing it and then providing in as you said,
recommendations, trading recommendations to or data from which trades could be made to
right to uh, you know, to different types of investors. There is
at the moment. I mean, I think I would hesitate to mandate competition
in that space. I would be intuitively, I mean intuitively, I think
you know, the business is that business is I think growing. So in
other words, the initially it started okay, we have we're getting this information
were as you said. I mean, a lot of this comes out of
what you said. Like the first one was like all right, hey we
can do this. Let's do this and sell it to hedge funds, right,
and that is like, you know, who else would be interested in
knowing all this stuff is, for example, a retailer. Nothing wrong with
that either, because all of this information is de identified, so it's really
just like there's no there's it's just okay, there were this many sales and
this is the these are the trend lines, you know, make your bets
right, right. And I think as the you know, as the business
evolves, my expectation is that it's going to be that type of know how.
It's going to become more and more widely available because there's no reason,
there's no reason for it not to be interesting. That's a good point.
H Yeah, Well we're talking about the information life cycle right and where it
is and where you can access it and what you can do with it.
And open data is a big thing these days. I think that's wonderful news.
There's a lot of information coming out. I was actually an advocate way
back in two thousand and five when I worked for the Data Warehousing Institute.
I got at a big soapbox and said, we need transparency in federal spending.
And I did this whole media campaign. I had Bacon's media source at
my access and so I emailed forty thousand reporters all about the need for transparency
and federal spending. Everyone told me I was crazy, And then something happened.
There was actually a guy at the Heritage Foundation, Mark Tapscott, who
picked up on it, and he was like, why did you send me
this? And I was like, well, I went off my whole tangent.
He goes, I've been focused on this for twenty years, Like where
have you been all my life? And he then he used my article and
really he was the imprimature of the Data Warehousing Institute that he used to go
testify before Congress and say we need these citizen auditors to help us find waste
and lo and behold, the House passed the bill. The Senate passed the
bill, co sponsored by a guy named Barack Obama who was a Senator from
Illinois. And on September twenty sixth, two thousand and six, then President
George W. Bush signed the Federal Funding Accountability and Transparency Law. And I
almost had a heart attack on My friend called me and told me, I
was like, are you kidding me? No, I'm not going to say
I got any credit for or anything, which is fine, you know,
I don't mind, but I was just absolutely shocked. And now when you
look at it, you think my theory back then was that, look,
everybody knows something. So even though some accountant may not know that this line
on looks funky, there's someone who does. There's a carpenter, someone who
knows this kind of nail should only cost ten cents, it should not cost
a dollar and ten cents, or whatever the case may be. And these
days we have the capacity, like the Amazon Mechanical Turk for example, to
leverage crowdsourcing at scale, and so if people register with the system, you
know, I'm a citizen. You know, everything I do in the system,
when I flag something, if I'm right or wrong, you can see
how you could dynamically score these things and then really wind up with a whole
cadre of experts greening or information systems for the government to help everyone know where
the money goes. Because a lot of times people worry about fraud. That
is true. Bad things do happen, are just mistakes, They're just things
that people overlook too. So being able to find all that stuff, I
think it's very very interesting. But final thoughts from you, Chris Moore of
s I I a here in the show. Uh, you know, like
I said, I'm mostly optimistic about these technologies. Yeah, you know,
do they need guardrails? Yeah, they do. They really do. Because
if if what you're trying to do is mimic human intelligence, you know,
human intelligence, we put guardrails around that as well. There's things for not
allowed to do and we have good reasons for doing that. Right, pretty
funny you go to put it. But the so you know, there's going
to be a need for that as things develop, and what the type of
thing that you are discussing is applied to the government is tremendously useful. I
think there are a number of private businesses and even members who are selling all
kinds of solutions that do the same thing. Right, they go into your
they go into your into your spreadhe and they'll say, oh that's weird.
Yeah, you know, for a year over here and employee changes one year
to the other. Maybe they don't go back five years. Maybe they're about
one year, right, you know, and chack to see how things have
changed, and suddenly that looks hot and it gets flat and that's how thanks
to tech fraud too. Yeah right, well, hey, this has been
this has been absolutely fantastic. Chris more m HR. Look this organization up
online. SIIA doing wonderful work. You've made me more optimistic too about the
future. So it's very good to know. And they're doing great work in
policy. Look them up online. Folks, you've been listening to Inside Analysis,
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Radio. I'm Chris Gragio. President Biden is leveling some of his harshest criticism
yet the Supreme Court. At a fundraiser in Los Angeles yesterday. The President
called the conservative majority court out of step with the rest of the country.
Biden also warned that a former President Trump is elected, he would appoint more
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