September 22, 2020 • 29 mins
Human trafficking to create slave labor has persisted globally for centuries, devastating the lives of millions, and frustrating those who work tirelessly to end it. On today's episode, John McGrath, Sr. Solution Architect at IBM, and Neil Giles, CEO of the Traffik Analysis Hub, discuss how the IBM Cloud-based Hub, powered by IBM Watson helps organizations securely share and analyze data to help curb modern human slavery.
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Episode Transcript
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Speaker 1 (00:00):
Before that, he worked in law enforcement and intelligence agencies,
including Scotland Yard and the National Criminal Intelligence Service. John
McGrath is a global solution Architect with IBM and works
with IBM r S to find ways in which the
company can turn its expertise and technology towards solving real
world problems. And when it comes to real world problems,
(00:21):
human trafficking is a major one. When you consider the
impact of the issue not just on those who are
the direct victims, but also their families and communities, as
well as the various companies that are profiting off the
proliferation of human trafficking, it can quickly become overwhelming. That's
why I was excited to speak with Neil and John,
(00:42):
as they helped me get a better understanding of the
issue and how technology is playing an intrinsic and at
times non intuitive part in combating human trafficking. First, let
me thank both of you for being on the show,
and before we get into the topic at hand, I
thought it would be nice to get to know the
two of you and to learn more about your background
(01:04):
and what brought you to your current positions. So John,
could we start with you. Could you tell us a
little bit about yourself and what it is you do
and how you got there. Sure. So, my name is
John McGrath. I'm a senior solution architect for IBM in
Ireland based out of the Dublin Lab in in Ireland.
(01:24):
My background, Jonathan, is fourteen years working in lab services
for IBM, which involves the dealing with clients on a
daily basis. But about two and a half three years
ago I got involved in the Traffic Analysis Hub initiative
and from that I managed to form a team called
a Tech for Good Team in Dublin and uh and
(01:46):
that's what I do on a daily basis. Now I
work with the Tech for Good Team excellent and Neil,
can you tell us a bit about yourself and your position? Surely,
Jonathan M. So, my name is Neil Jonles. I'm currently
CEO of a new reform not for profit called Traffic
Analysis Hub. My journey here is a torturous one. I
(02:09):
spent thirty six years in law enforcement in the United Kingdom,
concluding that time as Deputy Director of our National Agency.
I'm an organized crime intelligence expert UM and while I
was serving with our National Agency, I came across a
small not for profit called Stop the Traffic, who were
(02:32):
specializing in preventing human trafficking. They began their work with
the cocoa industry in West Africa that was using thousands
of child slaves to pick cocoa for our chocolate UM
and I was disappointed to learn that they knew more
about trafficking than the intelligence systems in my national agency.
(02:53):
UM so began formula relationship with them too, to grow
that understanding in the agency and and to begin to
build unusual partnerships with NGOs and other subject matter experts.
And when I left law enforcement nine years ago, I
began working with Stop the Traffic more routinely, realizing that
(03:14):
we needed a richer picture of trafficking if we were
going to be effective as societies to begin to make
a history. We haven't done that yet, but we've begun
to create that richer picture through the work that we've
been doing. And Neil, I think you've hit on something
that I really wanted to focus on in the early
(03:35):
part of our conversation, the fact that even in your
role in intelligence, that there was a lack of real
knowledge about human trafficking. I think that certainly can apply
to the general population. I know that for myself, it's
something that I am aware happens, and typically I don't
(03:57):
really even think about it until I'm going through an
airport and I see a poster that's bringing it to
your attention directly, and otherwise I'm kind of in the dark.
Can you give us sort of a an outline of
how big a problem this is? Give us the scope
and the impact of human trafficking. Human trafficking is pretty
(04:21):
well defined as a global phenomenon now. The the academic estimates,
which are reasonable, suggests that something like fourteen million people
globally are in circumstances that we would be comfortable to
describe as trafficking and exploitation. Um, that's an enormous number
(04:42):
of people. Even in in the UK, the best estimates
suggest that something like d thirty five thousand people are
in circumstances of exploitation having been trafficked. So you could
fill the biggest sports stadium that we've got twice with
those people. And I think the best way of describing
(05:03):
it to people is that it's it's an errant economy
in its own right. Traffic, trafficking and exploitation splits into
two chunks thirty five percent estimate of those people and
exploitation tend to be in some aspect of commercial sexual
exploitation are in labor markets, particularly those labor markets that
(05:29):
rely on seasonal workers contract workers, so agriculture, food processing,
and manufacturing, construction, big fishing, sea fleets, logistics are very
popular destinations for traffic labor where very criminal recruitment gangs
(05:52):
infiltrate them into the workforce. Most people's journeys into exploitation
beginners journeys of hope. They're tricked into taking a journey
on the basis that there's a great new future for
them and their family, and then when they get to
that destination, it turns the dust and becomes a creeping
debt bondage situation. And it's worth something like three quarters
(06:15):
of a trillion dollars a year. We estimate. There's a
new official estimate out this year or sorry, early next
year that will define it slightly differently, probably m but
but that's our best guests. I hope that that that
gives you a sense of of of how the thing operates.
(06:36):
It needs to recruit something like of its workforce newly
every year, so somewhere somewhere up to eight million people
a year as a recruitment requirement. It's about money, and
most of that money goes through financial institutions, And it's
about creating a market, creating demand and maintaining demand. And
it can't be solved just by the justice process, and
(06:59):
it can't be solved just by humanitarian activity rescuing and
rehabilitating Neil. That also brings me to a follow up question. Traditionally,
what measures do various agencies and governments take in an
effort to prevent human trafficking? You had mentioned that this
is beyond the scope of any one organization, but what
(07:22):
are the sort of efforts that have been put forward? Uh,
so far, we need traffickers to have a real sense
of risk if they do this, that that they are
likely to be discovered and held to account. And therefore
there there is a significant role for investigators for the
(07:45):
justice process. But but more broadly, we need to think
about the problem in an economic sense, um and and
that's the aspect that I think has taken too long
to develop. You know, in lots of parts of the world,
the justice process doesn't work well, and of course trafficking
(08:05):
is a global issue. In the more developed societies, the
justice process does hold people to account, perhaps not in
the numbers that we might like, but but it's a
sanction that people fear um and and therefore it's a
very worthy element of of the program. Um And and
(08:28):
encouraging other parts of the world where that doesn't work
so well to get better at it is really important.
But we have over relied on in my view, on
on that outcome as the resolution to the problem. And
of course, while there's money to make in good quantity
and not enough fear of sanction, then traffickers will still
(08:54):
flourish and demand will still maintain or grow. Right, so
without us having any you know, without addressing those root causes,
what we're looking at really is dealing with the consequences,
and that's just going to be a consistent issue without
addressing those root causes. Obviously, this is an enormous issue
(09:18):
that is going to require a lot of work across
the globe in order to really tamp down on it. John,
I'm curious about how you come into the picture. We're
about to start talking about using technology in a way
to detect and then take measures to prevent things like
(09:39):
human trafficking, how did you get involved with this particular challenge. Okay,
So I think I mentioned earlier Jonathan that I was
working as a services person. So I was based in
the Middle East working with some government agencies on behalf
of IBM Security, and in my role, I had a
give back opportunity and I was invited by IBM Corporate
(10:01):
Social Responsibility to come to London to help facilitate a
workshop for Stop the Traffic and that was the first
exposure I had really to the issue of human trafficking
beyond what the casual lay person knew about it. But
the thing that was interesting for me when I walked
into the room to host the workshop was the attendees
weren't just the people I expected. So I expected to
(10:24):
see non government organizations and not for profits there, and
I expected to see law enforcement agencies and some government agencies.
What I didn't expect to see where financial institutions, and
there were a lot of financial institutions present. And it
was really during that workshop that I kind of got
the realization that this was across sectoral issue and the
(10:45):
solution had to come from multisectoral collaboration. So that was
really the starting point for me and from that I
worked with Neil and to stop the traffic team to
learn more about the issue, and I spent many evenings
and weekends in the hotel in the Middle Least building
prototypes and sampling what could be done using various technologies.
(11:06):
All are all based on this principle of how do
we get to data sharing collaboration around this issue. Can
you talk a little bit more about those technologies, what
form did they take? What was it that you were thinking, like,
what metrics are you looking at and how are you
analyzing them? Sure? The the starting point in the first
(11:27):
workshop was there was kind of a division in the
room depending on the agencies and the at the sort
of core mission of each organization, but there was a
basic two requirements primary requirements that came out. The first
was for this ability to do a global level analysis
of the problem to see where the areas of intensity were,
(11:48):
first particular types of trafficking, to be able to see
how this is influenced by not just geography but by time.
And then also there there was a requirement to be
able to see the roots we're being used by the
traffickers to move their their victims from point day to
point b so, so that was kind of the one
half of the room. We're looking for this macro level
(12:09):
view that would give them the global picture and and
if you like, validate some of the high level figures,
the estimated figures that Neil was talking about earlier. And
then the other half of the room were more interested in, Okay,
now that I know where the issue is, how do
I pull that into a secure environment where I could
start to investigate it and start to understand the network
in more detail. Who are the people involved, how are
(12:31):
they moving people? What tools are they using? You know,
what addresses, account numbers, all that kind of stuff. So
we had this kind of a double requirement, so we
started to look at what kind of technologies we used
and used in the past which could help to satisfy
both of these requirements. While you were developing this in
the early days, what were some of the lessons you learned,
(12:53):
What were things that you know, were there pathways that
you were taking early on that turned out to be
less rutful than you hoped, or things that you discovered
that surprised you while you were developing this early approach.
Sure the well, one of the first things that hit
us wasn't necessarily a surprise, Jonathan, but uh, the extent
(13:15):
of how it impacted us kind of surprised. This was
the whole data privacy issue and the challenges around sharing
data across jurisdictions. So so this became a reasonably high
priority in our requirements, if you like. When we were
trying to design the system. A lot of the basis
of what we were trying to do is captured data
from all over the world and make it available to
(13:38):
partners from all over the world. But we had to
be very careful that we took out any sensitive information,
any unique identifiers, and then we had to run the
proposals true you know, various legal people to give us
advice on whether or not we were following the right path.
So not not so much a technical issue, although there
are technologies that can help with this. It was more about,
(13:59):
you know, requirements issue. And then we started to look
at things like, um, the largest amount of the data
is contained in the narratives that the victims are, the
narratives about the stories the victims, and to do that
we we turned to natural language understanding and machine learning,
and then we hit the challenges that everybody hits in
(14:20):
this domain of making sure it's accurate, make sure it's unbiased,
but also dealing with multilingual issues, so a lot of
the data is not necessarily in the primary languages. So
that that was another one of the big challenges that
we had to think about. Yes, this is an enormous challenge,
justin in machine learning in general, is the natural language
(14:42):
processing and being able to parse what someone means when
they say something in particular way. And I imagine when
you are trying to handle or analyze an enormous amount
of data, that problem becomes magnified enormously. What was it
the a particular set of efforts that then led into
(15:04):
the Traffic Analysis hub or did that come about in
a different way. Yeah, the Traffic Analysis Hub came out
of a kind of vision that stopped the traffic it
had for a while. It became part of that workshop
on the back in London. It was that macro level
view that everybody could share and everybody got value from,
(15:24):
and that became the primary target for the initial prototypes.
So when we were looking at that, we were trying
to get a geospatial view, you know, a map based
analysis of data. We were trying to figure out how
to capture data, and then we realized that every different
source that we accessed kind of classified their data uniquely,
and now it's very difficult to do comparative analysis across
(15:44):
these things. So then we hit the challenge of how
do we make it consistent so that it makes sense
to everybody. And then we we hit challenges like things
like locations. So there's lots of in the narratives of stories,
there's lots of references to location. We needed to understand
not just where location was referenced, but the context in
(16:05):
which has been referenced. And then when we knew that,
we had to go find a coordinates for it to
put it on the map. But we had to be
careful that we were getting to correct coordinates for the
correct location because there's lots of For instance, I think
there's seventeen different Londons around the world, so we have
to be clear about which London was actually been referenced
in text. So so that was really kind of the
progression of the prototypes. Yeah, I think that for for
(16:28):
a lot of people, myself included, we can sometimes fall
into a trap where we're thinking about these very sophisticated
systems pulling data as if it's magically all in a centralized,
uniform database. I think the magical thing for a lot
of folks who look into this is that we see
(16:48):
how these systems are able to spot patterns, uh and
trends in data sets that are so enormous that to
us there's no signal, it's just noise. So seeing something
that can pick out the signal does seem a little magical. Well,
as the t A hub is evolving and taking shape,
have we already seen some impact in the real world?
(17:12):
Is it being used right now to help identify and
prevent trafficking? Today? It's being used by over We have
over a hundred organizations who are members of the Hub
at this point, and all of them have their own
secret missions or their own, uh, their own core missions
of what they're they're trying to achieve with us, But
(17:33):
we have anecdotal stories from various parties of where they've
got value from the data that's in the Hub. And
sometimes the value, interestingly, is not just in the data,
it's in the collaboration with their peer organizations and the
other partners in the hub, which was part of what
we tried to set out to do in the first place,
was achieved as kind of safe collaborative environment where people
(17:56):
could share their expertise as well as their knowledge for
the purpose of disrupting human trafficking. But we have got
a lot of feedback from various partners where they've been
able to validate data that they had seen in their
internal systems when they were starting to investigate issues. They're
able to validate some of that in the Hub by
looking at the data that we've been collecting. And then conversely,
(18:20):
we've also had the same feedback from organizations who are
investigators or say, we're able to identify new areas of
investigation in the Hub that we weren't aware of because
we've never looked there before, but once we started to look,
we started to see patterns in our own data sets
in those locations. There are facilities for different audiences in
the Hub, So you've got people like researchers and academia
(18:43):
who come in and the facility we have in which
in the hub, which allows them to navigate by concept
through large news data sets, and that's a facility that
they give us feedback on a lot that tells us
it helps them to find information and to support their
their research. We had one person who um Every month
(19:06):
we have an analyst call in the community where the
community and the Hub come together. They look at the
functionalities that we're building and the data sets that were gathering,
and they give us direction of what they need and
we feature a participant on that every month. So we
have had a person who actually actually presented their thesis
and part of their thesis was based on data that
(19:26):
they pulled in from the Hub to to validate their
own their ow own insights into human trafficking. That's phenomenal.
So not just building a system that's doing this very
technical work, but also just building these relationships, forming relationships
across various sectors and various countries that can all be
(19:48):
you know, directed toward helping stop this problem. What other
ways do you see the Traffic Analysis Hub impacting various industries?
So we've well, first off, we've built a platform underpinning
the Traffic Analysis Hub which allows allows us to reuse
the capabilities across different um issues. So we've also used
(20:12):
it for things like food redistribution to avoid food waste,
and we've also used it in the area of migration
and population displacement and trying to create prediction models and stuff.
So the thing that kind of excites me about this
is we're starting to bring in new sectors, but also
(20:33):
not just industry sectors, but sectors within the n g
O world who are focused on different parts of of
of social issues and we're bringing together into one platform
and one community and start to share information. So we've
been approached by organizations who are who are focused on
animal trafficking to see see if they can get access
(20:53):
to the hub and start to share their data in
there as well. And we're all starting to see the
reusability of some of the things that we've built. For instance,
we've built a causality model in partnership with IBM Research,
and where we were looking at the cause that the
attributes that are most prevalent in causing things like population displacements,
(21:15):
and these models are things that we can then reapply
from one use case to another. So we're trying trying
now to move that model into human trafficking to see
if we can determine, for instance, the the likely outcome
analysis for interventions in certain locations. To me, that's also
inspiring because in that process you could be working on
(21:38):
issues that are tangentially tied into trafficking, you know, some
of those underlying root causes we were talking about, and
being able to solve some of these social issues can
also help remove some of those causes or at least
diminish them somewhat, and thus have the sort of positive
feedback loop of being able to solve these these traditionally
(22:01):
incredibly difficult problems, largely because it is hard for us
to even get a grasp on all the data that
plays into this. I sometimes liken this too, you know,
making making the challenge of making a long hot term
forecast for the weather. There's just so many variables that
are out there, and they interact with each other in
(22:22):
ways that we don't fully understand. It can be difficult
to make anything, you know, uh, like a forecast that's
ten days out. On a similar front, we see this
real world you know, unfolding of of trying to tackle
these enormous social problems that also have all these different variables,
(22:42):
many of which are at their heart human issues, and
humans are largely unpredictable creatures. So it's fascinating to see
these systems that are starting to glean insights into the
way these these large systems of people and and the
way we work, how how they actually perform out in
the real world, being able to draw conclusions about that,
(23:04):
predictions and perhaps solutions. Um, what would you say are
some of the lessons you have learned in this, both
just as seeing how the t A Hub and the
related technologies have given insight into the human trafficking problem,
and also lessons you've learned as as leaders in that space.
(23:26):
Sure well, certainly from from my side, one of the
big lessons I've learned is how super motivated the IBM
staff are to get involved in initiatives like this. It's
been I was talking to somebody earlier today and I
was saying, I could spend fifty of my time talking
to volunteers within IBM who want to help, and they're
all bringing individual skills and capabilities and experience here and
(23:50):
offering to help us out with various pieces of the puzzle.
So there's a huge potential here to apply technology to
some of these challenges. The other thing that's very interesting
at the moment is a lot of these core major
social issues, whether it's the pandemic, whether it's climate change,
whether it's population displacement, whether it's trafficking. They're all intertwined
(24:13):
and one is influencing the other. And the attributes that
influence influence the prevalence of this of these events and
different parts of the world, they're very often common attributes.
So we're trying to figure out can we build models
that will help us to identify, you know, what are
the attributes that are that are interesting and trying to
lead a team through this, you know, keep them focused
(24:37):
on stuff that we have to deliver, but also giving
him the freedom and the ability to go and explore
these new opportunities and new ideas. That's as a core
learning for me. Yeah. From my side, Jonathan, I think
the first thing I discovered was that whilst we are
absolutely data rich, we are terribly knowledge poor um and
(25:02):
and the work that we've been doing together with IBM
and the Tech for Good team, I think has begun
to change that picture um and and then So the
next key element in that chain of activity needs to
be to ensure the widest possible appropriate audience can access
that knowledge. Because no one's got enough resources to do
(25:26):
everything at once. It's it's it's the classic problem. You
can only focus on so many things, so you need
to use that knowledge like I would have used intelligence
in an investigative way in law enforcement, to focus the
resources that you've got at the hot spots and points
where you can make a difference. And that that's how
(25:48):
we get this thing on the run. And we need
to we need to start undermining the economic pillars that
currently comfortably support trafficking in persons and exploitation. And I
think that we've mind a decent stuff, And I like
Neil how you brought that around to this challenge of
(26:11):
being data rich and knowledge poor. To me, that was
we're seeing that that that pivot now where the early
days of big data seem to be an emphasis on
look at how much data we have access to, and
now we are kind of moving into a new era.
We're well into a new era really where it's how
(26:32):
do we actually leverage this enormous fire hose of information.
It's coming in from all directions, generated by more devices
than ever before in the history of humanity. And we're
actually starting to see systems like the the t A Hub,
systems that are able to take that information and do
(26:53):
something that's truly useful and impactful. How do you see
the approach to trafficking changing over the course of the future.
What do you see as the evolution of addressing human trafficking.
I think the big gains are in commerce and industry.
(27:14):
I think that the ability forum for corporates to begin
to understand where they need to focus their activities and
what questions they need to ask of their suppliers, particularly
and in difficult parts of the world UM And similarly
(27:37):
for financial institutions, again it helps them because because every
errant business has a banker and a banking facility UM,
and the clues are there. If the if the customer
management process knew what those clues were and knew what
questions to ask and and our view is that the
(28:00):
more the more we grow access to the data that
we've we have two businesses and financial institutions, the greater
influence they'll have on opportunity or and reduce opportunity for
trafficking to flourish. Before I sign off in this episode,
I just want to reiterate some other things we covered
(28:24):
in this and that is these are non trivial problems.
Both the real world problem of human trafficking, which is
clearly non trivial, it is critical, and the actual computer
problems that the teams are trying to solve in order
to really take full advantage of artificial intelligence machine learning
(28:45):
and apply that to this incredibly difficult issue. Everything from
natural language processing too, pulling in information from various sources
and contextualizing it in a way that's useful. These are
hard problems to solve, but as we've seen, it is
worth it in the effort to stop human trafficking. I
(29:06):
want to thank John and Neil again for joining the episode.
It was an honor to talk with them about such
an important issue. I hope that you learned something in
this episode, and I look forward to sharing more Smart
Talks episodes with you in the near future. Take care, ye.
(29:27):
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Before that, he worked in law enforcement and intelligence agencies,
including Scotland Yard and the National Criminal Intelligence Service. John
McGrath is a global solution Architect with IBM and works
with IBM r S to find ways in which the
company can turn its expertise and technology towards solving real
world problems. And when it comes to real world problems,
(00:21):
human trafficking is a major one. When you consider the
impact of the issue not just on those who are
the direct victims, but also their families and communities, as
well as the various companies that are profiting off the
proliferation of human trafficking, it can quickly become overwhelming. That's
why I was excited to speak with Neil and John,
(00:42):
as they helped me get a better understanding of the
issue and how technology is playing an intrinsic and at
times non intuitive part in combating human trafficking. First, let
me thank both of you for being on the show,
and before we get into the topic at hand, I
thought it would be nice to get to know the
two of you and to learn more about your background
(01:04):
and what brought you to your current positions. So John,
could we start with you. Could you tell us a
little bit about yourself and what it is you do
and how you got there. Sure. So, my name is
John McGrath. I'm a senior solution architect for IBM in
Ireland based out of the Dublin Lab in in Ireland.
(01:24):
My background, Jonathan, is fourteen years working in lab services
for IBM, which involves the dealing with clients on a
daily basis. But about two and a half three years
ago I got involved in the Traffic Analysis Hub initiative
and from that I managed to form a team called
a Tech for Good Team in Dublin and uh and
(01:46):
that's what I do on a daily basis. Now I
work with the Tech for Good Team excellent and Neil,
can you tell us a bit about yourself and your position? Surely,
Jonathan M. So, my name is Neil Jonles. I'm currently
CEO of a new reform not for profit called Traffic
Analysis Hub. My journey here is a torturous one. I
(02:09):
spent thirty six years in law enforcement in the United Kingdom,
concluding that time as Deputy Director of our National Agency.
I'm an organized crime intelligence expert UM and while I
was serving with our National Agency, I came across a
small not for profit called Stop the Traffic, who were
(02:32):
specializing in preventing human trafficking. They began their work with
the cocoa industry in West Africa that was using thousands
of child slaves to pick cocoa for our chocolate UM
and I was disappointed to learn that they knew more
about trafficking than the intelligence systems in my national agency.
(02:53):
UM so began formula relationship with them too, to grow
that understanding in the agency and and to begin to
build unusual partnerships with NGOs and other subject matter experts.
And when I left law enforcement nine years ago, I
began working with Stop the Traffic more routinely, realizing that
(03:14):
we needed a richer picture of trafficking if we were
going to be effective as societies to begin to make
a history. We haven't done that yet, but we've begun
to create that richer picture through the work that we've
been doing. And Neil, I think you've hit on something
that I really wanted to focus on in the early
(03:35):
part of our conversation, the fact that even in your
role in intelligence, that there was a lack of real
knowledge about human trafficking. I think that certainly can apply
to the general population. I know that for myself, it's
something that I am aware happens, and typically I don't
(03:57):
really even think about it until I'm going through an
airport and I see a poster that's bringing it to
your attention directly, and otherwise I'm kind of in the dark.
Can you give us sort of a an outline of
how big a problem this is? Give us the scope
and the impact of human trafficking. Human trafficking is pretty
(04:21):
well defined as a global phenomenon now. The the academic estimates,
which are reasonable, suggests that something like fourteen million people
globally are in circumstances that we would be comfortable to
describe as trafficking and exploitation. Um, that's an enormous number
(04:42):
of people. Even in in the UK, the best estimates
suggest that something like d thirty five thousand people are
in circumstances of exploitation having been trafficked. So you could
fill the biggest sports stadium that we've got twice with
those people. And I think the best way of describing
(05:03):
it to people is that it's it's an errant economy
in its own right. Traffic, trafficking and exploitation splits into
two chunks thirty five percent estimate of those people and
exploitation tend to be in some aspect of commercial sexual
exploitation are in labor markets, particularly those labor markets that
(05:29):
rely on seasonal workers contract workers, so agriculture, food processing,
and manufacturing, construction, big fishing, sea fleets, logistics are very
popular destinations for traffic labor where very criminal recruitment gangs
(05:52):
infiltrate them into the workforce. Most people's journeys into exploitation
beginners journeys of hope. They're tricked into taking a journey
on the basis that there's a great new future for
them and their family, and then when they get to
that destination, it turns the dust and becomes a creeping
debt bondage situation. And it's worth something like three quarters
(06:15):
of a trillion dollars a year. We estimate. There's a
new official estimate out this year or sorry, early next
year that will define it slightly differently, probably m but
but that's our best guests. I hope that that that
gives you a sense of of of how the thing operates.
(06:36):
It needs to recruit something like of its workforce newly
every year, so somewhere somewhere up to eight million people
a year as a recruitment requirement. It's about money, and
most of that money goes through financial institutions, And it's
about creating a market, creating demand and maintaining demand. And
it can't be solved just by the justice process, and
(06:59):
it can't be solved just by humanitarian activity rescuing and
rehabilitating Neil. That also brings me to a follow up question. Traditionally,
what measures do various agencies and governments take in an
effort to prevent human trafficking? You had mentioned that this
is beyond the scope of any one organization, but what
(07:22):
are the sort of efforts that have been put forward? Uh,
so far, we need traffickers to have a real sense
of risk if they do this, that that they are
likely to be discovered and held to account. And therefore
there there is a significant role for investigators for the
(07:45):
justice process. But but more broadly, we need to think
about the problem in an economic sense, um and and
that's the aspect that I think has taken too long
to develop. You know, in lots of parts of the world,
the justice process doesn't work well, and of course trafficking
(08:05):
is a global issue. In the more developed societies, the
justice process does hold people to account, perhaps not in
the numbers that we might like, but but it's a
sanction that people fear um and and therefore it's a
very worthy element of of the program. Um And and
(08:28):
encouraging other parts of the world where that doesn't work
so well to get better at it is really important.
But we have over relied on in my view, on
on that outcome as the resolution to the problem. And
of course, while there's money to make in good quantity
and not enough fear of sanction, then traffickers will still
(08:54):
flourish and demand will still maintain or grow. Right, so
without us having any you know, without addressing those root causes,
what we're looking at really is dealing with the consequences,
and that's just going to be a consistent issue without
addressing those root causes. Obviously, this is an enormous issue
(09:18):
that is going to require a lot of work across
the globe in order to really tamp down on it. John,
I'm curious about how you come into the picture. We're
about to start talking about using technology in a way
to detect and then take measures to prevent things like
(09:39):
human trafficking, how did you get involved with this particular challenge. Okay,
So I think I mentioned earlier Jonathan that I was
working as a services person. So I was based in
the Middle East working with some government agencies on behalf
of IBM Security, and in my role, I had a
give back opportunity and I was invited by IBM Corporate
(10:01):
Social Responsibility to come to London to help facilitate a
workshop for Stop the Traffic and that was the first
exposure I had really to the issue of human trafficking
beyond what the casual lay person knew about it. But
the thing that was interesting for me when I walked
into the room to host the workshop was the attendees
weren't just the people I expected. So I expected to
(10:24):
see non government organizations and not for profits there, and
I expected to see law enforcement agencies and some government agencies.
What I didn't expect to see where financial institutions, and
there were a lot of financial institutions present. And it
was really during that workshop that I kind of got
the realization that this was across sectoral issue and the
(10:45):
solution had to come from multisectoral collaboration. So that was
really the starting point for me and from that I
worked with Neil and to stop the traffic team to
learn more about the issue, and I spent many evenings
and weekends in the hotel in the Middle Least building
prototypes and sampling what could be done using various technologies.
(11:06):
All are all based on this principle of how do
we get to data sharing collaboration around this issue. Can
you talk a little bit more about those technologies, what
form did they take? What was it that you were thinking, like,
what metrics are you looking at and how are you
analyzing them? Sure? The the starting point in the first
(11:27):
workshop was there was kind of a division in the
room depending on the agencies and the at the sort
of core mission of each organization, but there was a
basic two requirements primary requirements that came out. The first
was for this ability to do a global level analysis
of the problem to see where the areas of intensity were,
(11:48):
first particular types of trafficking, to be able to see
how this is influenced by not just geography but by time.
And then also there there was a requirement to be
able to see the roots we're being used by the
traffickers to move their their victims from point day to
point b so, so that was kind of the one
half of the room. We're looking for this macro level
(12:09):
view that would give them the global picture and and
if you like, validate some of the high level figures,
the estimated figures that Neil was talking about earlier. And
then the other half of the room were more interested in, Okay,
now that I know where the issue is, how do
I pull that into a secure environment where I could
start to investigate it and start to understand the network
in more detail. Who are the people involved, how are
(12:31):
they moving people? What tools are they using? You know,
what addresses, account numbers, all that kind of stuff. So
we had this kind of a double requirement, so we
started to look at what kind of technologies we used
and used in the past which could help to satisfy
both of these requirements. While you were developing this in
the early days, what were some of the lessons you learned,
(12:53):
What were things that you know, were there pathways that
you were taking early on that turned out to be
less rutful than you hoped, or things that you discovered
that surprised you while you were developing this early approach.
Sure the well, one of the first things that hit
us wasn't necessarily a surprise, Jonathan, but uh, the extent
(13:15):
of how it impacted us kind of surprised. This was
the whole data privacy issue and the challenges around sharing
data across jurisdictions. So so this became a reasonably high
priority in our requirements, if you like. When we were
trying to design the system. A lot of the basis
of what we were trying to do is captured data
from all over the world and make it available to
(13:38):
partners from all over the world. But we had to
be very careful that we took out any sensitive information,
any unique identifiers, and then we had to run the
proposals true you know, various legal people to give us
advice on whether or not we were following the right path.
So not not so much a technical issue, although there
are technologies that can help with this. It was more about,
(13:59):
you know, requirements issue. And then we started to look
at things like, um, the largest amount of the data
is contained in the narratives that the victims are, the
narratives about the stories the victims, and to do that
we we turned to natural language understanding and machine learning,
and then we hit the challenges that everybody hits in
(14:20):
this domain of making sure it's accurate, make sure it's unbiased,
but also dealing with multilingual issues, so a lot of
the data is not necessarily in the primary languages. So
that that was another one of the big challenges that
we had to think about. Yes, this is an enormous challenge,
justin in machine learning in general, is the natural language
(14:42):
processing and being able to parse what someone means when
they say something in particular way. And I imagine when
you are trying to handle or analyze an enormous amount
of data, that problem becomes magnified enormously. What was it
the a particular set of efforts that then led into
(15:04):
the Traffic Analysis hub or did that come about in
a different way. Yeah, the Traffic Analysis Hub came out
of a kind of vision that stopped the traffic it
had for a while. It became part of that workshop
on the back in London. It was that macro level
view that everybody could share and everybody got value from,
(15:24):
and that became the primary target for the initial prototypes.
So when we were looking at that, we were trying
to get a geospatial view, you know, a map based
analysis of data. We were trying to figure out how
to capture data, and then we realized that every different
source that we accessed kind of classified their data uniquely,
and now it's very difficult to do comparative analysis across
(15:44):
these things. So then we hit the challenge of how
do we make it consistent so that it makes sense
to everybody. And then we we hit challenges like things
like locations. So there's lots of in the narratives of stories,
there's lots of references to location. We needed to understand
not just where location was referenced, but the context in
(16:05):
which has been referenced. And then when we knew that,
we had to go find a coordinates for it to
put it on the map. But we had to be
careful that we were getting to correct coordinates for the
correct location because there's lots of For instance, I think
there's seventeen different Londons around the world, so we have
to be clear about which London was actually been referenced
in text. So so that was really kind of the
progression of the prototypes. Yeah, I think that for for
(16:28):
a lot of people, myself included, we can sometimes fall
into a trap where we're thinking about these very sophisticated
systems pulling data as if it's magically all in a centralized,
uniform database. I think the magical thing for a lot
of folks who look into this is that we see
(16:48):
how these systems are able to spot patterns, uh and
trends in data sets that are so enormous that to
us there's no signal, it's just noise. So seeing something
that can pick out the signal does seem a little magical. Well,
as the t A hub is evolving and taking shape,
have we already seen some impact in the real world?
(17:12):
Is it being used right now to help identify and
prevent trafficking? Today? It's being used by over We have
over a hundred organizations who are members of the Hub
at this point, and all of them have their own
secret missions or their own, uh, their own core missions
of what they're they're trying to achieve with us, But
(17:33):
we have anecdotal stories from various parties of where they've
got value from the data that's in the Hub. And
sometimes the value, interestingly, is not just in the data,
it's in the collaboration with their peer organizations and the
other partners in the hub, which was part of what
we tried to set out to do in the first place,
was achieved as kind of safe collaborative environment where people
(17:56):
could share their expertise as well as their knowledge for
the purpose of disrupting human trafficking. But we have got
a lot of feedback from various partners where they've been
able to validate data that they had seen in their
internal systems when they were starting to investigate issues. They're
able to validate some of that in the Hub by
looking at the data that we've been collecting. And then conversely,
(18:20):
we've also had the same feedback from organizations who are
investigators or say, we're able to identify new areas of
investigation in the Hub that we weren't aware of because
we've never looked there before, but once we started to look,
we started to see patterns in our own data sets
in those locations. There are facilities for different audiences in
the Hub, So you've got people like researchers and academia
(18:43):
who come in and the facility we have in which
in the hub, which allows them to navigate by concept
through large news data sets, and that's a facility that
they give us feedback on a lot that tells us
it helps them to find information and to support their
their research. We had one person who um Every month
(19:06):
we have an analyst call in the community where the
community and the Hub come together. They look at the
functionalities that we're building and the data sets that were gathering,
and they give us direction of what they need and
we feature a participant on that every month. So we
have had a person who actually actually presented their thesis
and part of their thesis was based on data that
(19:26):
they pulled in from the Hub to to validate their
own their ow own insights into human trafficking. That's phenomenal.
So not just building a system that's doing this very
technical work, but also just building these relationships, forming relationships
across various sectors and various countries that can all be
(19:48):
you know, directed toward helping stop this problem. What other
ways do you see the Traffic Analysis Hub impacting various industries?
So we've well, first off, we've built a platform underpinning
the Traffic Analysis Hub which allows allows us to reuse
the capabilities across different um issues. So we've also used
(20:12):
it for things like food redistribution to avoid food waste,
and we've also used it in the area of migration
and population displacement and trying to create prediction models and stuff.
So the thing that kind of excites me about this
is we're starting to bring in new sectors, but also
(20:33):
not just industry sectors, but sectors within the n g
O world who are focused on different parts of of
of social issues and we're bringing together into one platform
and one community and start to share information. So we've
been approached by organizations who are who are focused on
animal trafficking to see see if they can get access
(20:53):
to the hub and start to share their data in
there as well. And we're all starting to see the
reusability of some of the things that we've built. For instance,
we've built a causality model in partnership with IBM Research,
and where we were looking at the cause that the
attributes that are most prevalent in causing things like population displacements,
(21:15):
and these models are things that we can then reapply
from one use case to another. So we're trying trying
now to move that model into human trafficking to see
if we can determine, for instance, the the likely outcome
analysis for interventions in certain locations. To me, that's also
inspiring because in that process you could be working on
(21:38):
issues that are tangentially tied into trafficking, you know, some
of those underlying root causes we were talking about, and
being able to solve some of these social issues can
also help remove some of those causes or at least
diminish them somewhat, and thus have the sort of positive
feedback loop of being able to solve these these traditionally
(22:01):
incredibly difficult problems, largely because it is hard for us
to even get a grasp on all the data that
plays into this. I sometimes liken this too, you know,
making making the challenge of making a long hot term
forecast for the weather. There's just so many variables that
are out there, and they interact with each other in
(22:22):
ways that we don't fully understand. It can be difficult
to make anything, you know, uh, like a forecast that's
ten days out. On a similar front, we see this
real world you know, unfolding of of trying to tackle
these enormous social problems that also have all these different variables,
(22:42):
many of which are at their heart human issues, and
humans are largely unpredictable creatures. So it's fascinating to see
these systems that are starting to glean insights into the
way these these large systems of people and and the
way we work, how how they actually perform out in
the real world, being able to draw conclusions about that,
(23:04):
predictions and perhaps solutions. Um, what would you say are
some of the lessons you have learned in this, both
just as seeing how the t A Hub and the
related technologies have given insight into the human trafficking problem,
and also lessons you've learned as as leaders in that space.
(23:26):
Sure well, certainly from from my side, one of the
big lessons I've learned is how super motivated the IBM
staff are to get involved in initiatives like this. It's
been I was talking to somebody earlier today and I
was saying, I could spend fifty of my time talking
to volunteers within IBM who want to help, and they're
all bringing individual skills and capabilities and experience here and
(23:50):
offering to help us out with various pieces of the puzzle.
So there's a huge potential here to apply technology to
some of these challenges. The other thing that's very interesting
at the moment is a lot of these core major
social issues, whether it's the pandemic, whether it's climate change,
whether it's population displacement, whether it's trafficking. They're all intertwined
(24:13):
and one is influencing the other. And the attributes that
influence influence the prevalence of this of these events and
different parts of the world, they're very often common attributes.
So we're trying to figure out can we build models
that will help us to identify, you know, what are
the attributes that are that are interesting and trying to
lead a team through this, you know, keep them focused
(24:37):
on stuff that we have to deliver, but also giving
him the freedom and the ability to go and explore
these new opportunities and new ideas. That's as a core
learning for me. Yeah. From my side, Jonathan, I think
the first thing I discovered was that whilst we are
absolutely data rich, we are terribly knowledge poor um and
(25:02):
and the work that we've been doing together with IBM
and the Tech for Good team, I think has begun
to change that picture um and and then So the
next key element in that chain of activity needs to
be to ensure the widest possible appropriate audience can access
that knowledge. Because no one's got enough resources to do
(25:26):
everything at once. It's it's it's the classic problem. You
can only focus on so many things, so you need
to use that knowledge like I would have used intelligence
in an investigative way in law enforcement, to focus the
resources that you've got at the hot spots and points
where you can make a difference. And that that's how
(25:48):
we get this thing on the run. And we need
to we need to start undermining the economic pillars that
currently comfortably support trafficking in persons and exploitation. And I
think that we've mind a decent stuff, And I like
Neil how you brought that around to this challenge of
(26:11):
being data rich and knowledge poor. To me, that was
we're seeing that that that pivot now where the early
days of big data seem to be an emphasis on
look at how much data we have access to, and
now we are kind of moving into a new era.
We're well into a new era really where it's how
(26:32):
do we actually leverage this enormous fire hose of information.
It's coming in from all directions, generated by more devices
than ever before in the history of humanity. And we're
actually starting to see systems like the the t A Hub,
systems that are able to take that information and do
(26:53):
something that's truly useful and impactful. How do you see
the approach to trafficking changing over the course of the future.
What do you see as the evolution of addressing human trafficking.
I think the big gains are in commerce and industry.
(27:14):
I think that the ability forum for corporates to begin
to understand where they need to focus their activities and
what questions they need to ask of their suppliers, particularly
and in difficult parts of the world UM And similarly
(27:37):
for financial institutions, again it helps them because because every
errant business has a banker and a banking facility UM,
and the clues are there. If the if the customer
management process knew what those clues were and knew what
questions to ask and and our view is that the
(28:00):
more the more we grow access to the data that
we've we have two businesses and financial institutions, the greater
influence they'll have on opportunity or and reduce opportunity for
trafficking to flourish. Before I sign off in this episode,
I just want to reiterate some other things we covered
(28:24):
in this and that is these are non trivial problems.
Both the real world problem of human trafficking, which is
clearly non trivial, it is critical, and the actual computer
problems that the teams are trying to solve in order
to really take full advantage of artificial intelligence machine learning
(28:45):
and apply that to this incredibly difficult issue. Everything from
natural language processing too, pulling in information from various sources
and contextualizing it in a way that's useful. These are
hard problems to solve, but as we've seen, it is
worth it in the effort to stop human trafficking. I
(29:06):
want to thank John and Neil again for joining the episode.
It was an honor to talk with them about such
an important issue. I hope that you learned something in
this episode, and I look forward to sharing more Smart
Talks episodes with you in the near future. Take care, ye.
(29:27):
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