WEBVTT - Smart Talks with IBM: Human Slavery Still Exists. Can AI Help Curb This Scourge? 0:00:00.080 --> 0:00:03.800 Before that, he worked in law enforcement and intelligence agencies, 0:00:03.920 --> 0:00:08.080 including Scotland Yard and the National Criminal Intelligence Service. John 0:00:08.160 --> 0:00:11.640 McGrath is a global solution Architect with IBM and works 0:00:11.680 --> 0:00:13.680 with IBM r S to find ways in which the 0:00:13.720 --> 0:00:17.759 company can turn its expertise and technology towards solving real 0:00:17.800 --> 0:00:21.280 world problems. And when it comes to real world problems, 0:00:21.600 --> 0:00:24.959 human trafficking is a major one. When you consider the 0:00:25.000 --> 0:00:27.720 impact of the issue not just on those who are 0:00:27.840 --> 0:00:31.720 the direct victims, but also their families and communities, as 0:00:31.760 --> 0:00:34.120 well as the various companies that are profiting off the 0:00:34.159 --> 0:00:39.720 proliferation of human trafficking, it can quickly become overwhelming. That's 0:00:39.720 --> 0:00:42.200 why I was excited to speak with Neil and John, 0:00:42.440 --> 0:00:44.720 as they helped me get a better understanding of the 0:00:44.800 --> 0:00:48.400 issue and how technology is playing an intrinsic and at 0:00:48.479 --> 0:00:53.800 times non intuitive part in combating human trafficking. First, let 0:00:53.840 --> 0:00:56.720 me thank both of you for being on the show, 0:00:56.840 --> 0:00:59.040 and before we get into the topic at hand, I 0:00:59.040 --> 0:01:01.560 thought it would be nice to get to know the 0:01:01.600 --> 0:01:04.160 two of you and to learn more about your background 0:01:04.160 --> 0:01:07.480 and what brought you to your current positions. So John, 0:01:07.520 --> 0:01:09.000 could we start with you. Could you tell us a 0:01:09.000 --> 0:01:12.000 little bit about yourself and what it is you do 0:01:12.200 --> 0:01:16.480 and how you got there. Sure. So, my name is 0:01:16.560 --> 0:01:20.399 John McGrath. I'm a senior solution architect for IBM in 0:01:20.440 --> 0:01:24.240 Ireland based out of the Dublin Lab in in Ireland. 0:01:24.560 --> 0:01:28.920 My background, Jonathan, is fourteen years working in lab services 0:01:28.959 --> 0:01:32.600 for IBM, which involves the dealing with clients on a 0:01:32.680 --> 0:01:36.559 daily basis. But about two and a half three years 0:01:36.560 --> 0:01:40.040 ago I got involved in the Traffic Analysis Hub initiative 0:01:40.600 --> 0:01:43.720 and from that I managed to form a team called 0:01:43.720 --> 0:01:46.039 a Tech for Good Team in Dublin and uh and 0:01:46.120 --> 0:01:48.280 that's what I do on a daily basis. Now I 0:01:48.320 --> 0:01:50.720 work with the Tech for Good Team excellent and Neil, 0:01:50.840 --> 0:01:54.640 can you tell us a bit about yourself and your position? Surely, 0:01:54.720 --> 0:01:59.120 Jonathan M. So, my name is Neil Jonles. I'm currently 0:01:59.240 --> 0:02:03.279 CEO of a new reform not for profit called Traffic 0:02:03.360 --> 0:02:09.360 Analysis Hub. My journey here is a torturous one. I 0:02:09.400 --> 0:02:15.559 spent thirty six years in law enforcement in the United Kingdom, 0:02:15.600 --> 0:02:19.639 concluding that time as Deputy Director of our National Agency. 0:02:20.320 --> 0:02:24.960 I'm an organized crime intelligence expert UM and while I 0:02:25.040 --> 0:02:28.720 was serving with our National Agency, I came across a 0:02:28.760 --> 0:02:32.119 small not for profit called Stop the Traffic, who were 0:02:32.360 --> 0:02:37.280 specializing in preventing human trafficking. They began their work with 0:02:37.400 --> 0:02:41.919 the cocoa industry in West Africa that was using thousands 0:02:41.960 --> 0:02:46.600 of child slaves to pick cocoa for our chocolate UM 0:02:46.639 --> 0:02:48.960 and I was disappointed to learn that they knew more 0:02:49.000 --> 0:02:53.079 about trafficking than the intelligence systems in my national agency. 0:02:53.639 --> 0:02:58.320 UM so began formula relationship with them too, to grow 0:02:58.800 --> 0:03:01.680 that understanding in the agency and and to begin to 0:03:01.720 --> 0:03:05.639 build unusual partnerships with NGOs and other subject matter experts. 0:03:05.680 --> 0:03:09.840 And when I left law enforcement nine years ago, I 0:03:09.919 --> 0:03:14.400 began working with Stop the Traffic more routinely, realizing that 0:03:14.800 --> 0:03:18.240 we needed a richer picture of trafficking if we were 0:03:18.280 --> 0:03:22.600 going to be effective as societies to begin to make 0:03:22.639 --> 0:03:26.880 a history. We haven't done that yet, but we've begun 0:03:26.960 --> 0:03:29.519 to create that richer picture through the work that we've 0:03:29.560 --> 0:03:32.359 been doing. And Neil, I think you've hit on something 0:03:32.680 --> 0:03:35.080 that I really wanted to focus on in the early 0:03:35.200 --> 0:03:39.160 part of our conversation, the fact that even in your 0:03:39.240 --> 0:03:44.040 role in intelligence, that there was a lack of real 0:03:44.240 --> 0:03:49.360 knowledge about human trafficking. I think that certainly can apply 0:03:49.520 --> 0:03:53.600 to the general population. I know that for myself, it's 0:03:53.640 --> 0:03:57.800 something that I am aware happens, and typically I don't 0:03:57.840 --> 0:04:02.040 really even think about it until I'm going through an 0:04:02.040 --> 0:04:04.480 airport and I see a poster that's bringing it to 0:04:04.520 --> 0:04:09.480 your attention directly, and otherwise I'm kind of in the dark. 0:04:09.720 --> 0:04:13.640 Can you give us sort of a an outline of 0:04:13.800 --> 0:04:16.320 how big a problem this is? Give us the scope 0:04:16.320 --> 0:04:21.480 and the impact of human trafficking. Human trafficking is pretty 0:04:21.520 --> 0:04:28.400 well defined as a global phenomenon now. The the academic estimates, 0:04:28.480 --> 0:04:32.159 which are reasonable, suggests that something like fourteen million people 0:04:32.240 --> 0:04:36.479 globally are in circumstances that we would be comfortable to 0:04:36.560 --> 0:04:42.280 describe as trafficking and exploitation. Um, that's an enormous number 0:04:42.320 --> 0:04:47.240 of people. Even in in the UK, the best estimates 0:04:47.279 --> 0:04:51.080 suggest that something like d thirty five thousand people are 0:04:51.080 --> 0:04:56.599 in circumstances of exploitation having been trafficked. So you could 0:04:56.680 --> 0:05:00.400 fill the biggest sports stadium that we've got twice with 0:05:00.440 --> 0:05:03.400 those people. And I think the best way of describing 0:05:03.400 --> 0:05:06.920 it to people is that it's it's an errant economy 0:05:06.960 --> 0:05:12.960 in its own right. Traffic, trafficking and exploitation splits into 0:05:12.960 --> 0:05:18.560 two chunks thirty five percent estimate of those people and 0:05:18.600 --> 0:05:22.640 exploitation tend to be in some aspect of commercial sexual 0:05:22.680 --> 0:05:29.000 exploitation are in labor markets, particularly those labor markets that 0:05:29.760 --> 0:05:36.120 rely on seasonal workers contract workers, so agriculture, food processing, 0:05:36.120 --> 0:05:44.880 and manufacturing, construction, big fishing, sea fleets, logistics are very 0:05:44.920 --> 0:05:51.680 popular destinations for traffic labor where very criminal recruitment gangs 0:05:52.440 --> 0:05:56.800 infiltrate them into the workforce. Most people's journeys into exploitation 0:05:57.279 --> 0:06:00.680 beginners journeys of hope. They're tricked into taking a journey 0:06:00.760 --> 0:06:02.840 on the basis that there's a great new future for 0:06:02.880 --> 0:06:05.359 them and their family, and then when they get to 0:06:05.440 --> 0:06:09.880 that destination, it turns the dust and becomes a creeping 0:06:10.000 --> 0:06:15.120 debt bondage situation. And it's worth something like three quarters 0:06:15.120 --> 0:06:18.800 of a trillion dollars a year. We estimate. There's a 0:06:18.839 --> 0:06:22.520 new official estimate out this year or sorry, early next 0:06:22.560 --> 0:06:28.080 year that will define it slightly differently, probably m but 0:06:28.080 --> 0:06:31.320 but that's our best guests. I hope that that that 0:06:31.440 --> 0:06:35.279 gives you a sense of of of how the thing operates. 0:06:36.279 --> 0:06:40.279 It needs to recruit something like of its workforce newly 0:06:40.360 --> 0:06:44.039 every year, so somewhere somewhere up to eight million people 0:06:44.040 --> 0:06:47.760 a year as a recruitment requirement. It's about money, and 0:06:48.240 --> 0:06:51.800 most of that money goes through financial institutions, And it's 0:06:51.800 --> 0:06:56.120 about creating a market, creating demand and maintaining demand. And 0:06:56.160 --> 0:06:59.240 it can't be solved just by the justice process, and 0:06:59.320 --> 0:07:03.680 it can't be solved just by humanitarian activity rescuing and 0:07:03.720 --> 0:07:10.120 rehabilitating Neil. That also brings me to a follow up question. Traditionally, 0:07:11.000 --> 0:07:14.800 what measures do various agencies and governments take in an 0:07:14.840 --> 0:07:18.880 effort to prevent human trafficking? You had mentioned that this 0:07:19.000 --> 0:07:22.120 is beyond the scope of any one organization, but what 0:07:22.200 --> 0:07:26.560 are the sort of efforts that have been put forward? Uh, 0:07:26.720 --> 0:07:31.440 so far, we need traffickers to have a real sense 0:07:31.680 --> 0:07:35.440 of risk if they do this, that that they are 0:07:35.560 --> 0:07:40.000 likely to be discovered and held to account. And therefore 0:07:40.040 --> 0:07:44.920 there there is a significant role for investigators for the 0:07:45.040 --> 0:07:49.480 justice process. But but more broadly, we need to think 0:07:49.520 --> 0:07:54.040 about the problem in an economic sense, um and and 0:07:54.120 --> 0:07:57.760 that's the aspect that I think has taken too long 0:07:58.280 --> 0:08:01.360 to develop. You know, in lots of parts of the world, 0:08:01.400 --> 0:08:05.440 the justice process doesn't work well, and of course trafficking 0:08:05.680 --> 0:08:10.680 is a global issue. In the more developed societies, the 0:08:10.760 --> 0:08:15.160 justice process does hold people to account, perhaps not in 0:08:15.240 --> 0:08:18.040 the numbers that we might like, but but it's a 0:08:18.080 --> 0:08:22.800 sanction that people fear um and and therefore it's a 0:08:22.880 --> 0:08:27.800 very worthy element of of the program. Um And and 0:08:28.000 --> 0:08:30.480 encouraging other parts of the world where that doesn't work 0:08:30.520 --> 0:08:33.600 so well to get better at it is really important. 0:08:34.720 --> 0:08:38.760 But we have over relied on in my view, on 0:08:38.760 --> 0:08:43.520 on that outcome as the resolution to the problem. And 0:08:43.559 --> 0:08:47.160 of course, while there's money to make in good quantity 0:08:47.520 --> 0:08:54.199 and not enough fear of sanction, then traffickers will still 0:08:54.200 --> 0:08:59.200 flourish and demand will still maintain or grow. Right, so 0:08:59.360 --> 0:09:03.800 without us having any you know, without addressing those root causes, 0:09:04.240 --> 0:09:09.280 what we're looking at really is dealing with the consequences, 0:09:09.320 --> 0:09:13.880 and that's just going to be a consistent issue without 0:09:13.920 --> 0:09:17.960 addressing those root causes. Obviously, this is an enormous issue 0:09:18.520 --> 0:09:21.840 that is going to require a lot of work across 0:09:21.880 --> 0:09:26.040 the globe in order to really tamp down on it. John, 0:09:26.240 --> 0:09:31.320 I'm curious about how you come into the picture. We're 0:09:31.360 --> 0:09:34.960 about to start talking about using technology in a way 0:09:35.000 --> 0:09:39.360 to detect and then take measures to prevent things like 0:09:39.440 --> 0:09:46.240 human trafficking, how did you get involved with this particular challenge. Okay, 0:09:46.400 --> 0:09:48.719 So I think I mentioned earlier Jonathan that I was 0:09:48.760 --> 0:09:51.480 working as a services person. So I was based in 0:09:51.520 --> 0:09:54.319 the Middle East working with some government agencies on behalf 0:09:54.320 --> 0:09:57.880 of IBM Security, and in my role, I had a 0:09:57.960 --> 0:10:01.680 give back opportunity and I was invited by IBM Corporate 0:10:01.720 --> 0:10:05.360 Social Responsibility to come to London to help facilitate a 0:10:05.400 --> 0:10:08.960 workshop for Stop the Traffic and that was the first 0:10:09.600 --> 0:10:12.600 exposure I had really to the issue of human trafficking 0:10:12.600 --> 0:10:15.960 beyond what the casual lay person knew about it. But 0:10:16.000 --> 0:10:18.000 the thing that was interesting for me when I walked 0:10:18.000 --> 0:10:21.360 into the room to host the workshop was the attendees 0:10:21.640 --> 0:10:24.599 weren't just the people I expected. So I expected to 0:10:24.640 --> 0:10:27.960 see non government organizations and not for profits there, and 0:10:28.000 --> 0:10:31.439 I expected to see law enforcement agencies and some government agencies. 0:10:32.600 --> 0:10:35.760 What I didn't expect to see where financial institutions, and 0:10:35.800 --> 0:10:38.800 there were a lot of financial institutions present. And it 0:10:38.880 --> 0:10:41.520 was really during that workshop that I kind of got 0:10:41.520 --> 0:10:45.079 the realization that this was across sectoral issue and the 0:10:45.160 --> 0:10:49.120 solution had to come from multisectoral collaboration. So that was 0:10:49.160 --> 0:10:51.920 really the starting point for me and from that I 0:10:51.960 --> 0:10:54.120 worked with Neil and to stop the traffic team to 0:10:54.200 --> 0:10:57.840 learn more about the issue, and I spent many evenings 0:10:57.840 --> 0:11:00.720 and weekends in the hotel in the Middle Least building 0:11:00.720 --> 0:11:04.800 prototypes and sampling what could be done using various technologies. 0:11:06.040 --> 0:11:08.319 All are all based on this principle of how do 0:11:08.400 --> 0:11:12.400 we get to data sharing collaboration around this issue. Can 0:11:12.440 --> 0:11:15.000 you talk a little bit more about those technologies, what 0:11:15.559 --> 0:11:18.640 form did they take? What was it that you were thinking, like, 0:11:19.000 --> 0:11:21.760 what metrics are you looking at and how are you 0:11:21.840 --> 0:11:27.880 analyzing them? Sure? The the starting point in the first 0:11:27.880 --> 0:11:29.800 workshop was there was kind of a division in the 0:11:29.880 --> 0:11:33.319 room depending on the agencies and the at the sort 0:11:33.320 --> 0:11:35.920 of core mission of each organization, but there was a 0:11:35.960 --> 0:11:40.720 basic two requirements primary requirements that came out. The first 0:11:40.880 --> 0:11:44.640 was for this ability to do a global level analysis 0:11:44.679 --> 0:11:48.479 of the problem to see where the areas of intensity were, 0:11:48.480 --> 0:11:51.760 first particular types of trafficking, to be able to see 0:11:51.760 --> 0:11:55.400 how this is influenced by not just geography but by time. 0:11:56.320 --> 0:11:58.880 And then also there there was a requirement to be 0:11:58.920 --> 0:12:01.600 able to see the roots we're being used by the 0:12:01.600 --> 0:12:04.880 traffickers to move their their victims from point day to 0:12:04.920 --> 0:12:07.320 point b so, so that was kind of the one 0:12:07.320 --> 0:12:09.240 half of the room. We're looking for this macro level 0:12:09.320 --> 0:12:12.000 view that would give them the global picture and and 0:12:12.280 --> 0:12:14.680 if you like, validate some of the high level figures, 0:12:14.720 --> 0:12:17.720 the estimated figures that Neil was talking about earlier. And 0:12:17.760 --> 0:12:21.040 then the other half of the room were more interested in, Okay, 0:12:21.080 --> 0:12:23.440 now that I know where the issue is, how do 0:12:23.520 --> 0:12:25.840 I pull that into a secure environment where I could 0:12:25.840 --> 0:12:29.120 start to investigate it and start to understand the network 0:12:29.120 --> 0:12:31.920 in more detail. Who are the people involved, how are 0:12:31.920 --> 0:12:35.280 they moving people? What tools are they using? You know, 0:12:35.320 --> 0:12:38.760 what addresses, account numbers, all that kind of stuff. So 0:12:38.880 --> 0:12:41.960 we had this kind of a double requirement, so we 0:12:42.000 --> 0:12:44.480 started to look at what kind of technologies we used 0:12:44.480 --> 0:12:46.640 and used in the past which could help to satisfy 0:12:46.760 --> 0:12:51.160 both of these requirements. While you were developing this in 0:12:51.160 --> 0:12:53.960 the early days, what were some of the lessons you learned, 0:12:53.960 --> 0:12:56.520 What were things that you know, were there pathways that 0:12:56.640 --> 0:12:59.920 you were taking early on that turned out to be 0:13:00.200 --> 0:13:03.559 less rutful than you hoped, or things that you discovered 0:13:03.600 --> 0:13:07.520 that surprised you while you were developing this early approach. 0:13:08.200 --> 0:13:10.839 Sure the well, one of the first things that hit 0:13:10.960 --> 0:13:15.160 us wasn't necessarily a surprise, Jonathan, but uh, the extent 0:13:15.520 --> 0:13:18.400 of how it impacted us kind of surprised. This was 0:13:18.480 --> 0:13:22.800 the whole data privacy issue and the challenges around sharing 0:13:22.880 --> 0:13:27.160 data across jurisdictions. So so this became a reasonably high 0:13:27.280 --> 0:13:29.880 priority in our requirements, if you like. When we were 0:13:29.880 --> 0:13:33.360 trying to design the system. A lot of the basis 0:13:33.360 --> 0:13:35.000 of what we were trying to do is captured data 0:13:35.080 --> 0:13:38.079 from all over the world and make it available to 0:13:38.240 --> 0:13:40.880 partners from all over the world. But we had to 0:13:40.880 --> 0:13:43.240 be very careful that we took out any sensitive information, 0:13:43.240 --> 0:13:46.040 any unique identifiers, and then we had to run the 0:13:46.120 --> 0:13:50.320 proposals true you know, various legal people to give us 0:13:50.320 --> 0:13:53.280 advice on whether or not we were following the right path. 0:13:53.880 --> 0:13:56.480 So not not so much a technical issue, although there 0:13:56.480 --> 0:13:59.760 are technologies that can help with this. It was more about, 0:13:59.800 --> 0:14:02.839 you know, requirements issue. And then we started to look 0:14:02.880 --> 0:14:06.360 at things like, um, the largest amount of the data 0:14:06.600 --> 0:14:09.400 is contained in the narratives that the victims are, the 0:14:09.480 --> 0:14:13.319 narratives about the stories the victims, and to do that 0:14:13.360 --> 0:14:16.720 we we turned to natural language understanding and machine learning, 0:14:17.280 --> 0:14:20.320 and then we hit the challenges that everybody hits in 0:14:20.360 --> 0:14:23.640 this domain of making sure it's accurate, make sure it's unbiased, 0:14:23.680 --> 0:14:28.320 but also dealing with multilingual issues, so a lot of 0:14:28.360 --> 0:14:31.320 the data is not necessarily in the primary languages. So 0:14:32.400 --> 0:14:34.160 that that was another one of the big challenges that 0:14:34.200 --> 0:14:38.160 we had to think about. Yes, this is an enormous challenge, 0:14:38.240 --> 0:14:41.920 justin in machine learning in general, is the natural language 0:14:42.080 --> 0:14:46.680 processing and being able to parse what someone means when 0:14:46.680 --> 0:14:49.360 they say something in particular way. And I imagine when 0:14:49.400 --> 0:14:53.760 you are trying to handle or analyze an enormous amount 0:14:53.760 --> 0:14:59.200 of data, that problem becomes magnified enormously. What was it 0:14:59.800 --> 0:15:04.320 the a particular set of efforts that then led into 0:15:04.480 --> 0:15:07.280 the Traffic Analysis hub or did that come about in 0:15:07.320 --> 0:15:12.120 a different way. Yeah, the Traffic Analysis Hub came out 0:15:12.160 --> 0:15:14.120 of a kind of vision that stopped the traffic it 0:15:14.240 --> 0:15:17.480 had for a while. It became part of that workshop 0:15:17.520 --> 0:15:21.240 on the back in London. It was that macro level 0:15:21.360 --> 0:15:24.600 view that everybody could share and everybody got value from, 0:15:24.640 --> 0:15:27.680 and that became the primary target for the initial prototypes. 0:15:28.080 --> 0:15:29.720 So when we were looking at that, we were trying 0:15:29.760 --> 0:15:32.560 to get a geospatial view, you know, a map based 0:15:32.600 --> 0:15:35.000 analysis of data. We were trying to figure out how 0:15:35.000 --> 0:15:38.000 to capture data, and then we realized that every different 0:15:38.040 --> 0:15:41.400 source that we accessed kind of classified their data uniquely, 0:15:41.920 --> 0:15:44.680 and now it's very difficult to do comparative analysis across 0:15:44.760 --> 0:15:47.360 these things. So then we hit the challenge of how 0:15:47.360 --> 0:15:50.000 do we make it consistent so that it makes sense 0:15:50.000 --> 0:15:53.880 to everybody. And then we we hit challenges like things 0:15:53.920 --> 0:15:58.080 like locations. So there's lots of in the narratives of stories, 0:15:58.120 --> 0:16:01.520 there's lots of references to location. We needed to understand 0:16:01.560 --> 0:16:05.040 not just where location was referenced, but the context in 0:16:05.080 --> 0:16:07.520 which has been referenced. And then when we knew that, 0:16:07.960 --> 0:16:09.640 we had to go find a coordinates for it to 0:16:09.640 --> 0:16:11.280 put it on the map. But we had to be 0:16:11.320 --> 0:16:13.360 careful that we were getting to correct coordinates for the 0:16:13.400 --> 0:16:16.640 correct location because there's lots of For instance, I think 0:16:16.640 --> 0:16:18.880 there's seventeen different Londons around the world, so we have 0:16:18.960 --> 0:16:21.680 to be clear about which London was actually been referenced 0:16:21.720 --> 0:16:24.400 in text. So so that was really kind of the 0:16:24.640 --> 0:16:28.440 progression of the prototypes. Yeah, I think that for for 0:16:28.600 --> 0:16:31.480 a lot of people, myself included, we can sometimes fall 0:16:31.520 --> 0:16:34.840 into a trap where we're thinking about these very sophisticated 0:16:34.840 --> 0:16:40.280 systems pulling data as if it's magically all in a centralized, 0:16:41.160 --> 0:16:45.520 uniform database. I think the magical thing for a lot 0:16:45.600 --> 0:16:48.960 of folks who look into this is that we see 0:16:48.960 --> 0:16:53.360 how these systems are able to spot patterns, uh and 0:16:53.480 --> 0:16:56.840 trends in data sets that are so enormous that to 0:16:57.000 --> 0:17:00.720 us there's no signal, it's just noise. So seeing something 0:17:01.080 --> 0:17:04.760 that can pick out the signal does seem a little magical. Well, 0:17:04.800 --> 0:17:08.040 as the t A hub is evolving and taking shape, 0:17:08.040 --> 0:17:11.879 have we already seen some impact in the real world? 0:17:12.040 --> 0:17:15.720 Is it being used right now to help identify and 0:17:15.800 --> 0:17:22.199 prevent trafficking? Today? It's being used by over We have 0:17:22.240 --> 0:17:24.920 over a hundred organizations who are members of the Hub 0:17:24.960 --> 0:17:27.880 at this point, and all of them have their own 0:17:27.880 --> 0:17:31.320 secret missions or their own, uh, their own core missions 0:17:31.359 --> 0:17:33.199 of what they're they're trying to achieve with us, But 0:17:33.280 --> 0:17:37.000 we have anecdotal stories from various parties of where they've 0:17:37.040 --> 0:17:39.479 got value from the data that's in the Hub. And 0:17:39.600 --> 0:17:42.960 sometimes the value, interestingly, is not just in the data, 0:17:43.040 --> 0:17:46.119 it's in the collaboration with their peer organizations and the 0:17:46.119 --> 0:17:48.760 other partners in the hub, which was part of what 0:17:48.920 --> 0:17:51.159 we tried to set out to do in the first place, 0:17:51.600 --> 0:17:56.159 was achieved as kind of safe collaborative environment where people 0:17:56.240 --> 0:18:00.480 could share their expertise as well as their knowledge for 0:18:00.560 --> 0:18:03.480 the purpose of disrupting human trafficking. But we have got 0:18:03.520 --> 0:18:06.800 a lot of feedback from various partners where they've been 0:18:06.800 --> 0:18:09.200 able to validate data that they had seen in their 0:18:09.200 --> 0:18:13.760 internal systems when they were starting to investigate issues. They're 0:18:13.760 --> 0:18:15.880 able to validate some of that in the Hub by 0:18:15.920 --> 0:18:19.960 looking at the data that we've been collecting. And then conversely, 0:18:20.040 --> 0:18:23.479 we've also had the same feedback from organizations who are 0:18:23.520 --> 0:18:26.960 investigators or say, we're able to identify new areas of 0:18:27.000 --> 0:18:30.160 investigation in the Hub that we weren't aware of because 0:18:30.160 --> 0:18:33.080 we've never looked there before, but once we started to look, 0:18:33.119 --> 0:18:36.440 we started to see patterns in our own data sets 0:18:36.440 --> 0:18:39.919 in those locations. There are facilities for different audiences in 0:18:39.960 --> 0:18:43.520 the Hub, So you've got people like researchers and academia 0:18:43.520 --> 0:18:47.640 who come in and the facility we have in which 0:18:48.040 --> 0:18:51.639 in the hub, which allows them to navigate by concept 0:18:52.040 --> 0:18:56.199 through large news data sets, and that's a facility that 0:18:56.280 --> 0:18:59.040 they give us feedback on a lot that tells us 0:18:59.080 --> 0:19:02.720 it helps them to find information and to support their 0:19:02.840 --> 0:19:06.600 their research. We had one person who um Every month 0:19:06.680 --> 0:19:09.359 we have an analyst call in the community where the 0:19:09.400 --> 0:19:12.000 community and the Hub come together. They look at the 0:19:12.000 --> 0:19:15.000 functionalities that we're building and the data sets that were gathering, 0:19:15.000 --> 0:19:17.000 and they give us direction of what they need and 0:19:17.040 --> 0:19:19.919 we feature a participant on that every month. So we 0:19:20.000 --> 0:19:23.640 have had a person who actually actually presented their thesis 0:19:24.480 --> 0:19:26.760 and part of their thesis was based on data that 0:19:26.800 --> 0:19:29.840 they pulled in from the Hub to to validate their 0:19:29.840 --> 0:19:34.480 own their ow own insights into human trafficking. That's phenomenal. 0:19:34.520 --> 0:19:37.800 So not just building a system that's doing this very 0:19:37.800 --> 0:19:41.919 technical work, but also just building these relationships, forming relationships 0:19:41.960 --> 0:19:47.639 across various sectors and various countries that can all be 0:19:48.119 --> 0:19:52.920 you know, directed toward helping stop this problem. What other 0:19:53.000 --> 0:19:58.360 ways do you see the Traffic Analysis Hub impacting various industries? 0:19:59.000 --> 0:20:04.480 So we've well, first off, we've built a platform underpinning 0:20:04.480 --> 0:20:07.720 the Traffic Analysis Hub which allows allows us to reuse 0:20:07.760 --> 0:20:12.440 the capabilities across different um issues. So we've also used 0:20:12.440 --> 0:20:16.679 it for things like food redistribution to avoid food waste, 0:20:16.720 --> 0:20:19.840 and we've also used it in the area of migration 0:20:19.920 --> 0:20:25.159 and population displacement and trying to create prediction models and stuff. 0:20:25.560 --> 0:20:28.680 So the thing that kind of excites me about this 0:20:28.760 --> 0:20:32.480 is we're starting to bring in new sectors, but also 0:20:33.280 --> 0:20:36.600 not just industry sectors, but sectors within the n g 0:20:36.760 --> 0:20:40.919 O world who are focused on different parts of of 0:20:40.920 --> 0:20:45.480 of social issues and we're bringing together into one platform 0:20:45.560 --> 0:20:48.320 and one community and start to share information. So we've 0:20:48.320 --> 0:20:51.159 been approached by organizations who are who are focused on 0:20:51.200 --> 0:20:53.560 animal trafficking to see see if they can get access 0:20:53.600 --> 0:20:55.679 to the hub and start to share their data in 0:20:55.720 --> 0:20:58.040 there as well. And we're all starting to see the 0:20:58.400 --> 0:21:01.800 reusability of some of the things that we've built. For instance, 0:21:01.800 --> 0:21:05.960 we've built a causality model in partnership with IBM Research, 0:21:06.520 --> 0:21:09.480 and where we were looking at the cause that the 0:21:09.520 --> 0:21:15.120 attributes that are most prevalent in causing things like population displacements, 0:21:15.880 --> 0:21:18.480 and these models are things that we can then reapply 0:21:19.040 --> 0:21:22.920 from one use case to another. So we're trying trying 0:21:22.960 --> 0:21:25.280 now to move that model into human trafficking to see 0:21:25.280 --> 0:21:29.480 if we can determine, for instance, the the likely outcome 0:21:29.520 --> 0:21:34.000 analysis for interventions in certain locations. To me, that's also 0:21:34.040 --> 0:21:38.080 inspiring because in that process you could be working on 0:21:38.240 --> 0:21:43.280 issues that are tangentially tied into trafficking, you know, some 0:21:43.359 --> 0:21:46.879 of those underlying root causes we were talking about, and 0:21:46.920 --> 0:21:50.280 being able to solve some of these social issues can 0:21:50.320 --> 0:21:53.960 also help remove some of those causes or at least 0:21:54.000 --> 0:21:57.840 diminish them somewhat, and thus have the sort of positive 0:21:57.840 --> 0:22:01.800 feedback loop of being able to solve these these traditionally 0:22:01.800 --> 0:22:05.840 incredibly difficult problems, largely because it is hard for us 0:22:05.840 --> 0:22:09.360 to even get a grasp on all the data that 0:22:09.680 --> 0:22:13.080 plays into this. I sometimes liken this too, you know, 0:22:13.400 --> 0:22:17.360 making making the challenge of making a long hot term 0:22:17.480 --> 0:22:19.960 forecast for the weather. There's just so many variables that 0:22:20.000 --> 0:22:22.600 are out there, and they interact with each other in 0:22:22.640 --> 0:22:26.640 ways that we don't fully understand. It can be difficult 0:22:26.680 --> 0:22:30.080 to make anything, you know, uh, like a forecast that's 0:22:30.119 --> 0:22:34.800 ten days out. On a similar front, we see this 0:22:34.920 --> 0:22:38.159 real world you know, unfolding of of trying to tackle 0:22:38.200 --> 0:22:42.760 these enormous social problems that also have all these different variables, 0:22:42.800 --> 0:22:46.639 many of which are at their heart human issues, and 0:22:46.760 --> 0:22:50.840 humans are largely unpredictable creatures. So it's fascinating to see 0:22:51.280 --> 0:22:55.040 these systems that are starting to glean insights into the 0:22:55.080 --> 0:22:58.280 way these these large systems of people and and the 0:22:58.280 --> 0:23:00.919 way we work, how how they actually perform out in 0:23:00.920 --> 0:23:04.520 the real world, being able to draw conclusions about that, 0:23:04.640 --> 0:23:09.480 predictions and perhaps solutions. Um, what would you say are 0:23:09.840 --> 0:23:12.359 some of the lessons you have learned in this, both 0:23:12.480 --> 0:23:17.560 just as seeing how the t A Hub and the 0:23:17.640 --> 0:23:22.760 related technologies have given insight into the human trafficking problem, 0:23:22.800 --> 0:23:26.119 and also lessons you've learned as as leaders in that space. 0:23:26.640 --> 0:23:29.639 Sure well, certainly from from my side, one of the 0:23:29.680 --> 0:23:32.600 big lessons I've learned is how super motivated the IBM 0:23:32.720 --> 0:23:36.640 staff are to get involved in initiatives like this. It's 0:23:36.640 --> 0:23:39.600 been I was talking to somebody earlier today and I 0:23:39.760 --> 0:23:41.920 was saying, I could spend fifty of my time talking 0:23:41.920 --> 0:23:45.240 to volunteers within IBM who want to help, and they're 0:23:45.280 --> 0:23:50.520 all bringing individual skills and capabilities and experience here and 0:23:50.640 --> 0:23:53.639 offering to help us out with various pieces of the puzzle. 0:23:54.520 --> 0:23:58.639 So there's a huge potential here to apply technology to 0:23:58.720 --> 0:24:01.640 some of these challenges. The other thing that's very interesting 0:24:01.640 --> 0:24:04.879 at the moment is a lot of these core major 0:24:04.960 --> 0:24:08.240 social issues, whether it's the pandemic, whether it's climate change, 0:24:08.240 --> 0:24:12.920 whether it's population displacement, whether it's trafficking. They're all intertwined 0:24:13.760 --> 0:24:16.199 and one is influencing the other. And the attributes that 0:24:16.320 --> 0:24:20.600 influence influence the prevalence of this of these events and 0:24:20.760 --> 0:24:24.440 different parts of the world, they're very often common attributes. 0:24:25.440 --> 0:24:27.679 So we're trying to figure out can we build models 0:24:27.680 --> 0:24:29.639 that will help us to identify, you know, what are 0:24:29.680 --> 0:24:32.960 the attributes that are that are interesting and trying to 0:24:33.080 --> 0:24:37.000 lead a team through this, you know, keep them focused 0:24:37.000 --> 0:24:39.040 on stuff that we have to deliver, but also giving 0:24:39.080 --> 0:24:44.040 him the freedom and the ability to go and explore 0:24:44.080 --> 0:24:49.000 these new opportunities and new ideas. That's as a core 0:24:49.280 --> 0:24:53.600 learning for me. Yeah. From my side, Jonathan, I think 0:24:53.640 --> 0:24:56.000 the first thing I discovered was that whilst we are 0:24:56.400 --> 0:25:02.639