The rise of agentic AI is opening new possibilities for shared intelligence to inform foresight and accelerate action in the fight against modern slavery. Yet persistent silos between companies, governments and stakeholders continue to limit collective impact.
How can AI-enabled approaches connect insights, break down barriers and generate actionable intelligence to combat forced labour in global supply chains?
At Davos 2026, leaders from business, government, and worker networks argued that modern slavery persists less because of indifference than because of “darkness, the information gap.” The session centered on a Global Data Partnership Against Forced Labor, designed to connect disparate data sources without forcing sensitive data to be transferred. HPE’s John Schultz described agentic AI as the “secret sauce,” enabling insights to be extracted while preserving confidentiality—for example, matching a survivor’s reported location or recruiter name against supply chain risk signals without exposing the survivor’s identity.
Amazon’s Kara Hurst emphasized moving from clunky audits to faster, more precise prioritization. Amazon’s internal predictive tool combines historical audits, simulated signals, media and geopolitical data, with “a human in the loop” for decisions; “nine out of ten times, it’s found the highest risk suppliers.” For Mahendra Pandey of the Global Migrant Workers Network, worker engagement is both practical and moral: “Without worker engagement, you cannot understand what is the problem.” He stressed trust, privacy, and protection from retaliation.
IOM Director-General Amy Pope framed the approach as “a game changer” that can remove traffickers’ “shield,” improve border and recruitment intelligence, and shift from remediation to prevention through worker education and government action. The near-term test: proving impact in Thailand, then scaling globally.
Hello everyone, and welcome to this afternoon's session. Both people who are here with us in person and online. My name is Dan Biederman. I am a Schwab social entrepreneur and an impact investor focused on creating equitable and inclusive and sustainable supply chains. We invest in companies, including those that address forced labor using technology so very much consistent with the theme for today on the table today, among other things, is how can agentic AI all the rage these days help us in the fight against forced labor? We're not going to talk too much about the problem of forced labor. If you're here. If you're there, there are lots of resources that can help you understand it. What we want to talk about is, is a solution that is emerging. It's emerged over the last couple of years. Started in a conversation here at a couple of years ago and then launched formally last year at Whef. And the question really is, how can we bring together disparate data sources? How can we bring together different pieces of information and different stakeholders around the same digital table, and thereby increase our ability to understand the reach of forced labor, both in supply chains and elsewhere? How can we mobilize our own knowledge? And fundamentally, how can we increase the amount of action that we take? How can we create accountability for ourselves and those of our for our, for our partners? For me, ultimately this is about creating a system. Early stages, though we're at that can lead to progress against the persistent problem of forced labor. We have a wonderful panel with us today, John Schultz, who's COO of HPE and has been very important in this effort. Mahindra Pindy, who's the founder of the Global Migrant Workers Network, Amy Pope, the director general of the International Organization of Migration, and Cara Hurst, chief sustainability officer from Amazon. John, let's start with you. As someone who's been with us for a couple of years leading this and leading the conversations, both internally at HPE with the web and with other businesses. What's on the table? Why is this initiative going to give us the potential to make progress in a way that others haven't?
Well, thanks for everyone for participating. You know, when we looked at this problem a couple of years ago, what we concluded was there are a lot of people engaged in the effort to eradicate forced labor, and yet we're losing the battle. And so fundamentally, the question was, how can we have all these people well intended doing an incredible amount of work on the ground, and we're still losing ground. And what we concluded was the solve is we've got to find a way to make everyone who's engaged more effective. And as a technology company, what we know is that data leads to insights and insights lead to more effective outcomes and actions. And so the question was, okay, how do we generate more insights for the people who are engaged in this fight so they can drive better outcomes? And that meant sharing data. We have seen any number of instances in which data sharing has allowed us to have a better outcome. Maybe one of the most powerful, if not the most powerful, in the last few years was the ability of people in the vaccine world to get together and share intelligence to find vaccines for Covid. And we supported that effort with our supercomputing, etc.. So you bring the infrastructure, you bring the data and you work together and you accelerate the time to value. And so what we concluded here was that if folks on the ground who have data from survivors and the like can marry their data with governments who are involved in lots of different activities, along with NGOs who are focused on, immigration and migrant rights, with companies who focus on supply chain. If we can make all that happen, everyone in the chain can be better. Agentic AI is the secret sauce because in the old world, we would have had to do all these special data structures and then somebody would be like, well, you got to read my data and I got to read your data. And I'm worried about confidentiality and I'm worried about my own, you know, my own business propriety, etc. the beauty of a genetic AI is we can pull insights out without actually having to transfer the data. So the example I like to use is if a survivor shows up on the front lines and says, you know, I just freed myself from bondage at this location. And the people who were involved in, in, in my recruitment and essentially my enslavement was this person and this person and on my end and my supply chain team, I can be doing an agentic crawl that sees the name and sees the location and indicates that it's actually someone we use in our supply chain. I don't need to know who the survivor was. I don't even need to know their circumstances. I don't have to jeopardize their personal confidentiality, but I can immediately take action on my side to go look at that site, to go talk to those people and see if I can change the game. So Agentic AI is the secret sauce in this, and that's what's kind of given rise to this global data partnership, where we're trying to get people to share data with an agentic AI overlay and make everyone in the fight more effective.
Give us another word or two on how far we've gotten, what's been built so far, and what does it look like?
Two answers to that. As a corporate guy we've gotten so far, we have not gotten nearly far enough, right? I mean, that's right. It's the healthy impatience. But so, we've stood up the architecture. We have a lot of partners AWS, Cisco, IOM, and the like. So great partners. And we are moving from the proof of concept concept stage to the MVP stage. But again, thinking about thinking out as a corporate guy activity isn't really what matters. Outcomes are what matters. And so what we've challenged ourselves to do is find one country where we can bring this solution and show that it makes a difference. And fortunately, we've gotten tremendous support from the government of Thailand. That partnership is critical because without government, cracking this problem in a particular location would be incredibly hard. So for year two, we've got to move this into full production. We have to do it in Thailand, and we have to show that it works. And if that is achieved next year, we'll be back here talking about how many more governments can we enlist in our effort, how many more partners can we get, and therefore how much more we can scale it. So year one tremendous progress. I think we're on schedule. Year two we now need to prove it works in Thailand. And then we got to go faster.
Yep. For those interested in more of the technical details, there's a white paper online that was launched this morning by Jeff. I think if you Google Global Data Partnership Against Forced Labor, you'll be able to find it. And it goes into some more detail about the technical architecture. But the concept, of course, is improving and increasing cross-sectoral collaboration with irrefutable data that is also protected. Cara Amazon it's a large company. You're in most sectors around the world. Obviously, the company has developed systems and processes internally that embrace and address forced labor for your own operations. You've also been incredibly important in supporting some cross-sectoral collaborations as you think about the role of data in particular and this initiative, how will it help you prioritize and take action?
Yeah, well, I think that was a great introduction to a lot of what we think is a huge opportunity. We're really excited about what AI can do to accelerate our ability to take what we're very disparate signals before and connect those insights. And, I've been working on this for a long time. Our teams have been working on this. And, you know, we were just talking about how far back some of this work goes. And I think this is just it's a really critical, catalytic moment right now where the technologies that we're building, I do have real hope that they're going to transform how we're able to look at, risk how we're going to be able to make those insights actionable. And most importantly, how we're going to be able to change people's lives. Because all of that, we can talk about data and we can talk about information, but what is it going to mean for people? So I want to make that keep coming back to that connection as well, because we can talk about these things in the abstract. And, you know, we always talk about this internally, talking about risk and talking about data and collection and open data and all of that is good. But what is it going to mean for people on the ground? So I do want to come back to that at some point. But, some of the things that we've been thinking about at Amazon, when we think about data at scale and experimenting with, and the value of these public private partnerships and collaboration, we have hundreds of thousands of suppliers around the world, globally. And we, you know, obviously have supplier code of conduct, and we expect all of our suppliers to adhere to that. We have a global human rights set of principles. All of this is public. We are open about sharing policies, improving them, learning from others, all of that. But, you know, the monitoring of that, the building of the capability, building of our suppliers, their capacity to respond, and all of that is challenging. We know that, so what we want to do is to try and learn as fast as we can to go in, you know, to understand where the highest risks are, to remediate, and to continue to hold that bar high. So what AI has been allowing us to do, we've built a couple of just to get very specific about it, because I think to get to get tangible to helps. We've built an AI tool inside of Amazon, which is predictive risk analysis. And as we've started to look at that, what it what it does allow us to do is to really focus our resources on where the highest risk areas are. So what it first allows us to do is to kind of go in and scan the, analyze the historical audit information that is available, and kind of take a look across of that, what we have available. Then it also allows us to, using computer generated simulated information to kind of pull from other types of audit information that are out there. And then we have an ability to kind of look across media reporting, analytics, geopolitical data, which would give us information about what would the protective what would we think about the risks for the supplier base where we're looking, and all of that put together, I think also to your point, protects confidentiality and it protects, you know, the specifics of any particular individual or, even like, you know, factory relationships to some extent. The other thing it does is it really just helps us to not it's not going to ultimately make decisions. And I want to make that distinction about where, where we use AI and where we don't. All of this is information that then a human will still call human in the loop when we're using AI tools. So at the end of the day, AI is just going to give us really good predictive information and analytics. But you still have a human in the loop at the end of the day making all of our decisions. And I think that's a very important distinction as we talk about these tools. But the the interesting thing, as we've gone through and used this tool to make this predictive risk analysis, is when we've gone back and looked nine out of ten times, it's found the highest risk suppliers. So we've gone back and we've looked at this. Is it working is it is. And what it allows us to do is instead of utilizing all of our team's time to go in and do that heavy lifting of going through these audit reports and looking and spending all the time doing this risk analysis, we have better risk analysis much, much quicker, with much more thorough information. And our people can spend their time doing the decision making and hopefully more of the capacity building the capability, building the engagement with the suppliers or getting the the worst of the suppliers out and working with the best of the suppliers. So I think what it's going to help to do is start to improve those relationships with supply chain and again, connect those signals and focus us on the important work.
Awesome. I mean, right, the process of of of trying to understand where the risk lies in your supply chain and really any company, much less one that's as massive as Amazon, is a clunky, old fashioned process that involves social audits and other things we don't have to talk too much about. But this has the promise of shortcutting the things that can be shortcut and putting decision criteria in the hands of individual people. So let's come back to a question about kind of what that decision making process looks like. But in the moment, Mahendra, you one of the other things that's difficult for companies within supply chains is sort of engagement with the people who do the work in supply chains, workers. Right. You yourself are a migrant worker at age 19, went to Saudi from Nepal and now founder and facilitator. What do you call yourself? Orchestrator Scaffolder of the Global Migrant Workers Network, which has 23,000 members around the world. The opportunity to engage with workers throughout the world, due to due to your network, puts those in the private sector and government in a different position. How in particular, do you see participation of your members and your network in this global data partnership?
Sir, thank you so much and great to be here. And being a migrant worker in Saudi Arabia in early 20s and now coming to the World Economic Forum and being at Davos, it was, you know, life changing experience for me. But I wanted to also make sure that, you know, all the migrant workers like me, they deserve, you know, similar kind of journey. So there are more than 37 million migrant workers in Gulf and Middle East and who have been working there. Of course, everybody are not here, but I'm one of them. So you know why? Sometimes when when we talk about data, partnership and data and AI and data is not just a number and we are talking about, you know, people's life and and their journey, their experience and their story as well. So if you are wondering about, you know, what kind of data we are talking about, migrant workers, you know, their passport is taken away and their salary is delayed and they are scared about being deported, and they don't have proper accommodations and they have to pay high recruitment fee. Like me. When I went to Saudi Arabia, I had to pay 55,000 Nepali rupees for the recruitment agency and if I didn't have to pay that, that money I could have sent to my mother. And you know, if the recruitment fee you have to pay for your moneylenders and your recruitment agency, then you know most of your six months, one year and you have to work for free to pay your loan back. And we wanted to make sure that, you know, whatever discussion we are talking about data partnership or AI and technology. And we wanted to make sure that that system that helps to reduce or solve those problems and why, you know, this partnership is so important for migrant worker like me or migrant worker led organizations like ours. Because without worker engagement, you cannot understand what is the problem. And without understanding the problem, you cannot solve the problems. So, you know, sometime when you develop the AI or technology without worker engagement and you cannot have the ground truth and you cannot have the ground reality, and also whether it is going to work or not. And without worker engagement, you are you are not going to know. That's why our participation and our engagement with this initiative is very crucial. And and also we wanted to build up the trust. And there is a stereotype among government and private sectors. And if we invite migrant workers to the states like this, they always share our story, their story, they cry. But if migrant workers are like, you know, are being deported and their salary is not being paid, their passport is controlled by employer and they are not given proper break, and they have to work like, you know, in the heat temperature, like 45 to 50 degree temperature in the Middle East and Gulf and what else they can share. Of course, those are the reality. But at the same time, if you are celebrating and if you are designing the AI technology and you have to have also courage to listen migrant workers, you have to have also courage to listen the survivors and also their story, their experience, their trauma. And so that those can those things, you know, you can build up while designing the technology and the and the system as well. So, you know, sometimes there is also stereotype about migrant worker are illiterate and they don't know technology. But if you go to the like some of the labor camp or like, you know, migrant worker community, they live in the cyber world and they know, like, you know, all the technology they learn faster than maybe, you know, each of us in this room because they are desperate and they are courage and they are like, you know, happiness or the, the bring, the joy they bring is, you know, extraordinary. And that's why, you know, Dan mentioned about the global migrant worker network is the survival rate and purely worker led from the global South, from the 27 countries, and most of whom are low income migrant workers, including domestic workers, and majority of them are migrant workers are from Africa region. This is not just like, you know, platform for beneficiary and talk share about like sad and bad stories, the platform about, you know, sharing about some ideas and also sharing about the solution, engaging the initiative like this and making sure that, you know, we have a voice and without worker engagement, if there is any AI, any tools is built up and worker is not going to trust. And if in the design process, if worker are, you know, able to be part of the design process, then they also become responsible to trust and also implement and at the same time share the truth and those kind of things. I wanted to share here. And one thing I want to humbly request everyone, you know, trust survivor, trust migrant workers and engage with them. And if we engage with the migrant worker and survivor, any, any kind of system we build up, that system is going to be work perfectly. Thank you so much.
Thanks, Mahindra. Yeah, there's obviously a practical reason to engage with workers in the sense as you suggest. Otherwise you don't really know what the problem is. There's also a moral reason to engage with people who are most affected by the decisions some of the rest of us are taking. I think a lot of people, when they think about or hear about tech AI as it relates to individuals, maybe do have that impression of sort of lack of sophistication, a lot of risk, as you're suggesting, to the extent that migrant workers or workers in general are involved in the design of the technology, then the risk can be mitigated. But of course, there's also continually concerns about confidentiality. As John mentioned, the system is designed to respect the sovereignty, the confidentiality of a data holder, be it an individual or an organization going forward. So an important design consideration as well. Last but not least, Amy, come to you. You sit within one organization that operates across sectors. You yourself have been involved back in the Department of Justice years ago in trafficking prosecutions. How do you see the opportunity particularly to to link data across sectors and, and increase impact and increase outcomes and increase accountability?
It's a game changer. So, put it this way, I've been working on the issue for about 25 years. 25 years ago, people didn't actually believe that there was still modern day slavery. If you told your average consumer, if you spoke to your average CEO, people didn't recognize it. So we start there. We started with the education, and then we got to a place where everyone agreed it's a bad thing. It's not good for markets, it's not good for consumers, it's not good for companies themselves. It's not good for workers. But the ability to detect it was increasingly impossible because of the complexity of global supply chains, because of the ways in which companies use subcontractors and because of the ways that unscrupulous recruiters and traffickers were manipulating systems to basically hide within traditional ways of working. And so I really want to say thank you to the forum and to those of you who are sitting on the stage with me, and importantly, to the migrant workers themselves, because the way that we are now taking on the issue is going to fundamentally remove the shield that traffickers have been using to hide their unscrupulous activities on the backs of some of the most vulnerable and desperate people in the world. And that's that's really the magic of what we're seeing here. Historically, even the last ten years, there's been a lot of interest in how do we use data? I've worked with various companies who've looked at their own supply chains. They've asked for help in building out ethical recruitment processes. They've looked for patterns that have identified where there are bad actors in the system. But it was nearly impossible to get all the way down to the recruitment of the worker, him or herself, because there were so many levels in between. And it was not reasonable to ask someone like John to tell me what's happening within a factory in X, Y, or Z country, because John would say, well, look, I'm looking at my supply chain. It looks good. Everybody's checked off. We're, you know, but beneath the surface there was tremendous exploitation happening. So what we're doing now, being able to safeguard data but to share it, having the input of the migrant workers themselves who are seeing and experiencing it in real time, having a partner, partner like Thailand who's all in, in coming up with a solution and having some key partners who are willing to step forward and say, look, this matters to me, we're going to put some skin in the game and we're going to figure out a solution. This is this could really change the dynamic in ways we have not seen since we've recognized the phenomenon.
Wonderful. Very optimistic I appreciate that. Let's let's turn towards sort of what what we expect to change at a sort of a level of specificity or at least hope, I guess. So, Carol, let me start with you. I mean, as you think about this initiative, you know, how far can we get, I guess, I mean, what what would credible progress look like in the future? What would Amazon want to see from this? How would it help you make decisions differently, and what decisions would those be?
I think one of the things.
That will be really transformative when you talk about that vision for the future is the sharing of data. Yeah. The more that people are willing to share data, and do that in aggregated ways, and we can facilitate that through this open data partnership, and we can share what we know, we can gather new insights, all of the models that we're building, all of the, you know, ways in which we can aggregate information, the more that we can share, the more we know, the more we know across industries. And even with you mentioned that we're in we're in multiple industries at Amazon. We're certain in multiple different industry segments. Agricultural commodity supply chains look very, very different than electronic supply chains, which again, look different than the healthcare industry, which looks different than aviation or maritime. And we work in all of those. And so what I know about a supply chain in one part of my business is going to be very different than another. And while the lessons are transferable, the information about those supply chains and how workers are treated and the migration patterns and, and then you overlay geography onto that. And it's a very complex pattern around the world, of what's happening in the intersection between workers and what traffickers know and how sophisticated they're becoming as well. With technology, we're not the only people implementing technology, unfortunately. And, you know, and what's happening in those industries. So I think the more that we can share information, and, and be open about it and say this is an area where it is pre-competitive it is an area where collaboration allows us to go much faster. And if we can find those ways and this is certainly an area of your expertise, Stan, but where we can share, find ways to aggregate and share data in an open setting. We now have the technological tools in things like AI and, and AI that will allow us to even go further. And, and then quantum, which is, you know, going to even allow us to drive new insights in the future that those kinds of tools that we are now seeing and building and testing and trialling and will continue to build, will, will be informed the more data that we have. And if I take the work that we've done in carbon, which we are a little bit further ahead in, to be honest. And I think about that as an analogy, the information that we have as actual inputs versus what we're able to model, has been really an interesting path that we've been walking on for a long time. I can have certain inputs in my business where I know the actual numbers, and when I have an actual piece of information about a carbon emission versus I have to model an economic input output analysis, I can do things very differently in the business. I can drive a decarbonisation decision very much faster with a business leader than I can when I just have to model something. So it's great that we can model certain pieces of information or, you know, we can. But when you have actual information, I think you start to also say, we know this information, we've put it together, we have these actionable insights now and then you can go and say, what are the policy levers we need to then come together and drive, and we can advocate for in a collective way as an industry. What are the policy changes we need to see that will be better for workers? We can go and talk to workers and say, what are you know what? What would benefit you? Because of course, community always knows best. So I think those kinds of things, once we have real information, we now have the ability to derive the insights much faster and and get those changes going. So I'm I'm excited about that. But we need that collective ability and trust to find those data sources.
Those of us who work on human rights primarily, are always a little envious of the progress that people in focus on carbon have made, which isn't to suggest that we're unhappy with progress against carbon, but rather the the issues themselves are different. One is sort of less less measurable, less easily measurable, less scientific. It involves human activities, I guess. One of the, one of the things we're talking about, and maybe there's an analogy to the carbon world as well, is that we really have to talk both about remediation, that is, identifying where issues happen and prevention. You mentioned, Kara, the opportunity to engage, policymakers as a sort of a preventative approach. But, Amy, maybe I come to you with a version of the question that I asked Kara, which is sort of what what progress do you think we can credibly make? And and in particular, as someone who's operating at the sort of regulatory level, but at the intersection of governance and business, how do you see this leading in the direction of more preventative activities? Not not merely remedial activities?
So it's key that the insights that are pulled out of this are shared with government actors. Ultimately, for example, we at IOM have partnerships with governments to help them better manage their borders and understand who's crossing their borders. And we've been able to use AI to help various governments who may not normally have the kind of insight, to understand what are the flows of people and what are they basically, what are they up to, not down to the level of the individual, but to understand well, this this trafficking network has been operating across our border. So the more that we can share the insights that we get from something like this with our government partners, with those who are implementing on the front lines, the more they can take steps to manage their borders. Secondarily, a key piece of this is educating workers before they go. Because workers are often sold a pipe dream, they're often told, you're going to have a fabulous job, you're going to be able to send money home. Yes, you just have to pay this fee. They don't really appreciate what they're walking into. So the more we can use it as a way to educate communities before they go and work abroad, the better outcomes. And then finally, once and the more we're able to empower workers to report out and then ensure that there's capacity within governments to respond to it, the better outcomes will get. So the the private sector is key to make sure that they're setting the standard and they're setting the expectation, right. Because if everybody is happy to look away, then nothing is going to change. So having the private sector set the demand signal, having governments come behind what these are, regular regulatory expectations and then helping to fill in the gaps so that governments, community leaders, migrant workers themselves have the capacity before, especially people take that job, the better outcomes will get.
So you could see IOM being part of this pulling, pulling in some of the data and sharing it with both your governmental partners. Any businesses that ask you questions in your engagement directly with workers in-country as you have.
Absolutely. And we see it. We see really good impact when we do. For example, we have started educating migrant workers in the east of Africa who are going to work in the Gulf about what are their rights, what are the expectations, just that little bit of knowledge and knowing where to go, knowing who to call when things go the wrong way has made a significant difference.
Mahendra, my former migrant worker, someone in touch with migrant workers all the time. What are your hopes for this? What practically do you think this can lead to? Such that it enhances the work life, the quality of life for the people who are part of your network and who will continue to join your network as you grow.
So, you know, I agree with you, Amy. That worker needs to have educations and also they deserve like have educations and access to information and that helps a lot. And you know, not being in exploitation and but unfortunately, you know, even though there is a number of education and awareness being done by the IOM, ILO and the NGO and some private sectors and the government, those are not enough. And we need to, put more resources and, and just just a simple example, you know, if we are able to invest resources to build up the stadium, Disneyland and other things, why cannot we put the resources to have the migrant justice and why cannot put the resources to have justice for the people? And ultimately, you know, everybody is going to be benefited for that. And some some countries, you know, they are developing and they talk about, you know, number of GDP and number of economy and also infrastructure like building towers, bridges and roads post all those are, you know, being built up by the migrant workers. But at the same times when people they see, they only see the beauty of like towers building stadiums, Disneyland, those kind of things. And how about like exploitations? How about salary is not being paid? How about passport is taken away? How about migrant workers are being deported? How about they are being surveillance. And if we able to make that darkness in the beauty, their will become complete kind of beauty that migrant workers. Unless you know there is exploitation, unless there is a sadness, unless they have to leave their home country. Nobody wants to like, you know, leave unless they have to. You know, for me, I had to leave because of the poverty. And I wanted to, you know, help my family. My dad also went for the same purposes. Millions of migrant workers also, they are doing same thing. And one important thing, you know, I wanted to make sure that all of us recognize and also understand that, you know, without addressing those exploitation abuses and also that trauma that worker have, there won't be the like, you know, complete prosperity we talk about in the Davos prosperity dialogue and those kind of without their engagement, without understanding them it won't be that. And for me, you know, this partnership is, very unique. And since I joined, I believed and I appreciate the collaborators and wave and SP and and other in the room. This is not just the tokenism. It's giving power, giving agency to the workers. That's how I believe. And that's what I wanted to contribute as well. So why worker you know they need to be part of that. You know when we talk about worker exploitations, these are the, like evidence. And when we talk about worker story, these are the data. So if you look talk with the millions of migrant workers in the Gulf and network like, you know, hours and and if you want to hear more story about recruitment fee, more story about exploitations, more story about like, you know, whether that company is able to address some of the issue or not, you can get a data, but you need to make sure that you have a trust. And how do you build up the trust with the workers. And you need to make sure that those data are private and their privacy is secured. And, you know, you know, by saying their story. And when workers say their experience, making sure that they are not deported, they are not losing jobs and making sure that they are empowered enough. And they build up the trust with the private sector and governments. And that is second thing. And third thing is, you know, when you talk about data and if you only look the data about company audits or like, you know, UN reports and other things, you cannot get the data from audit findings. And if you look the audit findings of the company, they are not going to say that, you know, recruitment fee abuses. And they are not going to say that, worker are deported and they are not going to say that, you know, migrant workers are, you know, staying in the one room, eight people, nine people, sometimes without air conditions and those kind of things in order to have real data and real story, you need to engage with the workers. In order to engage with the workers, you need to invite and welcome them. And we we want to also welcome you as well, not only like you know, you welcome us. We also welcome you. Come us, visit us, come us, Kenya, come us, Qatar, Saudi Arabia, Nepal and be with us and see our life.
Wonderful. And, and and obviously the data partnership is one way of kind of virtually and digitally engaging one another, even if we can't all go visit your network where you gather on a on a biannual basis. John, back to you, I think sort of two part version of this question, one of which is there's always a danger in anything, where there is regulation and the potential for, embarrassment, whether you're a corporate or an individual. And, and, and, and certainly forced labor is an area where there is kind of, you know, shame attached to it and also regulatory penalty. And so how do we first part, how do we how do we make sure this does not turn into another compliance exercise? We all have lots of those. We don't need to do more work to make sort of compliance tools. Right. They they exist I guess the second part, I hope you can segue into it and I repeat it, if you forget it is what role leadership. I mean, you know, you at HPE have been pushing this both internally to your company and externally. How are how are you not only looking at it from the sort of avoiding compliance internally and as we build this, but really making sure that the world embraces this as an opportunity for impact, not just another tool that kind of helps us carry on.
So let me take those two and combine it with the question that you asked the others, which is. So my expectation is we can materially reduce, if not entirely eradicate, forced labour from the corporate economy. What I mean by the corporate economy is where you have corporate supply chains, and where traditional organizations and governments are engaged, you know, when you're into the black market and things like that, right? That's going to be a different level of things. But the reason I say that is goes back to even this notion around shame and the like, with the exception of the people who make money directly off of the slave trade, no one is pro-slavery. Yeah, like this is not a controversial issue, right? You know, and again, not to open a can of worms with climate, but you've got people who are like, well, it's not junk science and all that. We don't have any of that in slavery. Like there's nobody who's like, look, let's talk slavery. I mean, there's some positive elements to it, right? So the only thing that allows it to thrive is what Amy and Mahendra have been saying, which is darkness, the information gap. Right. So we have to turn the model around where companies and governments and the like, they're not ashamed of saying I found something they're ashamed of not looking and allowing the information gap to continue. And in reality, I think in a lot of companies, it's not that they're looking the other way. I think they're doing the best they reasonably can with the resources they have, and the fact that it's not a top priority and you can feel good about what you're doing. And the reality is you're not making a damn bit of difference. And that's the problem we have. Right? So that's why I'm so excited about the partnership. That's why I think we can have this impact, because there are lots of causes to slavery and forced labor, and we can't address all of them economic disparity, etc., etc. across the globe, etc. but the one common ingredient that we can go attack is their ability to hide in the information gap and in the darkness. This is truly one where sunlight is the best disinfectant. It is the total disinfectant. And that's what we really need to do. And so we have to change the mindset at the corporate level, government level. Don't be afraid or ashamed of what you're finding. Be ashamed of the fact that you're not looking. Be ashamed that you're not part of the data partnership and therefore you're creating darkness and an information gap that the bad people are exploiting. Be part of the solution. And, you know, we will achieve this expectation. It's all right there. Yeah. All right there in front of us. And we don't have to guess at it because the people who are living it every day, their organizations are doing this amazing work, are telling you, just don't let these people hide any longer. So let's go find them. Yeah.
Beautifully said. I think, right. Be ashamed that you're not part of the data partnership is one of the messages. One of the. contestants gone? I mean, we're open, right? We got a lot of really important partners around the table from the private sector, from the intergovernmental sector, the UN system, from governments as well, in the in the form of, of the Ministry of Ministry of Foreign Affairs from Thailand. I think from my perspective as someone who's been in the NGO side and now on the investment side, but has known many of the people who've been working on this for a long time, we have no choice but to explore alternatives to the current system. There is a a wealth of solutions out there. We need to pull them together, and one way to pull them together is to eliminate them. Where are they bringing data to the table. Where are they helping us understand the problem? But very importantly, also where can they help us understand what works and what doesn't? It's not simply a matter of identifying. It's also a matter of then remediating, then leading in the direction of preventing, taking policy steps, or changing sourcing practices, or engaging workers at scale in a different way so that the problem doesn't emerge anymore. That's ultimately the challenge in front of us. I think the data partnership, as it's structured right now, gives us a really strong chance to make progress. Ultimately, we should hold ourselves accountable, those of us who are part of it, as to whether or not it leads in the direction of measurable reduction and incidence of forced labour, both in supply chains, in civil society, as governments experience it as well. So with that, I will thank you all very much for being part of this panel and so powerfully talking about this issue that doesn't get as much attention as it probably should for its importance to individuals, governments and also businesses. Thanks to those of you in the audience, in person and those of you online, grateful for your participation. If you want to learn more, go to the website, send us a note, join. Be part. Be willing to share your insight. Share your data. Share your tools. We're in open process right now. So thank you everybody.
Thank you.
Thank you.
Thank you. Thank you so much.