 Alright, welcome everyone. This is my name is Santiago City. I'm a co-founder of Democracy Earth. We're a non-profit organization that basically tries to understand what democracy is in the information age. And we have been doing a bunch of crazy democratic experiments over the last five years. We tried everything from big democracy, direct democracy, participatory budgeting, quality voting, you name it. And we work with all kinds of organizations. We work with political parties, we work with parliaments, congresses, non-profit organizations and also decentralized networks. And here's the thing that I can tell you after five years of doing democratic experiments. Usually when people go out there for a vote, it's because it's a very high-risk decision. And the higher the risk, the higher the need for legitimacy. And when it's a contentious decision, there's also a lot of interest in trying to corrupt or subvert or manipulate the decision-making process. So over these five years, we have also been hacked a lot of times. And democracy is one of the most hostile environments to deploy systems. So we learned a lot. But today I want to talk about the core of the problem that is in decentralized networks. We know very well what happened with centralized networks. With centralized networks, there's one company, one Silicon Valley corporation that broke democracy. And that's Facebook. And the reason Facebook broke democracy is simply because they were the best ones at formalizing humans on the web. And that means that Facebook today has a database with 2.7 billion people, larger than the Chinese government, larger than any government on earth. And now they are even considering doing their own currency. So when you become the largest repository of identity with people taking selfies, training your face AI algorithms every day, you'll likely end up breaking whatever technology is, precedent technologies, democracy is being made with me. Now when we look into blockchain space, there's a tremendous amount of innovation happening. But there's one key missing ingredient which the web got in an authoritarian way, which is identity, humans. Where are the humans in the blockchain? We are lacking tools to formalize humans over these networks. So the problem with this is that it's limiting the social impact of blockchains. Blockchains have been extremely good on the financial part of the spectrum of economic activity. But the promise of what blockchains can achieve in terms of transforming our society beyond finance, beyond capital is not there. If we look quickly at proof of work, which inaugurated our whole space with Satoshi's paper, the explicit mention of Satoshi in his paper in relation to governance is one CPU, one bolt. And basically one CPU, one bolt is ruled by the machines, not by the people, ruled by capital, not by society. And this has, you know, this makes sense in the world of finance where privacy is a fundamental right. And this has bent the design of these protocols around finance and around protecting privacy. But at the same time we're still witnessing how democracy is being disrupted and very broken all around the world because of digital technology as well. So the potential of decentralized networks of blockchain-based networks to use this, this, the tremendous destructive capacity of these technologies to improve our democracies or to level up our democracies for the challenges of the 21st century must not be lost. Now, when we look into proof of state systems, something we all know here in this room is that proof of state systems building with your money and whoever has more money wins is a plutocratic way of governance. And maybe this makes sense for private companies where you want the largest shareholders with the largest scheme in the game to have more influence in the decision-making process. And when it's about public goods, when it's about public infrastructure, not necessarily a scheme in the game is a good alternative to weight the conflicting interests we find within society. And even if the scheme in the game argument was to be put out there, sometimes investors have scheme in multiple games and sometimes you want some games to lose for other games to win. So, scheme in the game is not compatible when there's conflict of interest. An interesting case this year is in the governance of Aragorn on one of the AGPs. This is a timeline where on the x-axis you see the different votes of people staking tokens on the AGP and at the last minute a whale appeared and that single whale pretty much took control of the entire election. None of the other votes became relevant because the whale simply waited until the last moment to decide how he, she or he would allocate its tokens and bring the election in its own interest. So, proof of stake can work for certain aspects but it's at the end of the day a plutocratic way of governance. So, here's the question that we're facing and I will make a disclaimer right now. I'm more confused than ever about this problem, about trying to figure out proof of human, which is what we have identified after five years of doing work at Democracy Earth as the core challenge to sort out in order to understand how we can do democracy over the internet. Is proof of human possible? I'm no more confused than ever but I'm convinced that if we get this we will get all of these gaps that are that have tremendous potential. If we sort out proof of human we will get democracy, universal basic income, world-level credit, better alternatives to KYC, fair air drops, even luxury communism, who knows. A lot can happen if we can do proof of human. I do know, I've been involved with some organizations, the Partido de la Red in Argentina, which is a party I had the incredible experience to found eight years ago, the Partido Digital of Uruguay, which is exactly like the Partido de la Red in Buenos Aires and they are running for the presidential election this year or Andrew Yang who is trying to make UBI. This is the demand side. These are the funds that will need a technology like proof of human in order to deliver on some of their promises and in order to take blockchain-based technology beyond the means of finance and start building technologies that can also bring solutions to society. But here's the principle that we must abide with. Here's a very important consideration that I think it's great that Edward Snowden during Web 3 in Berlin this year he said this and I think it was definitely the highlight of that extraordinary conference he gave in the summer. We don't need to verify the identity, we need to verify the right to use certain knowledge and this is a very important distinction because if we put our identity on chain in a mutable leisure for there to zero forever and if things happen and there's a totalitarian regime then that totalitarian regime can abuse that information in very dangerous ways. So when we are working with an entity we're working with a very sensitive piece of information. But we need to prepare ourselves for the future. We need to design systems that can help us understand in what context in a world where computation will be far more abundant than it is today which is far more abundant than it was yesterday how will computation look in that world and how in that world we can create systems that we are sure that we will not be taken away by AIs and all kinds of machines and also we must avoid recreating all over again Facebook or the Chinese Communist Party which are the two largest threats to privacy and our identities in the world right now. When the web began 25 years ago the web was also this dream of free information of connecting humanity. No one envisioned during the 90s even though there was a lot of people trying to make money but no one envisioned that something like Facebook would come out of and you know Facebook started breaking democracies far way before Donald Trump. They started breaking democracies actually during the Obama election. Facebook was critical for the election of Obama. I guess society was not pissed off because after all it was the Americans using Facebook to elect an American president and when the Russians are using Facebook to elect an American president or any foreign powers using destruction force to elect an authority over another country then the very concept of the nation state starts blurring. The very concept of non-domestic intervention in each other's affairs is no longer there so we are in a very new political reality and we must prepare ourselves for a world of abundant computation. Now abundant computation is showing us something that is very scary and is that the cheaper information gets the more truth becomes manipulated and I think that an extraordinary example of this is deep text. Deep text all of these pictures all of these guys that you see here don't exist they have been generated by what is called a generative adversarial network which is two machine learning algorithms or two neural networks that reinforce each other and learn from each other and once the network is able to understand the properties of human faces it can I guess the HGVs in the room. I don't mind I can talk loud we're among friends here there might be some spies but that's okay so the the weird thing about this technique of guns is that usually in technology there's this thing called the uncanny valley and the uncanny valley in CGI computer graphics the uncanny valley is that moment where you can the thing tries to be so realistic that you see it start seeing the glitches rather than the realistic attempts of the graphic or the 3d animation that you're looking at. In these pictures there's no more uncanny valley it's extremely hard to tell if these pictures have been generated or not by an algorithm there are some tweaks that you can look very closely that might tell you that this has been generated by an algorithm but otherwise we have to prepare for a world where this can be done with any face in video format and also with our voices and the better guns get the weirder reality gets. Now this is not necessarily all bad this is this actually can be weaponized to break existing political reality. An interesting example on deepfakes is the Andrew Yang campaign which is in Hassa Dao called the Yang Dao which I would encourage you to check it out and become a member you have to pay 40 die to put to become a member of the Yang Dao. One of the first experiments that came out of that is doing deepfake videos about Andrew Yang. I think this is brilliant because you know in a McLuhan as McLuhan said the medium is the message Andrew Yang is this guy talking about universal basic income because artificial intelligence is going to take all our jobs and we get to communicate this displaying exactly what the power of your artificial intelligence is doing with reality itself. So the medium is the message let's use them to break existing political reality but let's be aware that this can be used this is the space the world that we're going to be living in the next couple of years. Another big problem for decentralized identity or very very fine humans on chain is civils. Civils that might impersonate multiple identities usually a technique to prevent this is using reputation algorithms. Page rank a very well-known reputation algorithm that gave us Google but the problem with reputation algorithms is that they are really in mathematical terms algorithms of centrality they identify they start making more notes more central than other notes so it starts centralizing all over again when what we want to achieve is a decentralized protocol. Then there's how we store identities or how we index these identities if we narrow down identity to a one-dimensional piece of information whether it's a username or an address or a any kind of one-dimensional identifier whoever has no knowledge of that the list of addresses can start exploiting this information in an Orwellian way so maybe we have to also think about how we store the information related to identity or how we refer to identity in digital space. So because I'm very confused what I thought I'm going to do here at Decon is just show you everything that I've been looking around that's happening that's trying to do a proof-of-human approach and these are all prototypes or very interesting demos and experiments that are trying to take different angles of how we verify humans over decentralized networks. So a very very nice experiment is humanity now humanity now is a token curated registry you stake tokens and you nominate candidates and these get approved or disapproved or you can challenge existing candidates by using a simple token economy humanity now is based on Twitter so you evaluate the candidates by looking at their Twitter profiles and if their Twitter profile looks like it's a unique human then you can vote in favor of that profile. It has some bugs I was able to generate five identities with it simply changing the username of the of my Twitter account but it is a first step it is a nice first step where we can start playing well how can TCRs come with this or not and I encourage you to check out the humanity now and play with it yourself it's already live on the main end. An evolution of this approach and this is still under the wraps I think it's you know it's still being worked by the team of Kledos we at Democracy Arts we are collaborating with them is to do a web of trust using TCR with video proofs. The interesting thing about Kledos is that it introduces an element which I think is very important for evaluating these proofs which is randomness. If you know if you look at PageRank in PageRank you will end up having validators that are that gain more reputation and other validators and as you gain more reputation that means that maybe in the future you can bribe those highly reputable validators and those highly reputable validators can corrupt the system so we need to do a system where random randomness is an important element and the jurors or those who are evaluating the proofs you know cannot be predicted because they are at the consequence of sortition which is one of the properties of the original Greek democracy sortition and Kledos is definitely the one technology in the space that is really playing with randomness and electing jurors to evaluate evidence in very interesting ways and they are you know working in the direction of helping figure out how to do proof of human. Another approach which I think is one of the most approaches in this space is doing synchronous tutoring tests. This is a project from Russia called IDINA and what IDINA has identified is tuning tests that are machine learning resistant or that are very hard for computers to do but very easy for humans to do and here is a very simple example they call these flips so you have two flips the same four images on each strip and in one of the images the correct is the right order and the order is the is the is correct and in the other image the order is not correct so you have to decide which one is right usually you can do this right now yourselves usually humans get this right 95 percent of the times over 90 percent of the times computers get this right 70 percent of the times. Why is that? Because we humans we have the cultural background to understand what might be the narrative or the story of the cat and the flowers and what might be going on there computers might be able to recognize some of the patterns inside the pictures but computers don't have the cultural background to understand how these pictures go go together so this is a very interesting exercise and this has led to the guys of IDINA to be able to actually research and here's another one if you want to do it to actually research machine learning resistant tuning tests and they have found out that these types of tests they need to have certain properties to be machine learning resistant one is these problems need to belong to the what is considered AI heart problems AI heart problems are problems that do not consist of pattern recognition anything that is based on pattern recognition can eventually be exploited with an algorithm these are problems that require abstract thought or some kind of insight that you know it's harder for a machine to grasp also this means that these problems must not be generated by an algorithm because anything that is generated by an algorithm can be reverse engineered so these need to be problems that are created by humans and not by algorithms so what IDINA does what IDINA does is they put these tuning tests in the network and participants of the network have to solve these tuning tests at the same time so this is how they prevent civil attacks they have a party where let's say actually the next one is in two days you can go to the IDINA website thanks you're not good at HGP I guess for the FBI so you get people solving a problem that is easy for humans heart for machines at the same time because this tuning test party solving problem party is happening in simultaneous in a in a synchronous event you might have a genius that is able to solve three or four or five of these super fast but that's why it's not exactly a proof of human protocol more of a proof of person good protocol you might get some signals but because it's the resolution of these problems happens at the same time it prevents the civil attack problem assuming that these tuning tests are still hard for machines to to figure out I think that we need to look deeper into this type of tuning test that in there there can be more creativity on how we think about this but this definitely is a very interesting approach to this problem another approach which is still theoretical but I'm going to show later a practical implementation of some of these ideas is you know researching your social graph there's a very good paper by Glenn Weil identity as a social intersection consists on evaluating or your social graph assuming that identity is a consequence of the different social intersections or the different groups we are a part of and it's interesting because for example usually in cities a lot of people will belong to a lot of groups so you will have many many narrow intersections and then each one of these intersections will describe a part of your identity whereas in rural areas usually a lot of people belong to the same group so you have populated few populated intersections whereas in cities you have many intersections with few people in them and that's the difference between living in a city and living in the countryside intersectional identity is a pre-formal way of identity and the claim of this paper is that we can formalize some aspects of this identity model and start placing identity in or creating even a new political subject that is not directly linked to the individual but it's also linked to its communities or its multiple communities which is an interesting approach I would recommend checking out this paper there's an implementation of some of these ideas on this protocol called Bright ID Bright ID you have a score which begins with zero and you start making handshakes with trusted contacts and if those contacts belong to a group and you 50 percent of the group you have already established a connection with and you become a member of that group that will feed your score your civil rank score and Bright ID is an implementation of graph analysis and through graph analysis establishes a score that tries to estimate your your you know the likeness of you being a unique human so they have a working version on then there's you know something that we ask ourselves is you know can we actually piggyback piggyback a government infrastructure what what the piece of information you know provided by governments which have already done a lot of the proof of work to determine you know that you're a unique human in a way that we don't reveal information about the government so maybe a technique is you know showing that you own a government issued ID and you take the typical picture you know KYC of your face and and showing your government issued ID but with a technology that is able to automatically blur from that government ID any bit of personal information that you know the same technology that can automatically blur bits of information in your government ID is the same technology that can actually paste a new information into that into that government ID what we want to verify is you know not necessarily who you are or what specific information that is personal about you in that ID what we want to verify is that you hold a government issued value ID we don't care about your name or username or your surname or your address or anything like that we only care that you are a legitimate holder of a government issued ID but you know this technology can also be you know used for other other purposes the other is thinking with hardware not not only thinking about this with software uh using tracking devices that never ask for any kind of personal information you know technology like this thing that I have here that is constantly tracking my movement around the planet or my movement around the streets I've got you know kind of estimate that I'm actually moving around this world and so make sure that you know either I'm I'm a living being uh might be a way of trying to to use that bit of information then to mine some kind of proof of humanity assuming that you know this this hardware is intemperable which you know it's a big assumption um but you know these are some of the ideas of what's happening with with proof of human so looking at all this looking at everything that's happening and all of these implementations have trade-offs some are easier than others some are cheaper than others some are more specific we figured well let's not just focus on one specific approach let's try to find out how you know how we can work with all of these different approaches and come up with a common denominator or a common score that can help us weight these different ways of evaluating identities so an idea that has been injected to me uh by Albert Wenger actually is going to the realm of thinking about identity in a probabilistic way uh not thinking about the identity either on a you know you're a human or not a human but thinking an identity or humanity as a spectrum and the chances of the likelihood that you're a human and based on different certificates or different scores so um the problem with you know basically why identity is very hard is because it's this thing that lives in these two universes there are no one is the subjective universe that consists mostly of our attention and the other is the objective universe uh and identity is something that lives in between this so considering the proof of human things that I showed you before we can see that in the objective side of the spectrum basically we're doing claims as strings of bits any string of bits uh we have pictures fingerprint video uh you know these these strings of bits go from lower entropy to high entropy depending on the complex they are and we have different verification mechanisms that rely on computing some rely more on computing and the others rely more on uh on attention um like API timestamps intersections machine learning tuning tests and TCRs um so can we weigh all this in a in a score that can help us make sense out of this uh something that you know given any Ethereum address uh I have some kind of a risk that will tell me okay this is this address is likely to be 92 percent human and then people can write small contracts using that input uh in order to evaluate a credit or or evaluate any kind of human-based service uh but the thing that we need to ask is if we are doing an algorithm like this is who watches the watchman who is determining how this algorithm works so this algorithm must not be simply a blind machine uh must be something that can take into consideration uh you know the how we want to be observed how we we want to decide how we want to be observed how we you know our identity is the source of something that comes from a process that is legitimate and so to understand legitimacy a technique that you know it's interesting that we have been researching a lot of democracy here is quadratic voting and uh how many of here are familiar with quadratic voting okay I would encourage you to read about it quadratic voting basically you can vote directly on an issue by allocating votes but the more votes you put on the same issue the cost of that increases not linearly but quadratically so if you put two votes on something it would cost you four tokens if you put three votes it would cost you nine tokens and this means that with quadratic voting you are not only measuring the preferences of the voters you're also measuring the intensity of those preferences and a consequence of quadratic voting is that it tends to generate normal distributions of preferences in tests we have done using Likert scales Likert scale ballots this typical service where you have strongly disagrees strongly agree and values in between when it's without quadratic voting people tend to go to the extremes to strongly disagree strongly agree but when it's with quadratic voting we have these Gaussian uh belts these these normal distributions and people tend to go closer to the center so there's less polarization and the data is has a much better organic distribution which helps to which is better data actually for algorithms for machine learning algorithms we have done an implementation in quadratic voting the first implementation in the US for the state of Colorado in the United States and we got exactly that we got an organic distribution of 41 state legislators to decide over a hundred and seven bills and we had an organic distribution of the of how to write these bills throughout time so when we compare this with participatory budgeting with participatory budgeting which is a linear distribution of tokens or votes with participatory budgeting legislators found that 50 60 percent of the bills had the same amount of tokens so they were not able to prioritize among the long tail of bills with quadratic voting we were able to prioritize along a very long tail of bills so we're working on bringing some of these ideas together we're building a democracy now you know i'm a member of Molotow so i'm going to put my shares of Molotow subject to this democracy now experiments and we you know there's more to this like zero knowledge proofs and years in 725 identity standard come look for me on twitter and add sandy city on twitter and there's a group of humanity meetup if you want to come it's tomorrow at 6 p.m take a picture of that and we'll we'll talk more about this thank you very much