 I'm very, very happy to be with you all. I have been teaching in a library studies department for nearly 15 years. You can see a little bit of that gray creepin' up right here. I wasn't there, asked a couple of my former students when I first started. I would like to ask you all to engage with me via Twitter if you're interested or, you know, able. Ramesh Media is my Twitter handle, and you can really engage with a lot of what I'm up to, especially around this new book that way. You can, you know, ask me thoughts, share thoughts, questions, et cetera. And I try to really engage with everybody I can that way increasingly these days. So what I really want to do today is tease out some of the big ideas in my new book. So as was alluded to, this is the cover of the new book, as was alluded to, I'm really an interdisciplinary scholar. I'm trained in engineering and artificial intelligence. I was actually an artificial intelligence developer back in the day, but all the while I was always very intimately and personally concerned with questions of social justice, equity, and diversity in a deeper sense. Not sort of diversity as a form of passive inclusion, but as a form of radical multiplicity, right? Meaning how do we think about an informational world, whether it's libraries or technological systems, that really, truly, truly, truly empower the different voices of stakeholders, of communities, of those who are almost always left flattens and invisible and silent when it comes to systems of techno-social power and techno-political power. So this book is my attempt to grapple with all of these issues in relation to the internet and new technology. Really, it's a survey. The book's about to come out in four weeks. I'll be doing a bunch of MSNBC and NPR hits around it, and also, of course, more progressive media as well. But the book is really a survey of a bunch of timely themes that I'm going to give you a teaser around right now. So I'm looking at economic justice, labor, the future of labor, the future of worker power, artificial intelligence, and all of its issues around bias and inclusion and exclusion. I'm looking at issues around data and democracy, political and social movements, their relationship to technology. And perhaps most importantly, I'm looking at issues of diverse cultures, indigenous peoples, people of color communities, queer communities, subaltern communities, for lack of a better term, and their relationship to information technologies as well. So it's a kind of broad survey. It's written for a general public. I would love to do outreach on this book with your stakeholders, with those that you serve, with your patrons. For me, it's not just about sharing the book in fancy circles. It's about sharing it with the broader public and with folks like yourselves. You all are representatives between scholars like myself and people, really. And I try to do my best to close that gap myself. I do a lot of work in that image. But it's all of you, right? Having trained and worked with librarians for many years, I know how incredibly important your profession is, especially at this time. And I'm going to try to make the point about why libraries are so important and librarians are so important in an era where we are blindly trusting corporate-provided, surveillance-driven technology that is designed to manipulate, addict, and maintain our attention. So I'm not keeping it mellow here with those issues, right? I'm trying to keep it real here. And I come out from those ranks, and I'm also not trying to single anybody out in particular here. I'm trying to call stuff out to talk about where we can go. And the book actually presents a bunch of pathways where we can humanize technology, where we can ensure that technologies serve all our interests. They can serve business interests, but not in a zero-sum fashion, not at the cost of working people, not at the cost of the 99%, not at the cost of a global public that is increasingly colliding with digital technologies and the internet in all its forms. So quite an ambitious project that is coming out here and coming out in four weeks. So please support the book. Thank you for sharing the book with your audiences and those you work with and so on. And the book is super broad, so you can kind of dive into whichever areas of the book you're most interested in. I also in the book had an opportunity to speak with a number of very important, or at least famous, and influential public figures. Senator Elizabeth Warren, I did a brief interview with for this book. I actually drove to Tucson, Arizona to interview Noam Chomsky about technology and kind of ideas around manufacturing consent, which is now 30 plus years old, if I'm not mistaken. And really, I actually met him on the 30th anniversary of manufacturing consent and I was like, what's up with this in relation to the internet and new technology? I spoke with folks like Lawrence Lessig, right? Creative Commons, big open-source free software advocate, David Axelrod. Ro Khanna, our congressman, South Asian brother from Santa Clara area and now the national co-chair of Bernie's campaign, Eric Holder, a former attorney general and a bunch of very neoliberal figures as well. Vicente Fox, former president of Mexico, David Axelrod, Obama's data guy. So I tried to see what all these folks are saying, people leading in unions. I tried to talk to people across the gamut, look at scholarship that I was doing myself, look at scholarship that was being done by a bunch of really fantastic colleagues of mine and very importantly, really engage with excellent journalism that's being done around issues of technology and its relationship to people and our world. And one of the places where I start this book and really a lot of my earlier work is really getting to the basics of what the internet is, right? The internet is often spoken about as a cloud. It used to be spoken about, remember when it was spoken about as a library, an infinite library, almost in this Jorge Luis Borges type of manner. It used to be called a supermarket, Al Gore called it an information superhighway. We ascribe various metaphors to describe the internet, but in reality it's a set of material territorialized infrastructures. This specifically is the internet, at least one major way by which people access the internet via its global fiber optic cables, okay? And if you look at this map, I don't know how well you all can see it. You can see the lines, can you all see the lines between various continents? It's striking for what is connected and therefore what is not connected, right? So if you look specifically at the two major continents of the global South, Africa and South America, you see a paltry two, three fiber optic cables connecting those continents, right? Which is pretty interesting given that Africa is the youngest population in the world, the fastest growing population in the world, and the fastest uptick of internet access right now in the world. There's a lot going on in Africa yet that's not reflected by the very infrastructures that actually form the foundation of the internet itself. And so I think that's really important to understand how infrastructures of all forms, like electricity, like where your cell phone towers are located, like even flights, you know, where you fly between and between, all relate to economic and political power, right? You can see so many cables, I don't know if you all see this, connecting the east coast of the United States with western Europe, right? Like London and Paris. You can see a number of cables connecting the west coast of the United States with places like Shanghai, Beijing, Tokyo, Hong Kong, et cetera, right? These are financial relationships. These are economic relationships. It's very difficult to catch a flight from the continent of Africa directly to the continent of South America. You often have to be routed, right? So these contours, these geometries, these patterns actually follow relationships of inequality, of asymmetry. And I think that's important because we can understand what connectivity makes possible when you look at a country like Kenya, which I'll speak about a little bit later, a couple chapters taken from my field work in Kenya. There's a lot of global stuff in this book as well. And a lot of my earlier work was very global in its scope. And the fact that there's a fiber optic landing cable point in Kenya has actually translated into quite a bit in terms of a DIY entrepreneurial and also even humanitarian ecosystem associated with technology. So connectivity means a lot. It's not sufficient, but it might be necessary for various forms of digital action and growth. So we have reached a profound moment, and it's like right around the corner here in San Francisco where the hub of a lot of this has gone down, where the explosion of democratized content on the Internet gave way to us turning to intermediaries or platforms, and those platforms became the places we go to make sense of all the stuff that was on the Internet, right? We all know what we're talking about here, Google, Facebook, Uber, etc. But what we didn't quite realize is those platforms are not neutral, perhaps somewhat naively. They follow corporate logic, investor logic, executive logic, logic of capital valuation, right? And what you see in that process is by turning to those specific platforms, right? Those platforms themselves, of course, are profiting off of those forms of decentralized exchange or supposedly decentralized exchange, right? Uber, for example, calls itself a technology company. Yeah, it's a technology company, but it's also the largest taxi company in the history of the world, right? It's astonishing actually. It's in a fairly rural part of Tanzania, about an hour and a half outside of Dar es Salaam, the capital, and there's a three-wheeler, a tuk-tuk. In India, we call them auto rickshaws. And you can actually call or contact that tuk-tuk using Uber. It's enveloped within the Uber data network, right? And so I've also witnessed multiple Uber strikes in countries like Uganda and Kenya. What happened is these folks in the middle minimized their costs by creating almost nothing. I don't mean that as a negative, but I'm just kind of keeping it real. Hiring almost nobody, you know, in the case of Uber, as we know, independent contractors, finally that might be changing now, only in California. And we don't yet know all the details, right? So what has happened is the costs associated with the technological intermediary have gone down all the while all of our exchanges with one another, so-called peer-to-peer exchanges, are being monetized in the middle, right? And so that has created a great amount of valuation because of the revenues that can be generated through the process. But more importantly, it's the access to all of that intimate data, right? And that data offers a great amount of speculated value. You can even think about David Harvey's work or work by David Graber on debt and credit to kind of think about these issues. Kind of fairly academic text but really excellent and insightful when it comes to these issues. So the access to that data, the collection of that data, the ability to monetize and reroute and serve that data in different forms to different users is a great form of power and that is speculated into a great amount of value, right? For example, there is some evidence that shows that Uber, for example, is not actually making much money, if any at all. Yet it's worth, I forgot the latest number, well over $100 billion, right? So is that possibly because the gig work, the gig data, the gig economy data, is being used to put Uber in a leg up when it comes to new forms of industry, like automated vehicles. So you have to always understand the way a technology company functions in my mind. A lot of friends who work in tech companies, I'm a former engineer from Stanford, et cetera, right? So you have to understand it not simply by the status quo but all the different kinds of pathways that it can lead to. This is what I mean by speculated value. And we can see that all over the place. And if you look at Chinese examples, they're no different necessarily. You see, for example, that's Alibaba, but we can obviously easily replace that with Amazon as well. Just very briefly, you can see, for example, there's two concentrated places of power when it comes to our global technology revolution, if you will. Silicon Valley slash Seattle, right? And Eastern China, right? And so Tencent, which owns WeChat, which some of you might have used, an integrated technology platform is actually worth more as of last year than Facebook. That's because of its control of a global gaming market as well and mobile markets as well. And you could actually see the different contours by which technologies expand around the world as related to the doctrines not only of the companies themselves but in the Chinese case, the state. Right? Xi Jinping. Yesterday there was just a celebration, right? In Tiananmen Square. And Xi Jinping has actually pushed forward the One Belt, One Road initiative or the New Silk Road initiative, which is a combination of AI-type systems, global expansionist approaches in terms of gathering energy, for example, mines in the Congo and so on. I've witnessed some of this firsthand. And also internet of things and automated systems as well. So you can actually see in this case how the global aspirations of Chinese internet capitalism and Chinese internet governance are ones that are actually following a territorial logic as well, meaning they're expanding based on places and spaces, right? And particular types of political and economic relationships. And I can go into all of this if you're curious. I'm just giving you a lot of seductive and hopefully tantalizing teasers here. Any tweets? Yeah? So this is the Microsoft AI Twitter chatbot. I'm just using it as an example. I'm not trying to call anyone out. That turned genocidal, racist, homophobic, sexist, xenophobic and started hashtagging MAGA very quickly. I think within just a few days, MAGA and make America good. All right. So what happened with this, right? So this was an AI system that was supposed to just interact with folks on Twitter, right? But it quickly radicalized in a particular way, right? And why is it that that occurred? Well, we don't know the exact answer when it comes to te tweets because we don't have an exposure to how it was built, the data sets that the technology learned from and sort of the ways in which that technology interacted with others to learn and adapt to the so-called intelligent. But what we do know is that te tweets is reflective of a common issue when it comes to artificial intelligence. As I mentioned earlier, I was an artificial intelligence developer back in the day. This is now dating me 15 to 20 years ago, all right? And I went to Stanford and MIT mainly as an engineer that was always at the core a humanist as a person, I believe, and a social scientist in people and communities and users of technology. But what we realized at that time when I was doing AI, everybody laughed, right? They were like, why are you trying to build a robot thing? Why are you so obsessed with the Turing test? And those are all good questions. But if you notice now, everybody is speaking about AI. In fact, several folks at Google that I interviewed actually referred to Google as an AI company on the record interviews I got for this book. That's really interesting. What happens here? First of all, we built exponentially faster processing machines. One, two, is we actually built the capacity to store exponentially more data, right? If you think about it these days, you just put anything up online and you're just like, oh, it'll just be stored somewhere, right? There were exponential decreases in cost per processing and data storage, right? Think about how many of you have like a terabyte drive, external hard drive? Were you able to have one 10 years ago? Can you even imagine what that would have cost? Right? So these are technological revolutionary breakthroughs and they're wonderful, right? However, what that has also paved the way for is that data being all accessible. So if you have more data accessible you can compute it faster and here's the key point. We are emitting data all the time by these phones that we have in our pockets, right? And increasingly through internet of things, 5G applications, embedded smart city applications, potentially the embedding of technologies in our bodies I'm just kind of going there a little bit. So AI is a real deal, right? But the question is whose AI? How are those AI systems built? Who do they serve? Who is included? Who has power? Who doesn't have power? Are we supposed to blindly trust them much like we trust many algorithms today that we experience online? Well, what we found through a series of different examples and I give tons of them from across the world in this book is that AI systems of course tend to reflect the biases and I don't mean like, you know mean people or bad people but the value systems, the ways their engineers build them, right? It's kind of obvious but it's really important. When I write a book I write it obviously based on who I am, right? If I create a painting I paint based on who I am, right? Similarly when I write software or express my own sort of logical framework or even how I was socialized professionally, intellectually etc. I'm creating it based on who I am, right? So we do of course know and this is a known fact that engineers tend to be disproportionately in our country and certainly in China tend to be disproportionately male white and Asian and that's kind of like the deal, right? And upper middle class in many cases, right? How else can you afford to live around here, right? So I'm from here so I'm just joking a little bit and so what we saw happen is for example St. Petersburg in Russia, a company that was just trying to be, you know, cute and create stuff for the internet called FaceApp turned Obama's face younger and whiter, right? Why did that happen? They apologized for it. It's not because they necessarily inherently thought being white is better but it's because they were feeding that technological system data sets based on beauty or concepts of beauty that are local, if you will to Russia. There's a lot of white looking folks in Russia. Not as diverse as here in San Francisco, right? So we build systems that are conditioned based on how we create or sculpt code, right? The data sets we feed to those systems and then sort of what we optimize those systems for, and here's a key point. If we optimize those systems to maintain people's attention then you're going to feed people content that gets our attention, that releases that dopamine in our heads. My colleague Tim Wu published a book called The Attention Merchants to describe some of the big tech companies right now. So these are the kinds of issues that we're seeing pop up, right? And to their credit a bunch of folks in the tech world are trying to deal with these issues. What they're doing and where it goes remains to be seen but there are attempts to engage with these issues because it's frankly embarrassing, right? So Google, for example, has set up a massive AI lab. I interviewed the folks running it in Accra, in Ghana. They're in the book too. Why is that? Because searches you know it's an embarrassing thing for Google. Searches for gorillas if I'm not mistaken resulted in images of black people. So temporarily the search for gorillas was suspended. That was their way of dealing with the issue rather than the underlying engine that's producing these forms of inequality and reinforcing if not normalizing discrimination. The issue with technology is people again and again and again tend to treat it as morally neutral. That's true with science as well. Rather than culturally or socially conditioned or part of a political economy. So my friend Joy Bolawani who I also spoke to for this book made this like cool poem like visual poem called AI Aint I a Woman and she gives examples of facial recognition systems which thankfully have been at least temporarily suspended if not banned here in San Francisco are showing again and again how powerful and wonderful and famous black women like Serena Williams, Michelle Obama she gives examples here are all misidentified by facial recognitions whether they're Chinese or American to be men or misidentified. So I recommend looking at this great teaching tool for your students or patrons or so on. And we see this again and again and again. We know that Amazon for example despite soap opera spats between Jeff Bezos and President Donald Trump has been selling its facial recognition technologies to ICE and oops sorry that got me mad and that and that other folks are also trying to do the same right? I mean it's a business deal right? And why is that being used? Well a lot of ICE agents will go into communities independent of whether you support that or not and they have body cameras and those body cameras are connected to cloud computing facial recognition algorithmic systems that are then identifying these folks as being in some database or not and they are misidentifying people across the board. Again why? For the same reasons that I just made that I just described earlier with the other examples. We also saw this occur with members of Congress. I remember posting this on like Twitter or Facebook and friends were cracking up when I did that. Well of course it's Congress. There are a lot of folks that we don't like etc. But it turned out that a very high percentage again of those people who are misidentified as suspected criminals from Congress were members of the Congressional Black Caucus. So again and again we see this pattern right? So it's high time and we got to do it right now intervene in all of these issues and all of you as librarians have the opportunity to advocate push for if you will a human centric, people centric alternative to all of these issues. I mean honestly you all as librarians and I know I rant about this to my students all the time and they kind of laugh at me are really one of our big hopes when it comes to these issues. You know and I hear from I hear from people all the time. Why do we want to use the library? We have Google. You know no offense. And well, because what are librarians about right? Especially public librarians they are about serving the public but not the public in some bland homogenized Habermasian way but publics, multiplicities of publics. Diversities in their truce form openness, incommensurability these are like fancy terms I use in my writing. I can unlock them. So we are using algorithmic systems of course not just in the kind of political arena I wanted to start by kind of jarring you a little but also we are embedding AI systems as part of automation technologies. So automated technologies are ones that are going to replace the economist estimates about 47% incredible of existing jobs within the next 20 there are different estimates 20 to 30 years or so. And we have found that for example AI systems, automated systems for human resources work that are going to replace human resources employee work are finding these biases again. In this particular case this is based on research that was published in the journal science. The Guardian and others reported on it. I'm very grateful for the work of my journalist colleagues so they can share this with me and get people like myself to think about these issues and they were and what this system did is called using algorithm called word embedding and what word embedding does is it's a text mining algorithm it scans CV's and makes deductions based on the text mining analysis of that CV the corpus of data that's within the CV itself on the suitability of that particular CV for the job at hand. And so what we found again and again and again with this particular experiment you welcome to look it up a little bit is that it was choosing men over women for science and engineering jobs. You all know how it goes with job hiring. The hardest part is to make that first cut from like 200 to like 10 interviews. Once you get to the final 10 you can charm them in person or on the phone. But once you're 200 and you're just throwing a CV out there who the heck knows what's going on. Well these systems are supposed to replace that work of humans doing that sorting out but we're finding out again that they're reinforcing the biases of that C science and engineering as something that men are better at and we know that that's a bias that exists in society not just because of the representation of who in the status quo is in those fields but we also even heard it. I heard it first hand when I was a PhD student at Harvard by Lawrence Summers. You all remember that? This dates me also. This was like in 2004, 2003, 2004 if I'm not mistaken. I was just like in the end of my PhD and he basically made a statement saying I'm not as good at science and technology. I mean I'm paraphrasing here. And it created a firestorm on campus. So why is that happening? Well it's because the machine learning systems again are learning from normalized inequality. And we know that we aspire, we have values that we aspire toward but the aspirational aspects of ourselves as a society we got to get there. The aspirational aspects of ourselves as a society are not the ones that guide many technologies. Technologies often learn from current historical and current patterns. What else do they learn from? They're not trained necessarily unless we choose to build them in that image which is what I'm arguing for in the image of aspiration in the image of what we aspire toward as a society, as a world, as a people. And that's an issue because of what I was just speaking about because this spells over to the experience of a given individual and their treatment in some sort of institutional or professional sense but also economically, right? I think you all know these numbers, right? I'm just repeating Senator Sanders lines from his rallies but we hear from Senator Warren as well. Three people with the equivalent wealth in our country to about 195 million. Eight people or so with equivalent wealth to about 3.9 billion people in our world and that is not the fault of tech companies. I want to make that point. That's not the fault of the internet but the internet could be an amplifier for these problems, right? There are a lot of reasons why we got in this state but what we don't want is technologies to contribute to greater division and that's a bad thing for everybody concerned, right? Because if tech companies are building their value based on our value as users, right? And really the products also of their algorithmic systems and we're worth nothing, what's that going to get you, right? So I'm part of an intelligence squared, I don't know if any of you have ever heard of this, intelligence squared debate in New York City in a couple weeks so we'll see how these arguments do when I'm debating some hardcore libertarians in American Enterprise Institute. So if you actually look at the macroeconomic data that we're seeing, right? To say the least. This is the first generation, this is Raj Chetty just down the road here at Stanford, economist who's been able to identify that for the first time the youngest generation in our country is going to make less than its parents for the first time in the history of the United States. If you look at overall tax records and you kind of normalize them by generation, we also know that the life expectancy in our country is decreasing so these are issues that are not again about technology themselves but they speak to overall dynamics within which we need to intervene technologically or otherwise. Alright, so you know I had this slide up back when he wasn't quite known, when the yang gang was not quite quite as big a deal but there's some significant material in my book looking at the universal basic income or what Andrew Yang calls the freedom dividend and actually explaining how that this is not a new idea. This is one attempt, some might say a patchwork attempt to actually deal with these inequality issues right and this whole campaign is kind of built around that. I mean I've read some hilarious stuff on you know what do we do about health care? 1,000 bucks a month right? But no, to give Mr. Yang some credit he's very intelligently pointing out acknowledging these issues of how in the gig economy are expanding various forms of economic inequality and he's proposing this as an approach but this idea of a dividend or so-called universal basic income is not new, it's existed for decades. The Cherokee used a dividend program Dolly Parton used a dividend program I give examples like this in the book to help folks who are dealing with issues of climate change and fires. Alaska has used a dividend program for decades but of course where are those dividends coming from? Oil revenues, right? So the point of trying to think about various ways to redistribute income or at least you know support people who are further troubled or further vilified by an extractive system are ideas that we can think about. I interviewed Michael Tubbs the mayor of Stockton for this book and it was on a panel with Michael like in 2011 when I used to work in Egypt writing about technology in the Arab Spring that's why I used to write for El Jazeera English and that's a whole other area that I'm really excited about and Michael has been experimenting with Chris Hughes, former founder of Facebook and some other folks with universal basic income idea. We do know that the data show that when you give people working people a little more money it's that it's used for socially empowering purposes for lack of education, health care and so on so the data does show that. The real question is whether this is the way to deal with the problem, right? Whether a system that is extractive that is overall designed or engineered to likely produce more inequality is compensated for by a monthly or I'm sorry yearly or monthly whatever he wants to say like dividend, right? What about does that come at the cost of health care? Does that come at the cost of the social contract? That's the word those are the words that Elizabeth Warren used when I interviewed her that work used to engender a social contract you've heard her say it on the campaign trail too, right? Like when my father was laid off my mother worked at Sears she had a minimum wage job it took care of my family, paid for education paid for the mortgage, paid for health care so we got to figure out what we do and she really acknowledges this a profound inflection point of transition when it comes to these issues so some ways people are dealing with it there's a substantial amount of this new book about economic issues, future of work I interviewed a lot of folks in the union world organizers as well I just tried to call together lots of different voices and interpret and analyze and think about them and tried to be sort of really open to what I was learning from people. This was a New York history showing how in Sweden miners are actually being replaced by robotic systems that sounds good in some ways never mind what one might think about mining I don't like mining but that's good because mining work is very, very dangerous and precarious and unhealthy so they're being replaced by robotic systems but instead of saying that's it, you're out of a job the miners are now operating the robotic systems in a safe control room and who better to know how to work with the mines than miners so that's just a very simple example by which we can think about new forms of work worker reskilling, what is the work of the future how do we make sure people have dignified, secure work the social contract, is that the way we want to go here AT&T right here in the United States has been actually pushing similar issues it is notable in this article which is a couple years old that the or else is in the title of the article which is hey get ready and train yourself and we'll give you the classes for the work of the future or else what does the or else mean, you lose a job you lose your job and examples like AT&T unsurprisingly, let's not even talk about AT&T's other issues potentially with the AT&T which I call out in the book but it's an interesting point that they're making and AT&T has been lauded by kind of centrist journalists like Thomas Friedman for being an example of identifying the way forward so we can basically see how these issues are attempting to start to be engaged with and dealt with by a range of different stakeholders but one of the policies that I've been trying to push into some of the progressive campaigns, again I'm showing where I come from here in the Democratic primary is what I call a future of worker justice piece of legislation and I have an op-ed that I'm hoping will run in the LA Times in about two weeks right when this book comes out about these issues which are described in the book as well let's take this a little further, my friend Nathan Schneider who writes for The Guardian sometimes who I interviewed for the book is one of the folks most important folks in the whole so-called platform cooperative movement which is like what if we had an Uber but all the workers for Uber had equity in the Uber in the Uber as a business model what if like everybody connected to the business worker owned businesses or even digital platforms that return some forms of equity so they're not just simply invisibly extractive for all of those implicated by that technology that's another model and those sorts of things exist there are small scale Ubers for example in places like Denver and other parts of the world as well and the platform cooperative movement is an attempt to take the cooperative model and apply it to digital technology but believe it or not there are a lot of cooperatives out there, Ace Hardware is a cooperative I give a bunch of examples that you may not have even known about I didn't know about it for sure until I wrote this book and you know the old school like technologist heroes or at least some of them like Tim Berners-Lee who's seen as a father figure of the web are also concerned about some of these issues and they're trying to think about decentralized platforms so what if we had email but it was really encrypted on every level what if we really kind of made sure that data privacy was a big issue really really really right not saying you own your data saying you own your data is fine Mark Zuckerberg made that point he made it right in front of me at the conference I was at but when I choose to opt out of Facebook services do they still have my data? yes do we have any evidence that they are collecting data of people who've never created Facebook accounts yes so like what do you mean when you say I support regulation I support the idea of data being your own so you have to kind of analytically like kind of engage with these perspectives that are sound good and probably well intentioned in some way and kind of push it a little further and so SOLID is an example of an interesting project that's emerging out of MIT that Tim Berners-Lee has been leading and another big issue is right here in the Bay Area I want to just kind of add this point and then I'm going to shift to global and cultural themes so another big issue is really the idea of diversity in the digital workplace there are a lot of studies that show that diversity in different forms racial gender, age, disability, etc LGBTQ like all of that means a lot when it comes to governance in corporations when it comes to corporate boards why is that? because different opinions, different perspectives often create something emergent that's better than the individual parts themselves and Tracy Chow who is like an early Pinterest engineer who I met at a conference a few months ago I interviewed her for the book has been pushing something called I think it's called Project Include it's right here in Silicon Valley and she's been calling out and saying hey we really need diversity involved in the engineering process in the management process and so on so these are just people that I think are interesting to pay attention to they all kind of fall on different sorts of spectrums in my mind in terms of what they stand for and how far they're willing to push it all these people and hear what they have to say it's kind of why I wrote this book because it was a lot of fun and then it drove me crazy too alright I want to now shift to something of you a bit more imaginative which is really what I love the most so as as was alluded to before I actually am a community based researcher that's where I come from my dissertation was work I did as I said an engineer but my dissertation was me like living and working with Native American communities in the southern United States thinking about connectivity and the design of digital spaces that were owned designed and truly built upon the value systems the ontologies that's a term I used a lot in my earlier writing the value systems of those communities themselves so I've been very interested in this idea of communities in the digital age of different forms taking sovereignty or power over how technologies are designed who they serve, who owns them, etc and one of the most amazing projects that I've had the privilege of visiting and writing about for the last 3-4 years that I have a whole chapter of called mobile power to the people in chapter 22 some reason I remember that in this book is based in the southern Mexican region of Oaxaca and Oaxaca is one of the most biodiverse and linguistically diverse parts of the world, North Americas I can get in details but it's magical like you'll go and I'm going to give a detail you go like pretty high altitude like 6,000 feet high and you'll see subtropical like plant species various types of like magei and cactus type plants and you'll see like pine forests all kind of juxtaposed it's just incredible and not surprisingly the Zapotec, Mixtec and Mihe communities that live in the region have an astonishing diversity of language and cultural traditions and these communities have never been not all of them but many of them who are located in rural locations high up in the middle of cloud forest that rain like crazy where the roads are not really stable etc have never been provided cell phone service why is that because cell phone service is not seen as a communication right it's based on the logic of the cell provider tell cell run by Carlos Slim one of the wealthiest people in the world it's not worth it it's not worth the investment they don't make enough money they're not enough of them and they're in these extremely remote areas right so these folks said we're going to build it ourselves they come out of the community radio movement radio comunitaria huge movement across Latin America that a lot of indigenous peoples have been building up and they were able to legally build a collectively owned cell phone network and now they have dozens of these networks around Oaxaca some are expanding into Puebla and in Chiapas and Guerrero some neighboring areas and they're even trying to push this model into other parts of the world this project is called Telefono Indigina comunitaria or Rizomatica named after the notion of the rhizome the delusion idea for those of you who are interested so this is Osvaldo who's been like a great teacher to me he's really interested in using this network a community owned and built cell phone network to really support orality language digital literacy in a community based way and really the use of this network to allow people in the community also to call relatives here in the United States so they have a local GSM network that they're tethering to a voice over IP network but it's all daisy chaining it's all about infrastructure in this case infrastructure unlike the fiber optic cables is dramatically localized the governance is really community based okay two minutes can I go a little over okay okay alright I'll go fast similar project look this one up called ICTACOP a bunch of Mayan celltile speaking communities have built their own Wi-Fi mesh network if you notice community networks communities building and owning their own digital internet access points but also internets or cell phone networks is increasingly common it's not just in Chiapas in Detroit this is happening right now Detroit community technology project it happens in Red Hook a resilience community network in response to Hurricane Superstorm Sandy these projects are everywhere there's a chapter in the book about community networks and there are also efforts that are more entrepreneurial or business based like a company based out of Nairobi called a brick BRCK and this is sort of one of their advertising points and they are actually what brick is doing is they're actually embedding Wi-Fi technologies that are free for people to use but that can that are sponsored by local community businesses on their moving vehicles because what happens in a place like Kenya which is true in many parts of where you just go on a street corner and you just get on a moving van packed with lots of people and this there's the like the Bob Marley bus that has Wi-Fi on it but there's a lot of people with the nozz right there in the front and what's what is this logic this is an alternative logic of thinking about innovation here's where I'm going to like sort of end which is that's a term that has been hijacked in many ways in our discourse and partly by Silicon Valley and more so right innovation seems like oh like by the newest iPhone great it can scan my retina that's innovation um this is also innovation that I'm showing right here resourcefulness doing more with less right doing more with less it's a there's a certain kind of environmental logic but there's also a logic of we're going to do it because we can because we don't have the resources we don't have this myth of infinite resources or excess resources to do or create whatever we can with this technology right so people are literally taking discarded forms of e-waste things that are left to die and they're rebuilding they're repairing they're recycling it's a whole alternative logic and I want to give you an example of this that I describe in the book this is a group called AB3D Africa born 3D it's in Nairobi and what have they done they're building 3D printers that are a fraction of the cost of US and Chinese 3D printers but are much more resilient and robust because they're designed by local folks they're optimized for the local environment right and they're building it primarily not completely but primarily out of discarded like trash e-waste and to me that's just like mind-blowing right it's a whole alternative logic of engaging with technology itself and look at that how like nice and neat does it look after that right so these so the question of engineering know how technological know how design know how cultural know how all of that we really need to shift and open up spaces for people and communities to exert their own sovereignty their own voices their own power and that's really the major major argument of the book it kind of looks at these issues across the board because technology is not just about a google search or or whatsapp or you know like being liked on Instagram it's also a gateway to digital technologies are the modes by which all forms of our lives are being expressed the vocabulary of the present and the future right think about life insurance now it's just algorithmic personalized life insurance right every vertical if you will in our world is being expressed through these computational technologies that's why it's so so so important that all of us have power and so what I'm arguing for in this book is an opportunity for us all to come together work together and build together and push this digital revolution forward in that image so I think it's time for us to do that right now thank you very much