Before I start, I do wanna say a deep thank you to Cary Anderson, Ruben Langevin, Dan Jones, and Ellen Popko, and without them, this event and many other, is this going in and out? I'll try this, this event and many other events at Berkman wouldn't be able to happen. Okay, so welcome to the honoring all expertise social responsibility and ethics and tech. There's a lot of work going on in the space of ethics, tech, and social responsibility. Many here at Harvard, ranging from what Aiden is doing at the Divinity School, initiatives at the Harvard Kennedy School of Government, research at the Cyber Law Clinic, new case studies being developed today for tech and ethics at the Harvard Business School, computer science and philosophy working together and partnering to embed ethics in computer science curricula led by Barbara Gross and Allison Simmons. There's Nathan outside of Harvard, there's Nathan Matias's work at Princeton Bridging Social Science and Computer Science. There's Casey Feisler, mobilizing people around the world to look at ethics syllabi and data science and computer science, countless codes of ethics initiatives, the Center for Humane Tech, and just right now today, Theresa Tabriz and others are running the R Security Advocate Conference in San Francisco right at this moment to talk, one of the things they're talking about is security policy and ethics. So there's a lot of work going on in the space and it's definitely not a comprehensive list. But today, we're here to talk about the work and research of the ethical tech working group at the Berkman Klein Center at Harvard. Some of us collaborate with some of the other people that I had mentioned earlier as well. We're a group of computer scientists, race and gender scholars, ethnographers, historians, lawyers, artists, social scientists, political scientists who get together week to week to discuss and debate topics of social responsibility and ethics in tech in a very open and supportive environment. We have a deep and profound, we've seen the deep and profound value of different expertise in the room over a period of time, discussing and debating issues from different angles. Where all expertise are equally honored and equally valued and we wanna share with you a little bit of the essence of our group, a group that I have grown to love very dearly over the course of this year at the Berkman Klein Center and then to open discussion with you all as well at the end of this. So this is our agenda for today. We'll do a series of lightning talks and we'll leave about 15 to 30 minutes for discussion at the end of this and Carrie has mentioned that we can go into about 115 if the discussion has us going further. So with that, we'll kick it off with Luke who will tell us more about ethics and codes of ethics. Anything that's really hard to read off is a group of scholars, friends, collaborators and this has been, as Kathy was saying, a fantastic experience for me and I've really learned a lot from it. So my name is Luke Stark. I'm a historian by training currently in the Department of Sociology at Dartmouth College and also a fellow here and this is a talk about hammers. Here's a hammer. Does anyone know what kind of hammer this is? Exactly, it's a ball peen hammer, good. It's also a talk about ethics and digital technology but I often like to perform a kind of simple thought experiment when I'm thinking about new media, new digital technologies. We just replace the technology you're talking about in your mind with the word hammer and see what you're saying sounds like, right? Ethics and hammers, ethics in hammering, okay. And this point isn't to put down or slight ethics or digital technologies or even hammers but to help me remind myself what's novel about digital technologies and what might not be so novel. So our contemporary sense of the word ethics comes from the Greek word ethos. An ethos is a moral, a habit, a character, a disposition, a custom and as you probably are all aware recent public controversies around the collection, analysis and publication of data sets about sensitive topics like say, Facebook collecting 87 million profiles or when giving them to Cambridge Analytica have helped push conversations around data ethics to the fore and in popular articles and emerging scholarly work, scholars, practitioners and policymakers have begun to try to flush out the long standing conceptual and practical tensions expressed in the notions of ethics and digital technologies and I should say that there is a large group of people, many of whom are in this room who have been doing this work for quite some time for 20 to 30 years, really since the beginning of computing, I have a book in my office that's a bibliography of ethics and computing from 1978, so it's not that this hasn't been something that people have been thinking about but it is something that now has new prominence. What many of these conversations have focused on the development of codes of ethics for data scientists and if you build on that my earlier definition of ethos, a code of ethics is the ethos of a particular group codified or transcribed. As Jacob Metcalf and Kate Crawford observed in a recent paper, ethics codes serve a number of functions beyond determining, deterring unethical behavior or determining unethical behavior, including the creation of a cohesive community identity, responding to external criticism and establishing a moral authority for self-regulation. Today there are many codes of professional ethics for many different professions including computer scientists, software engineers, statisticians, even plumbers and fortune tellers. The fortune teller code of ethics is quite short but it basically means it says don't make anything up. Some codes of ethics are more binding than others. So the common rule which regulates federally funded research on human subjects is one of the most famous and commented on these days as a potential model for computer scientists and the common rule in essence seeks to define a community of knowledge production that includes both professional researchers and ordinary people. But as Metcalf and Crawford point out, the line between professional researcher and ordinary person is in the context of digital technologies getting really blurry, right? One reason contemporary codes of ethics for digital technologies don't seem to have much teeth is because there's no specific shared community uniting the users of digital technologies. It's a bit like having a code of ethics for wielding a hammer. Now of course, we do actually have specific codes of ethics backed up by a legal sanction for the people who use hammers professionally, right? These are structural and civil engineers who can be held criminally responsible for flaws in the design of buildings. And we have a more general social ethos that regulates the general use of hammers and hammering, right? It's the same complicated messy system of social trust and law, here we are in a law school, that shapes how our society does all sorts of things. So there are certain technical affordances of digital technologies that make them quite different from hammers, but maybe not so different, right? And so if you take anything away from my talk, it's that we need to be careful thinking about what really is novel about these technologies and what isn't when we develop social policy and legal responses to them. So I'll leave you with two questions. First, how do communities of digital practice relate to both specific ethics codes and existed written laws to make sure that they use these new hangled, sorry, new fangled hammers appropriately and properly? And how do we as a more general community tackle these new devices and all the disruptions they bring in a way that is consonant with our own shared ethos, so at least what I hope is our own shared ethos, that of a free and democratic society. Thank you. Hey everyone, I'm Ben Green. I'm a fellow here at Berkman and a PhD student in the Computer Science Department. And I wanna talk today about my experiences trying to do socially and policy minded work here in the department. Oh, yeah, there we go, that's me. So when I came to Harvard about three or four years ago, I was full of naive optimism about what the field of computer science actually was. I, as a optimistic undergraduate, I thought it just meant a lot of people doing cool stuff to solve social problems using their computational tools. I quickly found out that was not the case. It's a technical field where they're far more interested in mathematical and engineering analysis. But what was particularly disturbing for me as I entered the field was to see the actual dismissal of non-technical voices and non-technical perspectives in the field. I had one experience where I heard a fellow graduate student of mine scoff at the idea of a social scientist being an actual scientist. And I had several conversations with faculty members in the department where they told me that the work that I wanted to do that was socially and policy minded was not computer science and wasn't worth doing. And I think that this is an incredibly dangerous and irresponsible perspective for a field like computer science to have because increasingly the tools of computer science being used to make some of the most important decisions in society today. My own research focuses on the use of data science and machine learning algorithms within local governments, specifically in city governments and the criminal justice system. And these are incredibly consequential decisions and yet we often see computer scientists entering the fray, wielding these tools as in believing them to just be a neutral source for good without fully understanding the context that they're entering into. And so this has led to situations of algorithms that make racially biased decisions, algorithms that actually warp and change the context that they're trying to be applied to, and algorithms that make decisions about people without the researchers behind those algorithms having fully grappled with the human impacts of their tools. But I'm also optimistic because I see the field moving quickly in the right direction. I even as many faculty members in computer science are still grappling with the social and political aspects of their work, students today in particular recognize intuitively and from experience that computer science is far more than a technical field and has increasingly and incredibly important social and political implications. So I've had many conversations with undergrads and grad students who are eagerly looking for opportunities to use their technical tools for good. I've talked to many of those students are in this room right now and I've created a list of social good tech jobs that I regularly share with students. Even in the computer science department just in the past month we've started a CS and ethics reading group. Many of the folks are here today and then that first meeting a few weeks ago we had about 20 students at that meeting and even two years ago I couldn't have imagined wrangling a group of maybe more than five to go read a paper that wasn't for a class and sit in a room and talk about this stuff. So I'm excited to see a quick change in the perspective of the field especially from the students today who really understand the need for broader perspectives within the field of computer science. So I'm optimistic about what the next several years will bring and I think that this ethical tech group in particular is a perfect example of how computer scientists can work with people across many diverse academic and non-academic perspectives to actually ensure we have a more positive future of technology. Thanks. Computer scientists who has spent more than the last decade in industry and I came to the Berkman Center because I wanted to think more deeply about the social responsibility of engineers and product managers and basically technologists as we build and ship products having felt a bit complicit in the state of the tech industry today. So I started my career at a big tech company. It's a place that is fun and or perceived as fun and open and transparent and we're all gonna work together to what Ben said to make the world a better place in this very idealistic sentiment and it's a place where you have full presentations across the company and to executives with completely in means alone because it's fun and it's great and it's this happy place where you build good products that's gonna change and save the world. And then I went and spent a little bit of time working on the contracting side where I helped companies write contracts for big enterprise companies and the federal government and also got to see how writing contracts without having users in mind and then building to those contracts end up producing products that actually cause a lot of harm and building services for government services that don't work for the people. And the flip side of that, I went, so after that I went and worked inside the federal government itself and got to really see that at a deep level as well and while I was in the federal government at the United States Digital Service, several of my former colleagues worked on the healthcare.gov failure where you really got to see the power of several really devoted engineers, product managers, designers, their power to really turn the tides where $800 million and 60 different contracts couldn't do. So what that really got me thinking about is the power of individual contributors and engineers, people who have our fingers on the keyboard to really make a difference. That's not to say that we shouldn't hold leaders accountable, that we shouldn't change our procurement process, all of that matters as well. But what I don't hear talked about as much and this is what Ben alluded to as well is to really think about how we can shift our engineering culture, how we train and teach our engineers and really impact folks at this level and many of us in this field we go through programs for our computer science where we become product managers at these companies we make decisions that significantly affect the direction of these products without any training in social science or art or design or really this deep understanding and empathy for users in the way that Luke and Mary and Joanne and Dua and Jen and Salome and Jenny will tell you about we don't have that kind of expertise and there's even this culture of end versus non-end and Ben also talked about that and we didn't coordinate at all and it's just there is end versus non-end or like SWE which is software engineer versus non-SWE it's how people talk in companies. Jay-Z just told me the other day that one of the companies he was at you were either engineering or support and it's a culture that is pervasive across companies and teams and it makes it so that we build in these silos where technology rolls we move fast, we make products that are efficient but we don't think nearly as deeply about the social impacts, adversarial attacks, privacy, security and there definitely teams that are thinking about that I mentioned the RSA conference that is going on today but for the most part most teams don't think about these problems as deeply and we can learn so much from having groups like this that get together often on product teams across companies to think about how we can build products while honoring the expertise of people who have deeply, deeply thought about how users interact with products and humans and build more responsible software for what we do, so thank you. I so clearly want to be a Cathy fan that I just. So hi, I want to put in your minds a way of rethinking what data are. Rather than thinking of them as something that pre-exists or that they're raw and plenty of my colleagues have critiqued that idea I want us to think about what exactly are social data the kinds of things that we're looking at in both industry and academia and what exactly we're doing when we sift around through online data to study social worlds. Are we just sifting through data? Are we observing public interactions as though we're walking through parks? Are we perhaps watching or listening in on other people's social lives without their knowledge? Or is it all of the above? And herein lies the challenge of how much industry and social sciences need each other and the fact that we don't discuss that when we're collecting data we're actually interacting with people's social lives in some way or another. To advance either industry, the products we develop or to advance our understanding of the human condition we actually deeply need each other. The social sciences for the most part are great tools for building theories about how the world works and how we understand or make sense of the world. So that's anthropology, political science, sociology. Humanities like philosophy, science, technology and studies are also meant to help us understand how the world works and to theorize that. Computer science, we know it's great at building tools that map and measure. We can perhaps see a network and see how it connects over space and time. But we deeply need each other's approaches to looking at how the world works to be able to develop a social world that's both informed by technology but not dominated by it. We need new tools for coming together to study this world. The social sciences have never been able to span space and time in the way that you can by looking at say a network map across the world in real time to see how many people might be tweeting this event, which is a social experience. But we also need to figure out the limits of those measurements and how we might go about understanding what does somebody mean when they retweet something? Is it agreement? Is it disagreement? Is it performance? Is it something deeper? Is it all of the above? We literally don't have the methodologies within social sciences to do that. But computer science doesn't have the training to see that as a social expression rather than a data point. At the end of the day, we have a great amount of responsibility and the great amount of responsibility we carry requires interacting with people. It means getting the general public to be as invested in learning about the human condition as social sciences might be and willing to let technology be part of that exploration. That's not a small ask. We need more than ethical principles or codes. We actually need research practices in both industry and social sciences. And those practices have to be able to collaborate across disciplines that literally until this decade have not really talked with one another. Literally they're set across different parts of a campus. So we're at the very beginning of learning how to study this deeply social world that is absolutely shot through with technology but not technology alone. And if we want to understand this social world, we need the general public. And I'll leave you with this. At the end of USENET today, the two groups that moderated USENET groups blocked were journalists and researchers. And if you look at your own practices today around how many of you might cover up your cameras or do somewhat adversarial practices to keep technologies from spying on you, we're heading toward a day where the general public doesn't trust technology to be a part of its life. That will shut down our capacity to understand our social worlds and the worlds we create online. Thank you. Hi, everyone. My name is Joanne and I am a artist and a designer. So when our working group first brainstormed the topics for today's luncheon, I was originally going to talk about empowering users. So you know how sometimes you look at a word and you stare at it for a long time and it starts to look really weird? The longer I thought about the words empowering users, the more I realized it's the context that necessitates the solution that's actually a part of the problem. So why is it a species who invented writing, books, computers, flew to the moon, feels the need to be empowered now? I think the problem isn't that we inherently lack power. It's that when we represent ourselves as users, we limit the power we think we can access. So what's wrong with our context? When thinking through contextual problems, if you look immediately around the problem, you're just gonna find more problems. I find it helpful to compare our context to another one from a different place in a different time. So we're gonna take a trip to great contiguity and compare users with another way of representing ourselves, muses. Muses are goddesses in Greek mythology. And my point here isn't to say that we're transcending humanity to become goddesses or cyborgs. What we take to be mythology today actually serve very specific and practical functions back in the day 2500 years ago. And these functions reveal the context of the time, which we can compare against ours and see what we're missing. Muses embodies all domains of knowledge, music, science, geography, art, poetry, drama, astronomy. The etymology of the word muse is song, which makes sense because this is before books and the media of a knowledge transfer back then was public recitation. And these recitations took place outdoors. Plato's Academy was a group of olive trees. This academy was one of three gymnasia. So each gymnasia represents one school of philosophy. And that kind of gym is totally different from the kind we go to today. Back then, physical movement and education were one and the same. So everyone learned the same place, they learned together. And if you follow Aristotle's peripatetic school, you go for walks together. And the Renaissance was actually very much inspired by Greek antiquity. And this is when Raphael painted the very famous fresco, the school of Athens. But there's a very important contextual change. So here in the Sistine Chapel, knowledge that used to be oral history is now permanently fixed onto the surface of the building. Making knowledge centralized and transcendent preserves the institutional power of the church. People can walk around the chapel, but they can't touch anything. So here, thinking and physical experience are kept separate. And half a century later, with the invention of the printing press, thinking and touching became connected. So now you can hold in your both hands a book that contains all of the knowledge and knowledge becomes democratized. But the downside is that you're now reading alone. And today we're more alone than ever and we're more disembodied than ever. To use a phone, you don't even need the whole hand anymore, just two thumbs and an index finger. And the user really is just two thumbs and an index finger. This worldview represented by muses has been in decline while the worldview represented by users is increasing exponentially. So you see that intersection right there around 1900. I think that intersection represents for me what Adam Curtis calls the century of the self. So the 20th century is a century defined by advertising, by mass consumerism, by the rise of PR as an industry, by the commodification of everyday life. And the 21st century is user-driven, user-generated and user-centered. Instead of accepting the century of the user, I hope we can build a context for being together. And what do I mean by this? I wanna recycle some wisdom from the muses to illustrate this idea. So being together means when the mind and the body move in concert. So muses recognize that human power is whole. It's not body parts and is certainly not finger gestures conditioned by devices. Being together means considering human across many scales. So human-computer interaction mostly studies this individual interaction between one person and their tool. And I think we need to study all of these interactions that scales of groups and communities. Being together also means that there are many domains of knowledge that are in conversation. And as humanity makes progress and as we accumulate knowledge instead of storing each domain of knowledge in a drawer, I think we should put it all on the table and have discussions about them. And that's what we're doing today. Honoring all excerpts. So thank you, Kathy, for organizing this. Thanks for being here. You should, I don't need to use the microphone. Hi, everyone. Good afternoon. My name is Dua Abueliones. I am a doctoral student here at the Law School and a fellow in the Perkman Klein Center. I'm here to talk to you today about the need to bridge the legal profession and the tech world. And I'm interested in this topic fairly because of a selfish reason. I'm a doctoral student who is trying to write a dissertation about criminal justice in artificial intelligence. And while I'm very lucky to have a great committee who supports me in what I'm doing, it was very hard for me to find someone who is an expert in both the world and help me to basically integrate these two distinct words together. We are seeing increasingly more and more courses in the Law School that are focusing on different aspects of the technology. Some of them are very specific and even intend to teach lawyers how to program. These initiatives are great and I hope we'll see more of them in all Law Schools across the country. But what I want to argue today is that a more inherent modification to the legal curriculum is needed to face the new challenges that technology brings to us. So the way I see it, especially the black law courses that we are all forced to take as a first-year law student, need to have some technological aspect in them. And I will demonstrate. For example, criminal law, how that course can be taught without mentioning the risk assessment tools that are taken over some of the work of judges. Or for example, predictive policing practices that are blurring the boundaries between the innocent, the suspect, and the convicted. Or constitutional law, how that course can be taught without mentioning the impact of surveillance technology on the First Amendment or the impact of online speech on the First Amendment. Maybe you're interested in tort law and how that one can be, we will soon be driven by automatic cars and how can we teach this course without paying attention to accidents that these cars might cause and who is liable for any mistakes that technology will make. Now these problems go way beyond academia and affect whatever we'll choose to do in the future as lawyers. As in-house lawyers, we will have the opportunity to shape the product, the design of the product, if we'll be involved early on and avoid expensive litigation, although who want to avoid litigation? As lawyers that are involved in litigation, if we have work for big companies, we'll be able to hire the best experts, but still we need to prepare the experts to testimony in court. Maybe if work for the government we'll be able to help shape the contract and the procurement of the tools that are mainly developed in the private sector. And of course, as judges, we need to have some basic knowledge in order to wait the expertise of whoever is standing before us. These cases are not hypothetical. They're starting to rise and they will grow more and more. Probably some of you are familiar with the Loomis case out of the Supreme Court of Wisconsin. Loomis challenged the use of risk assessment tool in his case. The court approved the use of the tool, but what was striking is that only the concurring judge mentioned the point that the court has no expertise, no knowledge about how the technology work. And apparently in oral argument, the judges ask the state and the defense lawyers questions about the technology and no one was able to respond. North Point, the company that developed campus, the risk assessment tool at Dispute, they even offered to submit Amicus brief to the court and explain more about the technology, but the court rejected that. These cases are going to, as I said, arise more and more in the future and we all need to be better prepared. Thank you. I'm Jen Halen and I'm a fellow at the Berkman Klein Center as well as a doctoral candidate in political science at the University of Minnesota. So my research focuses on the idea that policymakers, legislators, technology procurement officers can decide to use technology in a strategic way that ultimately can get them closer to what they want the world to be like or what they want a policy to result in. So adopting a technology isn't solely about what that technology can do, but it's about fitting it into a more complex puzzle of human interactions. These things are often adversarial in a political context and you need to understand what motivates different individuals within that process to ultimately understand what the outcome is. Social science helps to train students to understand human patterns and system behaviors by applying tools of scientific inquiry to these phenomena even when they're very messy, but in a way that ultimately results in patterns and a knowledge to build upon in order to better understand these phenomena. Political science focuses on understanding these mechanisms and patterns of control and influence and when and why and how people use those. These are all necessary for understanding problems like the risk assessment tools used in criminal justice because in order to properly understand what the result will be, you have to understand the deeply entrenched history of racism and socioeconomic prejudice and other very human systems that are shown in our data that are going to be influenced by utilizing a new tool in the same system, but this requires both that knowledge as well as more of an understanding about what fundamentally the technology is going to do. I encountered a lot of hurdles institutionally when first approaching these areas. I was also told very similar to what Ben heard, this is not political science. And so I think that that's where part of the problem comes up is that there isn't a clear discipline for these things to fall into and we need to encourage students to gather that's breadth of knowledge because these are new areas that don't fit neatly into the structures that we've already created in academia and elsewhere. I ended up arguing that I should be able to take computer science courses as a language requirement because I would learn Python and later Java. I don't know if anyone's ever tried saying computer code in parties, I wouldn't recommend it, but it did allow me to get the required knowledge that I needed for this dissertation and is something that more and more centers and academic institutions are starting to encourage, but I think that we need to really focus on taking down these hurdles for students as we're preparing undergrads to go into a variety of careers where that social and technical component are going to be more and more necessary. So we need to be able to provide students with the opportunity to use the tools and understand statistics and coding and things in a practical manner, but we also need to more deeply ingrain our study of technology and its influence in human systems into other topics that are important for them to take away and apply to their future careers. And I think that we need to reconsider some of our current educational structures in order to do so and to give those students the best opportunity possible. Going backwards. Okay, there we are. My name's Salome Filiun. I'm a lawyer and unsurprisingly, I'm going to be talking about regulation. So I think a lot about how we as a community can encourage the creation of the best versions of technology production. And I think one way to achieve a more holistic, even collective version of how we do technology is through regulation. So regulation does not have to be scary. I sort of think of these two quotes as a guiding principles of regulation. One, don't let the enemy be the perfect if the good is from Voltaire. And the others we come in peace comes over and over when alien life forms interact with human life forms. But the two quotes together sort of embody the spirit of what I think we should carry with us when we talk about regulating. One of two sides engaging with one another in good faith and a willingness to learn and sort of listen to one another's expertise languages. And then the other one is the willingness to realize that the 1.0 version of something is rarely the final version, right? It doesn't mean that it isn't worth making and using as a starting point. So sort of where to begin. This is a very blurry picture, but it's a picture of someone shooting themselves in the foot. I can't address all of the complexities wrapped up in our regulatory state or a taxonomy of regulation or a critique of where regulation fails in four minutes. So I'm gonna start with the assumption, the stereotypical assumption that all regulation is bad and not even that, that regulating is like shooting ourselves in the foot. We take out this tool and we think it's gonna fix a problem that exists out there. And then by using it, we not only don't solve that problem, but we hurt ourselves in the process. I would posit that maybe we have already shot ourselves in the foot. And now we need to go about figuring out what we're gonna do about it. So how do we not only just care for the foot that we have shot, but also ensure that more feet are not going to be shot in the future? And one solution I would suggest is starting to think meaningfully about regulation. Something that we did in our past and we were much more willing to use as a tool and something that I also would argue we've used really ambitiously in attempts to regulate that weren't just failures, but enormous successes. So we have taken in the past rapidly growing industries that were vitally important to the US economy and that also had big negative external effects on our society and we've regulated them successfully. And the example I'm going to use today is the EPA. So everybody loves to hate the EPA conservatives for the fact that it exists at all, progressives for the fact that it doesn't go far enough. But in fact, the EPA has been enormously successful in regulating pollutive industries to make them healthier and safer for the environment and they've done so in a number of ways. So where were we when the EPA started? Well, rivers were on fire and cities, that's Los Angeles down there, cities were just swathed in smog. And yeah, this was clearly an unsafe production ecosystem. Like literally our ecosystem was unsafe. So how did the EPA tackle this? Well, they used a lot of different regulatory tools that I think we can learn from today. So one, they used what we call proscribe and proscribe behavior, which is the banned use of pesticides like DDT. They mandated safety practices like secondary spill containment for oil storage facilities. They set standards. So the EPA sets safe standards for drinking water and they directly manage 160,000 different water systems in the US. They work with states, local governments and water suppliers to enforce those standards. They also calibrate incentives with sticks and carrots. So a stick is what was historically known as the Superfund where they would get taxes from petrochemical industries and use that money to do hazardous waste cleanup. This is known as internalizing an external cost. And carrots like encouraging solar and renewables with programs like the Green Power Partnership. They also did what we call preference shaping and Energy Star is a very successful example of preference shaping. It's a voluntary program that provides these simple, credible efficiency standards and it's totally voluntary. It's just providing sort of a standard and information for consumers. The EPA estimates it's saved about 14 billion in energy costs since 2006 alone. So in terms of getting lessons from the EPA, just sort of big picture takeaways, regulation doesn't have to mean the difference between no innovation and innovation. It can be the difference between safe innovation and unsafe innovation. So internalizing the costs of unsafe heavy manufacturing product levels the playing field between unsafe innovation and safe innovation like green energy. So it doesn't destroy all growth. It just influences the direction of growth in a new and innovative field. Anyway, I have lots more I could say about regulation but I'll wrap it up there. So since Dean and Boaz couldn't join us today, I'm going to channel them. They laugh because anyway, so I won't go into that. I want to channel them and basically read what they sent me to say. Boaz is a founder and CEO of Boku, a company that has driven open source integration at Fortune 500 companies. His hope was to make the web more open but he is currently rethinking this approach. Dean is executive director CEO of the Participatory Culture Foundation, a non-profit whose early focus was building open source software to drive media access in the pre-YouTube days. Today they support individuals, communities and ecosystems that make the media accessible. Throughout the early 2000s, Dean and Boaz are both deeply involved in the free and open source movement and they are critics in the working group against open source being a source for good and these are the points they sent to me. Reflecting on the original aim of the free and open source software movement to deeply empower individuals and put them in control of their computing destiny. Despite open source software being core to our modern computing infrastructure, the social benefits from this movement have not been distributed evenly. The largest beneficiaries are corporations and shareholders. On the other hand, the lay user today has less and less control over their digital selves. The move from personal computing to the cloud has exacerbated this lack of control with more and more processing and data storage happening in corporate data centers. And finally, calling into question the notion that building open source software is in and itself a social good. I know both Boaz and Dean have made it so that many of us have thought very differently about open source and it's something to really think deeply as we continue to move in this world of open is always good, which is something that we hear quite a bit. So I definitely reach out to either Boaz or Dean if you want to learn more from them and with that I'll turn it over to Jenny to wrap us up. Hi y'all, I am Jenny Korn and I am a critical race scholar. So what that means is that as an academic, I look often at the output of what programmers give me and I critique what they have done. For example, this is an example from the artwork that I shared at BKC, which is based on online image searches.  That means cartoons of white men show up before anybody looks like me does. Right, that's kind of funny, also kind of sad, okay? So there's a problem with that, that both women and folk of color are omitted from algorithmic results. In addition, another example is with the, hey, yeah, with the spread of facial recognition technology, we have to ask ourselves how might facial recognition technology be used in surveillance, particularly against folk of color, people who protest in racial justice movements? How will that change the relationship with police? How will that change our relationship in terms of potential job prospects with future employers? And so what we learn is that the disproportionate impacts of technology will always fall underrepresented communities when we don't actively work to include them, okay? So I'm gonna look at y'all and make an eye contact, I'm gonna repeat that because it's real serious, okay? We have to work to make sure that we include greater diversity from the community when it comes to ethics and technology. And that is the question that I want to ask y'all today, how might we actually do this on a more systematic way? How do we improve social justice and not repeat injustices? So I realized as an academic, instead of focusing only on the outputs of what programmers do and critiquing it, I actually can affect programmers themselves through their training. So let's talk about who is and who isn't at the table. How inclusive are the folk in the rooms in programming and coding and computer science and engineering and open source communities? What we do know is that when we don't actively build in folk of color and actual discussions of race, then we're going to reproduce whiteness. That's going to happen. So how do we make sure different folk are at the table? One way is to promote the training of critical race theory across the academy and industry. So critical race theory holds many tenants. One of the tenants is actually valuing experiential learning. So valuing that all of us up here are experts because we have lived through what technology has done to us, we've experienced it, we can share our perspectives on it. Another tenant of critical race theory is that racism is ordinary. It's embedded in American society. It's every day. It's as ubiquitous as, okay? Now if you don't know why I pushed our books up there, we really need to talk after this, okay? All right. And so another tenant of critical race theory is that we tie theory to action. We actually want to challenge racial power. We want to actually influence policy. And so again, what's one way to do that? Let's require training in critical race theory so that the people who are creating our technological worlds can actually think better about the ethical considerations related to race. So how do we do that? Well, one, instead of like hoping that programmers might want to take critical race theory, let's require it systematically. Let's say that folk who are gonna get a degree in computer science, which looks like this right now, actually have to also take critical race theory. The name of that course? Well, it could be critical race theory in technology. That's applying pressure from the academic side by requiring that folks have to take this course. There's also another side to the way we can apply pressure, and that's from industry. So let's have industry also update their tech job ads. As you see here, if you look at minimum qualifications, everything up on there is technical. There ain't one course that talks about people, okay? There's not one course that talks about people. I don't, y'all don't seem surprised, okay? That's surprising, and again sad. So why don't we actually require that the people who get hired have had to have had training in critical race theory? In that manner, then industry gets to help set curricular standards. It also helps folk like me train in critical race theory to have jobs in the future too, because then we will teach those classes. Now as I close, I do believe I do, that's right, it's an all moment, y'all. It's an all moment, okay? I do believe that all of us have the power and the agency to influence society and to make it better. I truly believe that. One way is to contact universities from which you've already graduated. You can ask them to include critical race theory as a required component of curricula. Another way is if you're in industry, if you're in a position to hire folk, if you're a manager, you can actually once again look for folk that have actually been trained in critical race theory. But the other way is informally, because I really believe in the power of dialogue, communication, casual conversation. All of us in this room now know that there's a field that exists called critical race theory. All of us now know that.  If you're a student about to graduate, you can tell the students behind you, y'all should take critical race theory. All of us in here can then tell our networks that this field exists, that's important, and that we need to all be trained in this critical race theory so that we can improve society together. That's all. And I think now we'll open for questions. I recognize there's probably a stop for some folks at one, but we can be here till about 1.15. And yeah, we'd love to hear from you and continue our dialogue with others in the room as well. And those are all of our names. They are in the order where we're seated, so if you forgot any of our names or our topic areas, just count down the list. A question for Salon or anyone who wants to address this. So with your EPA example, there are a lot of companies in the space, and so something like the Energy Star rating is a signal for consumers where it actually benefits companies. But in the tech field, there are just a few giants who really can ignore standards because people really don't have alternatives. And so I'm just curious what you think about how we apply regulation in that context. Yeah, this is my gun. Okay, it's hard to tell up here. Yeah, I think the benefit of just examining different regulatory metaphors or analogies is that there are a lot of different approaches. So where there are huge inside players regulation that breaks up some of those players or that looks at just internalizing costs across the board can set standards that apply to everyone. So that even effectively, if consumers don't have any other choice, those standards still are mandatorily placed on everyone. So for example, in Europe, the General Data Protection Regulation kicks in in May, that's gonna apply to everybody.  The other thing that you can look at is antitrust or sort of breaking up major programs. So for a long time, tobacco was controlled by Big Tobacco. Only Big Tobacco had an oligopoly basically on the tobacco industry. After we, the technical turn of the suit, the crap out of them, they had to pay huge fines and it actually was good for, well good for the competitive market of tobacco production because it enabled a lot of mom and pop stores to jump into that market. Now we may not wanna replicate the tobacco market, but it is an example of how regulation that focused on the big players that were causing a lot of the harms that we were concerned about actually fostered a competitive market that favored new incoming entrants. Hi, first of all, this is working. Thank you all for all of your work. I think this is really important work that is being done here. This question is really for any of you, but Kathy and Ben, you mentioned this in particular, the sort of end-on-end or sweet-not-sweet divide. Other than personally valuing this cross-discipline approach, how do you think that people, especially people who fall into that non-edge category, can break down this divide and make sure that there's more respect for all the different disciplines? I'll start and maybe Ben can feed it. I actually think that the burden should fall more on the end side. I respect that those, especially many of my colleagues here, are working really hard to basically find a seat at the table to be on equal footing as many others in these companies. This is just my call out there to my colleagues and tech companies that it's much of our duty as well to bring you all in, or not you all, but folks who are not engineering into the table and to value that expertise on an equal footing, not just as a, oh, I'll have you on the table because it's a requirement, or I'm doing you a favor by bringing you in, recognizing that that expertise is equally valuable versus the non-edge, having this uphill battle constantly to try to get heard. I really think it falls upon us on the end side to really bring everyone else in. Yeah, I would just quickly add on to that. I think that both in the industry and in academia, the more technical community seems to sort of fall into a more privileged place in terms of the status it's given and things like that. I think one place is maybe on leaders within the technical fields, whether a professor or a product manager or something like that to bring in those outside voices and sort of demonstrate through their practice and their leadership that these are important voices that need to be heard. I don't have sort of a great answer, especially as a non-technologist of what sort of someone from that side as an individual should really be doing to get into this space. Maybe someone else would, but. Oh, yes, ma'am. Or we can also just make sure other fields are paid just as much as engineering, so it's on, because, I mean, candidly, sometimes pay creates a hierarchy. If you're paid more, you're more valued. I just one thought, which is, I do think it's important for all of us to be underscoring something Ben just said, to be really explicit that there is a power asymmetry here that has a lot to do with being able to command wealth and value. And so I feel like as much as somebody trained in a non-edge world who works in predominantly engineering spaces, the most important thing I can recognize is that I have to persuade. I can't throw rocks. I can't tell people in computer science you have to do this. That's not working. And so that critical shift has to be persuading those in power and using the tools we have to force change, like the combination. I don't think it's either or. I have a question. So I'm wondering how do the people who are trained in ethics feature in this collaboration, both in academia and in the industry, in your experience? By which I mean trained in moral and political philosophy and not just the IRBs and review boards and the ethical system as it exists today. So, if any of you also, that would be good. Yeah, I think that ethics is a sticky term and we in the Ethics and Tech Working Group have talked a lot about whether or not ethics is the right term to describe what we're thinking about and what we think other folks should be thinking about. And I think one way to provide a synonym for ethics might be, I don't know, cultural social context, right? That it's not just about particular moral systems and codes, but it's about kind of broadening out the conversations within the tech sector and within tech and academia to understand the kind of social impact of their work and to engage with that as something that's just gonna happen. The tech industry is in the world, it's having effects on society. Often companies like Facebook are really happy to talk about their effects on society if they perceive that's gonna make the money or it's gonna get the prestige, but they also have these other potentially negative or complicated effects. And so I think let's not constrain this conversation to political philosophers and philosophers, although we should have them in the conversation, but also sociologists, critical race theorists, historians. I'll put in a plug for my own discipline. And I think, and I actually think that it's important to flip the script a little bit as well and to go to humanities and social science disciplines and say, so here are some things about tech that you might not know. How can you incorporate thinking about technologies and their social context into your predominantly non-technical conversations? That's maybe in some ways an even bigger ask than asking the tech companies to think about social impacts precisely because the humanities already see themselves and often are at a kind of asymmetry, a disadvantage in terms of power and status in the university and in the world. But I think you need to go, it does need to go both ways. And I think that's something that we're just in the academy trying to start figuring out how to do. I'll add one small thing to that since Barbara Gross isn't here. To, Ben talked a bit about CS curricula at Harvard, Barbara, and Alison Simmons who, so Alison is on the philosophy side and Barbara's on the computer science side. They've partnered together to embed ethics into computer science and they've been doing it for about three years. So it'll be interesting to see that unfold and also to see what industry picks up from that as well. Hi, my name is Laura. I just wanted to ask about something in connection with that. Alrighty. I come from the philosophy department and when I said I wanted to write about algorithms in 2012, the entire philosophy department just said that's not philosophy. So I understand where you're coming from but what I was sort of wondering is do we also need to talk about what is not solved just by putting people in the same room? Because what seems to me to be sort of urgent thing is the language barrier. And I mean, how do we bridge that gap? It's just people just do not understand each other. My sister's an engineer. I asked her to explain the algorithm to me and she just looked at me and said, I can't explain one. I can make one. Do you want one? I mean, that's as far as we got. So I was just wondering about that. Yeah, you've actually raised the point that we also discuss extensively in our group is how much is missed just in different professional languages speaking past one another. And I actually think that there's a little bit of a like full cultural immersion version of language where when we are just forced to sit in a room every Tuesday over the course of a year, you start to gain the comfort to be like, hold on, backup, you just used the word and I feel like you used it in a way that is a highly formal definition in your language. What does that mean? And that takes time and it takes like a regular meetings and it takes trust. But I do kind of think the immersion is just having to sit in a room and flesh out what those languages are. One, makes it clear where those language barriers are happening and two, creates the space to sort of start to break those down. At the same time, expanding the scope of what those language barriers can look like because I think that we are predisposed to say, oh, I can recognize that a critical race scholar is an expert in something probably critiquing and race and I can recognize that a social scientist is an expert in studying society but also sort of to get to a point that generates the experience, so experiential expertise. And who else needs to be in that room who's speaking a language that we don't hear, that might be all of the stakeholders that are not being considered when we do things like violate their privacy rights massively. Does anyone want to add something? I think the other way to think about is that there are many formats the language takes and we have conversations, we read, we write and language exists across a lot of different media and it might be difficult to communicate when you just restrain that to one, especially when two people speak different kinds of languages but in addition to this kind of immersion that Salome was talking about, like being able to see things together and being able to translate across different kinds of media I think helps increase that likelihood of understanding. I've spent the last almost six years at Microsoft Research in Cambridge which has majority computer scientists. There are three people who come from my background. I was trained in Native American Studies and Anthropology and I'm now in a computer science lab and I'm also based in a computing and engineering school. It has taken me that long to be able to have a conversation with my colleagues and understand in a rudimentary way what their taste and problems might be and I wanna connect this to the prior question about ethics is that we learn by doing and I know it's incredibly attractive to imagine we can read it and know it or have a set of principles and follow them but things like ethics are something we live and we do. It's not something we learn in the abstract. We have to be able to apply them and see how they change when I'm interacting with a different set of people. So the biggest challenge is that we need to do the same practice of learning when it comes to ethical principles and then what do they look like on the ground? We need to take the exact same approach to being able to understand these otherwise siloed approaches to building the world and building systems and see that they are always social. They're always gendered, they're always racialized, they're always classed, aged, able, like all of the time and that we're just beginning which is very exciting. Like I'm actually incredibly hopeful. We have never really sat down and thought, wow, when we build systems, we're always also building social worlds. So what does it look like to think about it that way and train in that way? The reason it hasn't changed, we haven't even tried that yet but let's try. Sure, I was just going to put in a plug for another, my other discipline which is science and technology studies. Because science and technology studies is explicitly about those kind of translation works and we should have more science and technology studies departments and have science and technology studies be something that engages across a lot of different fields. What's your training? I'm in science and technology studies. Like Jenny, I would also like there to be more jobs in this field. Hey, hi, I'm Anjali. I came here as an undergrad wanting to study the intersection of computer science and government and spent far too long, much like Ben and Jen, being told that those two things don't fit together and that I should pick. It took me four years to figure out that there were already a lot of academics doing, like answering the questions that I was interested in, in fields outside of those two departments. Like Luke mentioned in science and technology studies, information and library sciences, the history of science. And so I'm wondering what are your suggestions on finding people who are already working on the questions that we find interesting in perhaps departments outside of ours and how do we walk that line between molding our respective fields into something that is open to answering our questions versus going somewhere else where they may already be doing that? Yeah, I think that that's a really important question, something that I spent a lot of time trying to figure out. For me, it was a lot of sort of seeing the limits of what sort of the people right around me were doing and then realizing that, I mean, one of the amazing things about Harvard is how many other people are doing things outside of the engineering school, so I wasn't out of place where there was no one else. So I sent a lot of emails, knocked on a lot of doors, talked to a lot of people who seemed to be doing interesting things and eventually found people who I wanted to work with and sort of that's what led me to the Berkman Center. So I think there's a lot of outreach you have to do to get there. And the other thing, and this sort of touches on the previous question as well, is that it's really hard to do multiple things at once because especially, this is more true of a PhD than an undergrad experience, but you have to have sort of a home discipline and one of the things that's hard about doing multiple disciplines is that you sort of are splitting your time and for several years, you just feel like a really bad version of several things and it's only after several years of sticking through that that you realize that those like collection of expertises, even though neither in no individual field or maybe view you as an expert, you have an expertise in the intersection of three fields that actually is even harder to come by and more rare and more valuable. So but it's hard because right now there is such a lack of sort of guidance along those lines and I sort of tell people who wanna do this like you're sort of, there are now a couple other students, but you're sort of on your own and it's hard to realize how many people who are in shoes like yours or mine who maybe don't make it and they just pick one field and never get to pursue the work that they think is more important. So yes, there's like a need for students to sort of just cast off on their own and have this journey, but I think we shouldn't be making it that hard and we shouldn't be relying on students to do that, to do this work. This goes back to my answer to I think it was Jasmine's question. I think it's great there's lots of different interdisciplinary groups doing a lot of this work. I think there's a lot of responsibility on the computer science departments and the engineering departments to pull everyone in. So I'm positive you are going to go and lead engineering teams. I know you will, Anjali, and it's for people like us to really bring people in because the other fields are knocking and they're saying, hey, let us in. We want to impact the space and it's really on a lot of us in computer science and the engineering departments at companies to really bring those folks in and just say, hey, it's important for my team to bring these disciplines, I'm gonna do it. So that's all great advice and I think that it can really be a struggle, particularly when you're trying to decide what your interests are. So that can be the first step to kind of limit down who can be a resource for you. But I would suggest joining online communities and listservs and going to the conferences that are available in your area. And people can kind of direct you towards people who are at your institution or you can work with even at other institutions. And it also helps to just feel like you're not the only one who has these cross-disciplinary interests. I also think that there's value in looking within each of those departments and seeing who's open to being on a committee or helping you with coursework who specializes in something that you're interested in but they don't cross disciplinary boundaries that way. For instance, in your interesting government, if you're specifically interested in studying corruption or inequality or gendered influences on policy or things and that overlaps in some way, find people who do that and are open to bringing it into this new domain. And I think you can get a lot of unique knowledge that you can't find by only talking to people who have committed to this idea of going beyond in disciplines because there's value to be had within those specific domains as well. Because this is recorded, I'm going to take the opportunity to say organize. Before you graduate, get at least five of your colleagues who feel the same way and go to your departmental head, go to at least three other faculty and as a group say, I really felt like I needed to learn this and I didn't have it offered to me in this course. I'm gonna look for my department to do this after I graduate. You'd be shocked at how powerful you are as alum and I think the two strategies of having companies call out schools, we're not getting the students we need trained properly and students before you exit to say that to your home departments, incredibly powerful. We heard a good cry for the need for critical race theory and teaching in that area but is that unique? Is there, are there other topics that a well-rounded engineer or a well-rounded scientist needs to have in order to be part of the human race? So the question is you're asking for other courses besides critical race theory though, right? So I mean, just I come from a critical background so intersectional feminist theory is something, another course that I would like to see. I did focus this presentation particularly on race because I think that race gets often overlooked or people sometimes aren't comfortable talking about race. That's why I keep trying to push us to talk about race but I would like to see more critical perspectives along the lines of feminism as well and I think there are other courses that maybe other people have taken on this panel that they would like to see too. It's the problem though and the reason again why I focus on critical race theory being systematically required is that there are, there's room for electives but if you leave it for electives how many programmers would take those courses? That's the reason why I'm really pushing for across the board systematically requiring a critical course because I mean, everybody in this room has had experience with some sort of technology where they realized that they were not in the mind of the designer on the tail end, on the user end and so let's fix that. Thank you for your presentations. My name's Laura Leichkeli. I'm up from DC today. I'm at Georgetown at the Beck Center on social impact and innovation. I work directly with members of Congress in their districts on building technology for democracy in the 21st century. There's a real need for systems thinking in Congress. You could say, you say it's built as a complex adaptive system that is not adapting. Somebody had the Facebook hearing up from last week. My suggestion for everybody in this room right now is you have a huge opportunity to surge the ethics and technology questions into the thousand people running for Congress right now as a campaign issue that then can be followed up on in November and later. We're gonna lose 800 years of institutional memory in November just based on retirements right now. There's some real downsides to that on subject matter expertise, but in terms of process and systems, it's a huge opportunity. So what I would do is everybody in this room who is from somewhere else, it's not like Massachusetts is the problem here, but there are ways that you can make yourself obvious at home simply by going in and having a conversation about the fact that this new shiny thing in tech is about ethics, even though we should start with ethics. Every member of Congress crosses every single one of these issues at some point in their life on policymaking or institutional responsibilities, but the other opportunity you have here at Harvard is the boot camp for new members of Congress at the Kennedy School in November, December, January, any of the off-sites and just make sure somehow this is included and if they don't let you on the agenda do an un-conference or do some kind of other informal organizing in the periphery of the actual training. I'm sure you can hook it up if you start now. Sad time.


Alright, so I'm Jared Held, I'm from Fidelity and I'm here to kind of present a use case for, as an end user for Envoy to you guys today, and I'm here with Matt Turner from Tetra, who's going to talk a little bit about some of the help, some of the work they're doing as well with the Envoy Gateway. So yeah, first off, not endorsement of any vendors or anything along those lines. You know, standard disclaimer, make sure, you know, so yep, I am Jared Held, I'm a Principal Software Engineer, I've been working with Fidelity for the last 10 years. I actually started off with APIs when I first joined Fidelity as an intern, and since then I've just kind of, I moved around to a couple of business units, and I eventually ended up rejoining the API platform team once we came to kind of modernize the platform from, you know, the different systems that we've been using previously. Yeah, I guess I'm a software engineer at Tetra, I do a lot of community-facing open-source stuff, so I've been really interested in Envoy Gateway and Wazem and Mozero and those kind of projects, so I've been really interested in the ways that we've been helping Fidelity out, because a lot of it is based on the sort of open-source work, or is going to be, and that's what I'm going to talk about later. So yeah, there's, we've obviously gone on a long journey in our, you know, API platform in Fidelity from, like, you know, a variety of different, like, vendors and doing things homegrown, and, you know, we've always been, had a number of different objectives when we were going to be, like, building out an API platform, looking for specific features, you know, figuring out how to perform everything at scale, and obviously wanting to do this in an open-source-friendly manner. So Fidelity has always been really looking to innovate, as it's kind of gone through the years. I mean, it's, you know, from starting when they first purchased computers, you know, to when they, like, launched the home page for people to trade funds, to trade stocks and mutual funds and everything, and, you know, all the way to our cloud journey and deploying applications today. So in Fidelity, we've had a couple different vendors that we've used, you know, lower a couple different solutions. Some of it was on-prem, some of it was based in the cloud, like in VS As, but, you know, each different solution had a little bit of a different issue, right? It could be that, you know, that each business unit had its own gateway. It could be that, you know, it wasn't necessarily cloud-friendly. It could be a little bit difficult to upgrade them as a result, because it could be split out amongst different views, or, you know, there was a variety of different issues. We tried to solve that with VS As gateway. That solved some of the issues. We were able to deploy things automatically without having to go and manually configure things. It was a bit, it was friendlier, but it didn't really quite do everything that we needed to do. It didn't quite conform to what Fidelity was looking to do for requirements. So we started looking for a new solution, and we wanted to do that open source. There were a couple of different objectives that we had. We wanted it to be a relatively simple gateway. We didn't want anything that was really heavy-weight, and obviously, you know, on-way is going to be pretty lightweight. We wanted it to be highly resilient, in case any components failed, so that way we wouldn't have any downtime, and we'd be able to continue to serve our customers. And, you know, we wanted it to have some features that were already in built, like quota and security, things that we wouldn't have to redevelop on our own. Obviously, we only wanted, you know, best-class performance, and we wanted to be able to do things at a minimal cost. So that's where our whole Phenops optimization comes in and figuring out, okay, well, if we use Envoy, are we going to be able to run faster, and are we going to be able to run at a lower cost? And obviously, the answer was yes. Flexible. So with the filters types that Envoy provides, whether we'd be able to actually build out them to different features that are necessary to run our gateway. And then, obviously, we had some different languages that we wanted to support as well as we're going to be developing those different features. So that's where, you know, we started looking at a variety of different solutions. Things that were vendor-based, things that were open-source, things that were based in the cloud, things that were based on-prem, trying to figure out what's the proper solution. So we went through an evaluation about two years ago when we started on this whole journey to try to figure out what are we going to do to, you know, build a new gateway. And ultimately, we did arrive at Envoy. When we did the evaluation of Envoy, we realized that there's a lot going for it. I mean, there's obviously the observability and tracing that we've heard of today, which, you know, a lot of solutions didn't really have that level of depth when it came to that. The dynamic configuration via XDS, which we use in our own control plane, right, where you can configure that dynamically and in real time. The FinOps optimization, obviously, as I mentioned, in order to reduce our costs while we're doing all of these deployments, the multi-language filter support, so that way, you know, whether it's done in C++ or Lua or GoLang, whatever that may be, that we would be able to support that. We wanted something that was additionally close to the service match, right? Obviously, Envoy's very close to that. So that way, once we were building out this gateway, maybe we could do something with service match as well. REST, GRPC, WebSockets, Webhooks, we wanted support for all of those things. And additionally, we wanted to modernize our ingress through the edge to our internals. And obviously, we wanted to use this gateway on our internals as well. And obviously, as I mentioned, Envoy has top performance in class. So when we chose Envoy, we still had the problem of, well, okay, we've chosen a runtime for our gateway. What are we going to be doing for building out the rest of it? We needed to build out a management plane. We needed to build out a control plane. And that's what we proceeded to do over the last two years. So what you're seeing on your screen here is what we have something that we call internally as the stratum API platform. It provides a number of different things. As you can see, there's the multiple protocol support. And then we're also using a variety, we built out a variety of different tools to support this, as well as our own management UI. Where you can go in, you can self-service deploy an API and have that appear on the Envoy gateway itself. And then, based on OAS specs, and then additionally, configure your API to use certain filters. So we have a whole bunch of different tools and capabilities that we've built into the platform in order to best serve our customers' needs and for our development team's needs. So the idea behind this is that, regardless of the underlying technologies, that the API lifecycle stays the same across whatever product that you end up using, essentially, right? So we wanted to make sure that that was going to be stable no matter what we did. And one of the things that we did really consolidate on early on was to continue using OAS 3 contracts to ensure that during any deployment that every API uses an OAS 3 contract. Make sure that we have the full definition when we go to deploy. It's fully self-service, right? We have both platform managed and we're able to discover non-platform APIs as well. Things that aren't strictly running through a gateway today. And we're using domain modeling as well to make sure that we are able to classify those APIs appropriately. We also have the API tooling. As I've mentioned before, is a test studio that we built out. Then that allows us to go and test the APIs after they've been deployed. Different SDKs. We have a CLI that you can use to actually go and get information about what APIs are running and information like that. And then we have a variety of different other tools that we've built out, like breaking change validation where if you've deployed an API, you want to make sure that it's not going to go and break an existing deployment before you've actually deployed it. And as well as things like compliance APIs where we can ensure that when you are deploying something that you're not violating a security standard or a particular rule about which verbs are being used or whatever that may be. So as part of that, what you're seeing on the screen here is a simplified diagram of what we've actually built out. There's the management plane, which allows us to publish those APIs. We have an API publishing API. So that way, no matter which CICD solution you're using, you can go and call that API in order to publish that. That will then flow into a database that we have deployed. And you can additionally deploy that API via the stratum bui. So that way, you have both that it's fully self service, either via CICD or through the UI. From there, what we have is we have the XDS, they write that control plane. The control plane is consuming that information from the from an API, which links to the stratum database. So that way, we can actually on bootstrap, we can pull up that list of APIs that people have published. No matter, no matter whether that's in AWS or Azure or on prem or wherever that may be wherever our gateways are. Additionally, one of the things that we're also going to be doing is we're going to be integrating that XDS with Kafka. So that way, when somebody does publish an API, that it can just flow automatically in real time into the gateway. So that way, there's no like latency, no, no waiting time for after you publish your API for it to be able to actually you'd be able to call it. Once that goes into there, obviously, the envoy consumes that as well as all the other components that we have. So that way you are able to call your APIs through the gateway. So one of the other things that we wanted to do is really wanted to modernize how we're doing things too. So for both the edge and internally, we first started on our journey with the internal side of things, building out the gateways internally, and then additionally, making sure that everything, you know, function correctly, ran quickly, supporting all the different business use cases, adding the different authorization filters, you know, and supporting different protocols like the GRPC and the rest and everything like that. Then more recently, we've also been focusing on the edge modernization as well. As you can see on the left hand side, you know, we were coming through a firewall and then this has gateway. And then, you know, in the DMZ, there's a variety of different solutions that are lined up. That's, you know, how things worked previously. And what we're doing now is we're really deploying Envoy in the DMZ.  So it increases security and also make sure that our infrastructure is that our infrastructure is all consolidated into one particular pattern. So as we're building out that gateway, we had a decision to make as to how we're going to be implementing these different filters, these different features that we were going to be needing on the gateway. And we did an evaluation when we were doing that.  This by no means is all of them. But when we did our evaluation, we typically broke it down into, okay, well, we could build C++ filters, right? But as you may know, you know, it's can be difficult to find a, you know, talented C++ developer, it could be hard to, you know, retain them. And, you know, we additionally would have to build the Envoy binary, if we were going to be doing that. So we really wanted to try to avoid that, unless we were going to be doing something that required like the fastest speed that you could. So we then moved on to looking at kind of Lua and Wasm, and some of the external processing filters and everything along those lines. For a large majority of our filters, we actually use Lua because it's a very simple interface in order to program with that with the very simple hook into the actual Envoy. And we use it for some of our simpler filters. And additionally to make some call outs as well to external authorization APIs as well. More recently, we've been moving into the Xoff's filter type as well, for certain APIs as well, and then the external processing filter. We haven't gotten into Wasm too much yet, but we are evaluating that more recently, as that's matured a bit, and we're very excited about the future of Wasm on Envoy as well. One thing that we've done in Envoy is to ensure that we're able to meet the different use cases that the business units have, they might have different filter requirements. We deploy it in a decentralized model, the Envoy gateway. And what it is is that we have something that we call the orchestration filter. When people deploy their APIs, they can specify a particular tag essentially that lets the Envoy know which filter flow to actually execute. So it may require, one API may require validating a jot. One may be an opaque token, one may be an API key, and it may need to sign a jot later down the line or generate a different token. Who knows what it may be, right? There's a lot of different use cases out there. And we wanted to make sure that we were able to support that, and that's where this orchestration filter comes in. The orchestration filter is essentially a Lua filter that interprets that tag and then uses that in combination with composite filters in order to execute that those filters on a conditional basis. And that's effectively what that's doing. So it's really good for when there are common patterns and use cases and everything along those lines, right? But we did have a little bit of a problem and something that we're trying to solve. And what that is, is that it's not the best use case for when we have something it's a very API specific. So in the case of like a vendor or something along those lines, who needs to make a call into fidelity, they might have supply a different kind of token where they might do a different type of signature or something along those lines. So like simple use case here, there were four different APIs that were onboarded recently. One of them used IP IP wireless thing only one was only API keys. And then I had two different types of signature validation that were coming through as well. So one where the signature was just in the header, one where it was in a jot. So you need to validate the jot and the signature inside of the job. So you can have a wide variety of use cases. And that particular case, you have to build a filter for each one. And then you add that filter chain. So we're trying to figure out a way to solve that. But while we're trying to figure out a way to solve that, we also saw that there was a really big increase in performance. So what you're seeing on the screen here is based off compared to some of the other gateways that we had been using internally. Previously, we're really seeing about three to four times performance increase when we're using Envoy. And we know we can get faster on this as well. So what you're seeing is that when on our old gateway, when we're running this is on a single pod instance, the old gateway was only able to run about 300 TPS for Envoy were able to run 1000. Part of that's due to the reason that we use Lua filters. And as we transition over to bosom and other filter types, we do expect it to get faster as well. So one of the things that we're looking to do is the whole use case that I mentioned where you have different APIs and they require different authentication mechanisms that are very specific to those APIs is we wanted to try to make Envoy a bit more flexible. So what we've done is we're building on something that we're calling either policy engine or function engine, naming to be determined, right? But essentially what it allow you to do is that using a configuration based approach that you can apply different functions essentially in a side car that's going to be running that will allow you to do things like parse the, you know, grab grab that job out of the header, grab the the signature within the body of the job, and then be able to hash the the body to get that signature in addition to actually validating the job using something like the job authentication filter. So that way we can have that more flexible approach to be able to to support the very different APIs out there and then different vendors. So it's one of the things that we're really looking forward to doing and especially we're also hoping what this will do is for those of our developers within Fidelity who don't know Envoy at all that they can go and use these policies without ever having to actually touch Envoy or even know that it's really running underneath. Cool, thanks. So I'm just going to talk briefly about some of the work that TouchAinterverse are going to do in this area sort of together in the future and about a lot of the stuff that's happening in the sort of open source arena because a lot of this is going to be based on that. And I think it's I think it's pretty exciting which I'm a synchronized laptop series not the production values might not be might not be perfect. But yeah, I mean, I think we've done a lot of great work together so far, right? And a lot of these areas we were sort of ahead of the market. I mean, the external processing stuff was done before that was an official thing, right? It was kind of kind of clutched in, but it worked. But in a lot of those places, the market is now caught up and I think we can take advantage of that. And I think anybody else is in the same position can take advantage of that stuff too. So one of the first things I want to talk about is the Envoy Gateway project that you've probably heard sort of denyons talk about this morning. I think it's fair to say a lot of stuff that we've built was quite difficult or at least long winded to build. And of course, now it's got to be maintained. So I think one of the things that sort of fidelity could obviously do is move on to on-to-on gateway, right? So this is the control plane of that like the core of it is the go control plane library the same as you know, Monzo's custom service uses the same as Fidelity's stuff uses. So this is all you know, 100% of the sort of core of this is 100% tested code. And while, you know, Tetra is proud to have in a couple of their main contributors to Envoy Gateway, it is an open project with a big community backing in. So it's not going to go anywhere. And by adopting that, I think it's an obvious win where, you know, Fidelity won't have to be their own vendor, right? And I think the other big area of innovation that's really going to help everybody is, is Wazen, right? So you talked about all of the extensions we put into Envoy, making it into kind of a full featured API gateway kind of thing and all those functions that everybody needs like job validation like OIDC or like rate limiting like spotty validation the whole lot. And all of that stuff can be added to Envoy in various ways as we saw, right? But obviously Wazen is probably the way to do that going forwards. So I think doing that together, we can build this whole ecosystem of these plugins that can turn Envoy into a sort of fully featured API gateway project to rival the best commercial ones that I'm sure a lot of people here have, you know, used because they need the features but maybe had some bad experiences with. And if we manage the Envoy through the Envoy Gateway control plane, then that becomes really easy to do as well because Envoy Gateway implements the Gateway API, which is this, you know, sort of new emerging standard way to configure all kind, you know, all the ingress controllers you might ever have in Kubernetes. And we're working on extension to that. So we're working on making that API sort of modular and pluggable and having it be able to model all of these more advanced API gateway features. So as people in the community write these plugins with sort of an API gateway feature, maybe a certain type of signature validation, we can get a module, a component of the Gateway API that will model that and make it sort of configurable in a standard way, no more jumping into the old sort of annotations on the ingress object kind of thing. And I think, as you said, like Wazm just makes a lot of this, like a no brainer, that flow chart you have. It was perfectly valid, you know, sort of 12 months ago. But hopefully the answer going forward is going to be, you know, Wazm, Wazm is the answer. Right. And so all that really leaves is just the, you know, the language wars of what do we write and compile into Wazm.  If you don't like Rust then, you know, you're not only wrong, but you can use, there's a whole bunch of other things you can use. A tiny go is something I think is not on a lot of people's radars. It's a subset of the go language. We don't want to work on the compiler. That compiles down to Wazm quite nicely. So if you've got an infrastructure team that's really with Go, then that's a good thing to use. But, you know, so the jokes aside, I think it's important, I think the Wazm stuff addresses a bunch of real world concerns. Like it is a cool tech, it does have a lot of hype, but this isn't just a science experiment. We've seen how well the performance has increased already. You know, by moving to Lua, I think as you say, that's only going to be better when we move to Wazm. Especially if you're writing in a cool low level language with zero cost subtractions. It doesn't have a GC. But no, even, I mean, like Rust aside, it's another project that we've all been working on in communities, I think of Wazero, right, which is a Go lang, Wazm VM. So you can now host Wazm inside a Go program. So you could use that to write, you know, a test harness for an Envoy plugin or something, or I think maybe more relevantly, the external processing that you have talked about. So a lot of the stuff that Fidori does is actually going to have to stay in an external processing filter, because it needs to make it like a bunch of network calls of stuff that you just can't do from Wazm plugins inside Envoy. It needs to talk to IDPs and cert providers and stuff, I think, doesn't it? So some of that stuff is going to have to stay external. Obviously, Jarrod talked about building that system out to be this sort of DSL based thing that people who aren't familiar with Envoy can use. That, for example, could be written in, I'm riffing here on actually what your plan is, but like that, if anybody else is in a similar position, you could write something like that in Go lang and you could make that itself pluggable using Wazero, right, to host our sort of Wazm extensions into that. So, yeah, I think I have any more slides. I think that's basically all I was going to say. There's a bunch of stuff going on in the community around Envoy and Wazm, especially in Go lang. There's a bunch of design docs out there. There's a bunch of open source reflows. We'd love to see everybody get involved because there must be a whole lot of people who've got very similar requirements and I think we can all come together and build something awesome. So, yeah, if there are any questions at this time, I wouldn't say that we know explicitly that it's the Lua filters.  But I can't say that it's explicitly. We're still learning today, even as it is, exactly how to configure Envoy in the best ways. As everybody knows, Envoy can be a little complex. So I'm sure there's probably something that we've missed that we could use to you know help improve that performance as well. But we do know that Wazm really would help improve on that performance as well. So I wouldn't explicitly say it's the Lua filters. We've been very happy with them so far. Oh, and in case anybody can hear that question, basically the question was, do we know that it's explicitly the Lua filters that are slowing, keeping us at 1,000 TPS? So we've used a couple different filter types within Fidelity. I didn't really cover them all. But there's generally been a trend towards using Lua actually internally, simply because you know the, the how you integrate it is relatively easy. I mean it has just the Envoy on request, Envoy on response. It's a very simple, you know, it's plain as day, how you implement it, right? Additionally, I mean it can make the callouts that are necessary via the HTTP call, which is one of the wider use cases that we see when somebody's making a call to like an API that they may need to do for authorization. So and then, you know, just things that might be simpler as well within the Envoy as well. When it comes to other more complex use cases, like if we need to sign a jot, right, we do some re-signing of jots with specific AP, with specific keys essentially, to go to specific targets. And for that, what we did is before the external processing filter really came into its own, we built out our own C++ filter, which did that GRPC connection to a side car in order to do that kind of jot signing. So yeah, it's a mixture of Lua, some homegrown C++, and we're also going to be using the external processing filter today as well for like the policy engine and things like that, that we're going to be seeing a lot more use of the external processing filter. Thank you, gentlemen. Thank you.