Digital Urbanism: Smart Cities, Data & Equity

[00:00:00]welcome everyone I'm Amol andreas I'm the Dean of the school and I'm really thrilled to be having you all today in our school and also hosting this amazing conference you should know that it's well we don't sell but if we did sell it would be sold out and and that just the response has been you know incredible and it's so exciting to have scholars and practitioners from across the disciplines of Technology in the built environment and I see alumni and you know thinking about change in fact and how we deal with change everything is moving so fast as systems that we once established are being transformed and it's not clear whether it's always for the better and so for us to come together to think about these changes and their impacts and how we can kind of redirect them and harness them and make them visible and understand them and present them in a new way and find new modes of collaboration I think is what we're all kind of here today to assemble around urgent questions and in general I think this is the urgency that we find ourselves in where institutions and disciplines are both unable to adapt and yet this is what we have to transform and retool and I'm particularly excited that the conference was really masterminded by Leah Meister Lynn who is as you know assistant professor here at school but even more importantly I think Leah is one of the kind of seed bridges that we have you know sort of put forth in the hope of stitching back together architecture and urban planning and this body that was once divided needs to act as one again in new ways and I think Leah's you know unbelievable contribution already even within a few years to single-handedly transforming the kind of thinking and work of both architecture and planning students makes me hope for so please welcome Leah my student good morning I'm so excited okay I'll admit I've been excited for today in many ways much longer than it takes to organize a conference I want to start by thinking the school and the genome all especially for the support you've given to this conference and that support is very much appreciated and very deeply felt I want to thank Lila and cataluña and her entire advance and communications teams none of this is possible without your enthusiasm and your wizardry and I want to thank all of our participants from whom you will hear today participants contributors and of course the moderators as well for your time your attention your interest your work and your care because honestly well rather I'd like to start as honestly as I can by saying that I don't exactly know where to begin I do know that somewhere at some point in the last decade I lost count of the many irrevocably consequential changes in the digital material landscape of cities and I lose track consistently trying to follow the socio political cultural and economic processes that flow across through and against those landscapes I have lost count of the devices and the disruptions the platforms and the predictions I have lost track of the algorithmic and artificial automated and autonomous I suspect some of you are like me in this regard I am losing count of the new ways to count and by that mechanism the ways we determine who counts I'm afraid I'm losing track of everything that's designed to track and of course we're here because in both of those cases we're really talking about people we're talking about technologies designed this account and track people we're here to talk about digital technologies and cities which might mean very many different things today but for me at least right now I mean the collection of connective platforms computational processes and informational infrastructures that are reorganizing restructuring and in many cases simply accelerating social and capital relations within cities and across regions and specifically accomplishing this by changing the means by which places and people neighborhoods and communities are designed planned conceived imagined operated and represented so here's a quick really close to home and admittedly advantageous ly current example think of we work six months ago and think of we work today whatever happens internally for that organization in New York we work is not exactly a start-up but a technological intervention into the very code of the city's planning and it's spatio economic reproduction as New York City's largest tenant it is worth asking what happens if we work doesn't make us rent and whether New Yorkers will have to decide that this particular firm is now too big to fail I can reframe that question as one of technological and built scale it's worth asking whether commercial rent arbitrage predicated on precarious labor and facilitated by fastidious data collection and analysis that is counting and trucking right has scaled beyond the units of desks per week and conference rooms per hour into something not only larger but qualitatively different something that involves and implicates all of us put yet another way where's the line what is the scalar threshold between space as a service and space as a responsibility we work as just one example think ride-sharing and autonomous vehicles consider ecommerce logistics and personal GPS enabled wayfinding and routing crime mapping and predictive policing or social media based targeted advertising their internal logics their scale and reach their math their hardware and their impacts on human urban environments are not dissimilar among their commonalities they all involve spatial technologies of sensing and extraction extracting information as commodity as material extracting attention labor and time and particularly in a spatially regulated democracy extracting votes we know that now okay so before this gets too dark I don't I don't think we're here to talk about the good the bad and the optimized right and I won't speak for everyone but I'm not here to personally dávila PHY smart city sales pitches or nor am I here necessarily to accept techno positivism or the techno optimism that usually follows it rather I am so excited which means I will stop talking soon so that we can get into it I'm so excited because across today we will hear about digitality high powered counting right in terms that involve and implicate all of us in research in design in action and an outcome in the messy inefficient ambiguous conflicted diverse terms of urbanism plural the pluralism of cities the colocation and simultaneity of different communities and their spatial practices and priorities presents challenges that cleanly digital technologies cannot resolve by definition and thus it recalls questions and challenges that are absolutely not new those of access and distribution inclusion and marginalization equity and justice these are questions of responsibility and obligation so plan for the morning we're going to very quickly introduce the moderators for the to morning panels who will in turn introduce you to our incredible and generous speakers if you haven't already please grab a program which does include full bios and abstracts they're out there we will have two panels like I said before we break for lunch in each of them with three presentations by a discussion I'm gonna go in reverse order our second panel on systems of representation will be Margaret moderated by Marco Sudha co-director of the critical curatorial and conceptual practices in architecture program here at GSA among his many other super interesting projects is ongoing research and exhibition work control syntax examining smart city forms in formulations and the processes of computational urbanism they prescribed and otherwise and gender before that panel I would like to invite up Malo Hudson associate professor in urban planning also here at Columbia Giza and director of the school's urban community and health equity lab his work sits at the intersection of Community Development and health equity with a specific mind toward racial and ethnic inequalities and well not in your official bio I also think your mother's recent work on circular economies were warrants mentioning because it could certainly be of interest to our discussion so thank you very much thank you for the kind introduction and for inviting me here as Leah said I'm Malo Hudson I want to welcome you all to Columbia and I wanted to start off by first thanking Dean Amal Andros I think that when you when you think about urban planning in architecture and the built in urban design and think about the built environment overall you have to think about innovation and where we're going as a society and so one of the things I've so proud about being a part of G SAP is is the vision in this department or in the school I should say and how we think about innovation technology and so forth and so I look forward to today's discussion I also want to thank my colleague Leah Meister Lynne who's been very humble but I would have to say it's a pleasure to watch you as a young scholar and put something so important like this together and having the leadership to do so and I know it takes a lot of time but I hope today you can see all of that hard work has paid off so anyway big again I want to thank you for that [Applause] so as I was thinking about moderating this panel I thought about my own work in whatnot and 20 years ago I started my doctoral program at MIT I had the opportunity to work with people like William Mitchell Stephen Graham and Manuel Castells and many of us as young doctoral students were thinking about the growth of technology certainly Silicon Valley was taking off and we didn't talk about digital organisms then we were thinking about those concepts but didn't have it quite ly quite defined like it is today but we're thinking about information technology and what role would technology play in altering our daily lives in terms of where we live what we work thinking about mobility in general thinking about inequality opportunity there was a big discussion about the digital divide and all of those things and so I went back last night and looked at our old journals of our student journals and there was a whole thing on information technology and making in placemaking and so I'd say we've come a long way and so the panel that were I'm going to moderate is on infrastructures and digital materiality and part of that again goes back to many of the same themes we were talking about before but certainly much more advanced but thinking about the role of digital technologies in our everyday lives whether that be sensors will that be big data whether that be privacy security all the things that we think about now and so I would like to bring up our first panelist who will talk about how introduced one by one but that will begin to move us in this direction so the first panelist is Marissa Marais and I'll give the bios or NV you know in the booklet but I'll say a few things but Nerissa is a strategic planner and project manager who has worked extensively with both public and private sector clients on urban innovation parks in open space coastal planning and real estate and economic development projects at present she's an associate director of America's most innovative urban development project sidewalk Toronto and part of this was sidewalk Toronto's really thinking about the role that technology new technologies can play to improve the lives of urban citizens thinking about the digital the physical and also policy and financing and so the case she'll present today will really dive into issues around urban mobility system mobility and thinking about new systems sustainability and stormwater infrastructure and outcome-based Building Code so without set with that said please give a warm welcome to nervous amore I'm gonna go a little shallow and and wide today rather than deep so hopefully that will be okay but I think that the project that I'm working on in Toronto can introduce so many of the ideas that are gonna be talked about not only in our panel but later on this afternoon so I wanted to kind of give you a touchstone across the the whole of the project but also I wanted to talk a little bit at the beginning just about sidewalk labs where I work what we look for and think about in projects as well as then introduce the sidewalk Toronto project as a case study um so it's no surprise to the crowd here today you know that cities are ever facing more struggles and challenges in quality of life as we face continued urbanization around the world and in response to this we've always looked to technology through the ages but we know from history that technology can have both positive and negative outcomes and that's you know absolutely still true for what we're facing now instead of this next digital revolution we have to think really carefully about the impacts that what we do are going to have on the communities and society around us so sidewalk labs was actually formulated to think about these questions it's a company that's often sort of characterized as a smart city company but actually what's interesting is that the vast majority of people working at sidewalk are geeky urban planners and architects and engineers and they're mixed together with technologists but these are people most of us coming from having worked for the cities with cities and we are sort of looking for that spot looking for opportunity without bringing on the negative consequences so we do this by looking at all areas of innovation so today we're talking about digital innovation but in fact the company looks at innovation across the board so we look at materials innovation we look at policy and financing all of the tools that you have to look at in the toolkits to think about quality of life because the way we think of ourselves as a firm it's actually as a firm that's looking at increasing quality of life for citizens it's not about putting some digital layer on with nothing else it's really looking at the outcomes that all of these different kind of new innovations can bring in cities so we've organized the company in what we call five pillars and this is important because all of these parts of projects of city making we think are equally important so it's social infrastructure buildings and housing mobility public realm and sustainability with an emphasis on climate positive we don't think that projects can can or should be undertaken without all of these areas being investigated again it's not just about sticking some sort of digital technology on top it's about looking at how can all of these different areas be taken together to really improve outcomes for citizens so how do we think about sort of implementing that or as a framework for projects we think of these five pillars as sort of different physical layers in the environment with the digital layer is just one more of those and all together these sort of six different elements together are a framework for looking or in approaching a project so what does that mean in terms of a real project like to talk a little bit about sidewalk Toronto in this context so Toronto sidewalk Toronto is a project it's 15 acres of land on the eastern Waterfront in Toronto it's on the very far east of the waterfront but still that being said it's only 15 or 20 minutes walk from the major transportation hub in downtown Toronto so it's very close to the downtown area it's a brownfield land axé industrial site with almost no tendency on it at the moment it's owned by Waterfront Toronto which is a tripartite government organization with representatives from the city from the province and from the federal government and they've been developing the waterfront over the last 15 or 20 years they're quite progressive so they've been doing a lot of progressive development of the waterfront in downtown Toronto and so with this project which is one of the last parcels of land they have they were really looking to kind of move the bar on innovation with the development so they went out with an RFP for the site particularly looking for an innovation and financing partner so we responded to the RFP and we won in 2017 right at the end of the year and what did we win we won the right to actually prepare a plan for the site so that plan has to be ratified by government by both Waterfront Toronto in the city as well but that's what we've been doing is preparing that plan over the last year and a half and it was presented to Waterfront Toronto in June of this past summer one of the reasons we were interested in the project in Toronto is that waterfront Toronto's goals for the project are very similar to what we are interested in so they're interested in job creation housing sustainability mobility and urban and urban innovation it felt like a really great fit for us there looking holistically at the project and holistically at the outcomes that they want by developing this parcel of land so how do we approach a project from the beginning well because we are a kind of geeky group of urban planners and so on we do look at it just you know from a normal lens of urban design and urban planning at the beginning we look at what the relationship of the site is to the surrounding city we look at the connectivity we look at what the public spaces are going to be in key side the name of the site is the key side site we're excited to have three new public spaces that we're gonna be bringing online and so our design team and Landscape Architects have been looking at how to design those spaces but today since we're talking about digital digital layers and the digital architecture what I wanted to do is for each of these different pillars like for public space sort of talk about the digital layer that we see for each of these so for example for public space we're really interested in being able to enable more people more of the time to be outside and to be using public space so we think about that through basic design but we also think about the digital layer and how that can help to realize that outcome so whether it's something like weather mitigation structures that are tied to real time data about the weather that can deploy different structures and protect different areas of the site to make it more more usable outdoor space whether it's flexible multi spaced areas that have a digital architecture underlayer that make the space flexible so that communities can adapt that space in many different ways whether it be today or next week or ten years in the future when the needs of the community change whether it's ubiquitous connectivity and Wi-Fi to kind of lower the digital access and digital divide for people the neighborhood whether it's developing tools so that the community can self manage its own programming advocate for what they want in in spaces these are all different ways are different tools we look at for using a digital layer to actually provide those outcomes that we look for so mobility is the same way the actual most important backbone of mobility for the site to us is transit you know in any dense urban area having great public transit is the most important thing we feel so here in Toronto what we're actually pushing there is new financing vehicles for financing extension of the LRT through neighborhoods the west of us and then through our site so that's actually not added a layer it's actually a financing tool but I want to mention it because it's the most important thing to us but then we're also interested in lots of other digital infrastructure developments as well so for us public the reduction of public sorry the reduction of private car owner trip and private car trips is one of our most important outcomes for mobility so how do we do that we look to obviously support public transit but also to make active transportation more attractive so we do that for example through adaptive traffic signals and bicycle green waves adaptive traffic signals can help speed the LRT faster so give it better performance times it can make sia crosswalks safer towards a vision zero goals by having sort of adaptive traffic adaptive crosswalks in the sidewalk and it can use things like the bicycle Green Wave to speed up travel by bicycles and make their transit through signals more safe one of the things I'm particularly interested in is our curb management a dynamic curb we're planning for the streets and key side to be curbless also to have no parking on the street and no parking lanes so here in the bottom two pictures on the slide you can see sort of what would be traditionally thought of sidewalk on the left there's a car it's pulling in it's a drop-off and pickup zone that's variable used in high peak times as you know for vehicles but then an off-peak times you can use it as public space so you're reclaiming space back is one of the goals is more space for more people more at the time but also by emphasizing ride-sharing and pickup and drop off rather than private car ownership you're also working towards that goal of lowering private car ownership and you know this can only be done through data you need to first be able to manage pickup and drop off soon you need to be able to manage sidewalk used in different ways at different times you need to know who's coming in and going out or at least you need to know that the volume and capacity of that rather than personal details about anybody but volumes and capacities to be able to plan this kind of infrastructure for buildings innovation again our most exciting facet is not a digital facet but I'm still gonna mention it which is that this would be the first district built with mass tall timber all the buildings are planned to be built with tall timber and there also be the tallest buildings in the world this is enabled by digital processes it's enabled by designs of a modular kit of parts and a digital fabrication process that would take sourcing of timber through factories and Inter construction what we're most excited about this is it results in incredible construction time savings which equates into cost savings which for us we put back into affordable housing I want to mention the outcome based code quickly but the there's a there's a lot of use of environmental sensing for industrial uses and for environmental monitoring but we think that a similar kind of technology can be used within mixed-use developments to allow a great a mix of tenants so not just residential commercial but even light industrial if you want to allow that kind of mix for a more vibrant sort of live work environment you still have to monitor you have to know that that's going to be safe and we think that the technology that's available today can help to do that all of the innovations were doing in buildings as I said are resulting in something we're very proud of which is a commitment to 40% below market housing at keysight this will be 2 to 3 times more affordable housing that's been delivered by anybody else on any of the developments in the waterfront in the entire history of the development there then in terms of district infrastructure I would say we're introducing a lot of sort of innovative technology into district infrastructure I mean traditionally I mean most of this kind of infrastructure already works with control systems it has digital layers to it already but there are a number of different advances that we're proposing that we're very excited about that really significantly change for example with the storm water management we're proposing to pilot some technology that's actually already piloted here in New York with the ep they work with them one of the companies that we're interested in working with it adds a layer of smart controls to the system anticipates weather conditions it can flush out a system before a storm so there's better retention levels and keeps stormwater out of the municipal system we're predicting a 90% reduction in storm water into the municipal system through the design that we're proposing at the keysight site it's a huge differential amount and waste collection speaking about circular economies one of the things we're excited is the implementation of a pneumatic system into the site but more than that it's actually the information about the recycling that's coming through we're working with a local materials recovery and recycling facility to look at the garbage that's coming through to be able to provide information back to residents about how they're doing on recycling we think that an education loop it's really about you know human activity that education loop back to residents about how they're doing with recycling is the only way that you can really move the bar on sort of improvement to recycling and waste we're tied at targeting an 80 percent reduction of landfill for our waste collection and a key side which is radical compared to what they have in Toronto today which I think is somewhere between an inn multi-tenant buildings they get somewhere between 17 to 23 percent variably between residential and commercial but one of the other elements were most excited about is energy systems because achieving climate positive at keysight is our our goal that comes from just great design at the beginning passive house designs even for these larger buildings renewable energy sources Toronto has a pretty green grid which is great but augmenting that with solar with geothermal but then also one thing we're excited about is the use of AI to manage the building we know looking at buildings today we model buildings in a much more sophisticated way we have ASHRAE standards all sorts of other standards in Toronto they have slightly different ones but that we use to model buildings and to require for permitting of buildings but we know actually looking at the performance of buildings and in true operations the buildings just aren't performing at the level that they're supposed to according to the models so why is that a lot of that is just the human operations of the building they're simply not able to manage buildings in the way they're modelled and so we're really excited we're developing a series of building controls that will help automate you know which will absolutely be able to be managed and controlled by humans but also help to take over some of the functions that aren't being able to be done today to really radically lower the use of energy in buildings so the last one I don't want to miss is talking about social infrastructure in Toronto we're not looking at actually providing services and we think that's done very effectively by the government and other community groups and organizations in Toronto but we're very excited to partner with those kind of groups there for this we're really thinking about inclusion and equity whether it's providing digital tools where communities can get more involved in their own programming whether it's digital literacy programs or whether it's access to digital technology on the site these are the kind of things that we're very excited to partner with with local folks to deliver for the project so the result of all that is you know a set of outcomes that we're very excited about 40% below house below market housing as I mentioned 85% reduction in greenhouse gases at the site and at 60 percent reduction in car trips these are the metrics that we're aiming for and believe we can hit with the project but having said all that you know we are a private company that's going to develop the site and what does it mean to implement all of these or propose to implement all this digital infrastructure I can see some smiles in the front row so you know one of the other things that I wanted to mention or talk about today is really the framework for how do you governor a lot because it is incredibly important and we are do think of ourselves as good actors and we think it's incredibly important to address the implications of all of this so we've had a extensive consultation in Toronto we've had we've met with more than 20,000 Torontonians face to face in workshops and and other activities we've had more than 200,000 people participate in online webinars we've had grant programs and or fellowships and we've had a lot of discussion in the press a lot of discussion and community forums so some of the messages we've been hearing in Toronto are summarized here today so no tech protect the sake no data for data's sake we shouldn't be gathering or implementing anything that isn't directly tied back to achieving those outcomes that we're aiming for and that the government is interested in we should build in privacy from the beginning of development of any systems and then the outputs whether it's data outputs or whether it's technology that's implemented those should all be seen as resulting in in providing results for the public good so data that's generated at the site especially in public realm or an on land that was originally public we see that as an asset that should be a public asset and so we believe that that data should be actually made available to everybody and should not be privatized and no single entity whether it's us as a private company another private company or even a government agency should be able to monopolize either that technology or the data the results from it what does that actually mean in practice how do we take those that kind of feedback and think about how it's actually acted on so one thing that we've done is we've developed a set of responsible data use guidelines and an assessment system that we use in-house when we develop all our systems so we look to make sure that whatever we're developing has beneficial purpose that anything any data would be publicly accessible by default we look for transparency and in implementation so what are we implementing where is it going to be physically in the environment what is it collecting where does the data go who has governance over that data that should all be clear to the public and to government if we're developing anything with AI how do we develop that responsibly knowing the biases that can go into AI from the very beginning because of the datasets that are used how do we keep data to a minimum keep it secure and keep it diet de-identified at source and by default and you know just frankly speaking it's been a big I think misunderstanding in Toronto because we are an alphabet company and we're a sister company at Google we're interested in urban development we're not interested in advertising so we don't we're not interested in selling sharing or using personal information information for advertising in any way that's just not our business model and but it's it's one thing to say that we're doing that for ourselves but that's that's certainly not enough we're still a private company so we firmly believe that all of this should be overseen by government so in Toronto we've proposed an urban data trust that would be an independent government entity to oversee to review plans and plans and systems designs before they would be implemented in Toronto it may not end up being an urban made data trust and one of the things that's been very exciting about the project is that both the city and the provincial government are actually doing a refresh are looking at their existing data governance and privacy policies and regulations and they're actually aiming to update those over the next year partly in response to the project which we actually think is a very positive thing because we feel that these the government should be overseeing what's happening for this type of technology another thing that we're thinking about is how do you how do you bake in that transparency from the very beginning of a project so one of the things I'm excited about is that we're thinking about what materials would we submit with a development of formal development application to the city right now in Toronto I think it's the same in New York when you hand-in-hand in your first application for a project you don't have to provide any sort of information on a digital architecture of a site so we're thinking about you know what kind of system the architecture diagrams diagrams that sort of show the layering of the digital and the physical together what materials could we provide that would make that clear and transparent from the beginning of the project then those would be sort of reviewed and go through iterations through responsible data assessments and overview and then at the end what we think is equally as important is thinking about operational capacity we don't think that all systems and cities should be privatized we think it should be capacity that the government has so how do you work with government partners through co-creation through using regulatory processes to educate and to and to knowledge share with them how do you do that in a way that you can build capacity on the government side to take over as much as they can of these systems into operations of a site so the last thing I wanted to mention was then you know that transparency shouldn't and just when the project ends we think it's really important that people have visibility over this digital layer which is often you know invisible to people so we've worked with hundreds of partners to develop an open standard and visual language these are icons that would be displayed anywhere digital technology is implemented they're icons that describe the purpose of the technology the type of type of equipment that's being used whether the data is identified or de-identified who's responsible for the project or for the or for the equipment and how can you learn more so today maybe you know if CCTV cameras are being used you might get one small sign that sort of tells you about this this gives you much more information it starts to create a shared language and it gives you a way to actually find out more and to provide feedback so that's a super quick and shallow introduction to the project if you want some light reading our plan for the project is called the master innovation and development plan it's on our sidewalk Toronto website it's a mere 1500 pages with a mere 40 appendices but if you want even more information we will be actually at the end of this month publishing a digital in a specific digital innovation appendix a lot of the appendices are planning appendices there's a lot of detail there already but this is specifically about the digital architecture for the site so that'll be on the website it'll be public for everybody so please read if you're interested so thank you very much for a very thoughtful presentation the next speaker is Jennifer Dean who actually will complement this presentation quite well Jennifer is a solutions engineer at new mana translating data into insights for numinous partner cities around the world previously she was the founder and CEO of Park it a compute a computer vision company that developed algorithms to transform Internet Protocol cameras into a real-time parking data sensor venture backed by Jaguar Land Rover deployed across North America with major state universities municipalities and acquired by Newman ax and 2018 one of the things that when you look into Newman and the work of Jennifer they measure all forms of street-level activity with a privacy first approach so I know that many people with this last presentation thing about privacy and the mission is to make cities more responsive so they are safer healthier and more equitable and one thing I really like about looking at Jennifer's backgrounds or online and different things and certainly Newman ah there's a lot of discussion about privacy by design intelligence without surveillance and on their website they say we make cities more walkable bikeable equitable with data so with that said Jennifer please come up I'm so excited to be here today for this conversation on digital materiality I'm Jennifer ding from nuna today I'll talk a little bit about how we at Newman approach building the infrastructure and data platforms for creating street-level data in a human centered and privacy first way but first a bit about my past prior to them so as a born and bred Texan for the first 20 years of my life this was my material environment cars were equivalent to transportation and a mobility challenge was getting stuck in traffic so perhaps it wasn't surprising that when I founded a company our mission was to reduce the pain and energy waste of circling for parking I applied what I was learning as an engineer to develop some computer vision algorithms to collect real-time parking with cameras the company was called Park it and we worked with parking operators like the University of Michigan truck stocks truck stops in Oklahoma and we worked with their cameras oftentimes they already had IP cameras for security reasons and we would just turn them into data collection tools so it was a wild three years in parking and by the end I had not only become more aware of the wider world or mobility outside of cars but had come to recognize the growing relationship between city streets and the digital technologies that were moving from the online to the offline worlds so last year I joined the Newman engineering team and soon I realized that just like parking lots where if you want to get data today you usually manually count parking spaces many aspects of our cities are still measured like this as well so traditionally yeah it's it's a familiar sight right so if you want to get pedestrian counts or impact studies traffic engineers or interns will go outside with a clicker and a clipboard or maybe a fancy tablet and as you can imagine this process is slow inaccurate and expensive so oftentimes we don't have good data on how people walk in bike and streets and streets are then planned around what we do have good data on which is cars so in cities like the one I grew up in the transportation becomes prohibitive and dangerous for other modes so in joining new Mina one of the big things I learned was at the same technology computer vision can be shaped to achieve very different goals Newman I uses a camera as a sensor to measure all modes and streets and right now we focus on five main modes pedestrians bicycles buses trucks and cars but we're continually developing new object detectors like wheelchairs puddles scooters trash dogs you name it if you can see it we can measure it and we don't just measure it we also track the paths and behaviors of these objects but perhaps the most in Fortin part of how we do this is that we do it in a privacy first way all of our imagery is processed on the device our sensor and we do not save or transmit video the data that we do save and transmit is anonymous movement data so we don't need to collect personally identifiable information we do save a small subset of images a random image per sensor per hour that's used for algorithm algorithm validation and training but those are deleted after that process so this difference this differentiates us from other camera based traffic and smart city solutions we are committed to providing intelligence without surveillance as the camera networks expand across our cities we believe it's important that this infrastructure is built with privacy by design to reduce the risk of use cases for the surveillance state surveillance capitalism or targeted hacking processing at the edge is also a much more affordable and scalable approach to urban sensing so here's one of our sensors in Brooklyn our proprietary sensor is purpose-built for streets it's designed so that cities can easily deploy it themselves and they usually attach them to light poles with the same steel straps that any street side uses because we start at the sensor the data creation level we can be the gatekeepers of what data is and isn't collected so we can balance the value of real-time data without risking citizen privacy or data misuse so often our users are urban and transportation planners facility managers of the public realm and they access our data through a web dashboard which focuses on volume counts path visualizations and our what we call the new monobehaviour zones which is our way of spatially filtering data the behavior zones highlight another advantage of using a camera as a sensor instead of putting a tripwire or an array of sensors on each lane or rotor sidewalks you can draw behavior zones anywhere within the sensor field of view so one Neumann a sensor can do the work of a Street full of tripwires we're also developing some new metrics like speeds Duval time and direction another way people can access our data is through our API which can be used to build new applications or trigger services in the street where needed when needed so for example a retailer could use our API to get foot traffic alerts on activity in front of a specific store friend or a sanitation department can trigger a trash pickup when it when trash reaches a certain level on the sidewalk a common problem here in New York or an autonomous vehicle company may want the most up-to-date information on street conditions for the best routing we just released a public API sandbox with data from downtown Brooklyn so if there's any geospatial data junkies out there that want to play around with our data please let me know so we're currently deployed in 15 cities in three countries and we're about to have our fourth country our first Asian deployment we sell directly to cities but we also have customers in real-estate academia parks museums Business Improvement Districts cetera so how do cities use our data today so much of my work focuses on transforming our raw movement data into actually interesting insights based on what the cities are the questions that the cities that we work with have and I call this translating data into human so I'll start with some examples of desire lines or ways that people vote with their feet to identify the critical path in a certain area so here's one of our deployments in Nijmegen in the Netherlands and we color each mode with a distinct color and pedestrians are green bikes are blue cars ride and most the time the activity we see in this plaza is just really all over the place it's just to see mostly of green and blue which is always exciting to see but then one day we saw that the paths had turned into this instead and what had happened was a snowstorm as many of the urban planning folks out there know this is a great way to identify the critical paths that people use most often and in this case it happened to be that bikes were mostly going across this horizontal line at the top of the image and pedestrians were carving out these three main roads through the plaza and this data was important for the city so they could prioritize where to put new improvements like a bench or water fountain and to understand where potential mode conflicts were occurring between pedestrians and bikes that were crossing the same area but perhaps a more critical desire line use case happened in Jacksonville Florida which at the time of our deployment had the highest pedestrian fatality rate in any major American city there would be miles between crosswalks and what we saw was that the desire paths really showed crossing activity all over the street but it was really concentrated in this one area so this data could help Jacksonville identify where they should put a crosswalk so that it could match current crossing behavior most closely and now for a local example downtown Brooklyn last fall we deployed four sensors on the Fulton pedestrian mall we did one 100% sensor uptime from November to April about six months of data for 460 million data points of 26 million objects the customer downtown Brooklyn partnership or DBP had a lot of questions that they were interested in understanding what was happening at the street level but one big focus was vision zero metrics so specifically around road safety and traffic violations and we were able to look into some of them using our behavior zones so one big question was what is the impact of construction on pedestrian safety during the fall there's really construction all over Brooklyn so this was a big question for them and just for a little bit of background Fulton mall is a two-lane road of two-way traffic and there is just this constant negotiation between pedestrians bikes and vehicles for the sharing of this space and so when there is like a small disruption like a truck unloading for a few minutes or a bigger disruption like months of construction this really impacts how the space is used and shared and this is something of course that DBP knew this would have an impact but the real question here was understanding qualitatively what behaviors were occurring and also quantifying what the impact was on safety and as you can see here construction took place on the left side of the road and scaffolding was erected over a significant portion of the sidewalk and it actually shut down part of the sidewalk and as a result the pedestrians which are those green dots had to walk in the street to make their way down the road or cross in the middle of the street so we looked at activity in that yellow zone and what we found is that in the weeks that scaffolding was present pedestrians were 53 percent more likely to walk in the road and this information you know could help DVP understand where they might put a temporary crosswalk or better signage in the future when construction does occur and something that I found significant was that this problem really scales up when you consider the city of New York as a whole because at any given time we have over 300 miles of scaffolding another question that they asked was where do cars driving on Fulton pedestrian mall come from so this road is actually designated transit only but if you're ever around there you can definitely see private cars driving into Fulton mall all the time so anecdotally the customer had a theory that these cars were coming from a smaller side street Hanover place which is the red zone but actually we found that in a week of the 3700 cars that were driving into Fulton mall 84 percent were actually coming from flat intersection so this data they can use to prioritize where to put signage and another piece of information we shared with them was when the peak times were for these violations so this they could use to understand when best to deploy enforcement so to conclude here is our team we are passionate about cities and building technology to improve how we live and move in them this year we're working on some exciting new projects like detecting trash around Grand Central measuring exhibit engagement at a museum and analyzing mode conflict at intersections we are a mission driven company dedicated to empowering cities with data and enabling the future with an API for streets in this future we imagine that cities are more connected efficient and equitable for all in technology supports rather than inhibits this process to learn more we have some [Music] some more information on our website on our blog we have a detailed privacy policy explaining our rationale for how we do things the way we do and some case studies from all around the world and if you go to new Motoko backslash api we have our API sandbox there thank you very much Jennifer a lot to think about there more questions for you coming down the road the last speaker for this panel this morning is Vincent Lai and he's actually gonna complement the panel quite well because we'll have a discussion around the legal and regulatory frameworks when we think about digital materiality Vincent ley is a technology equity attorney at the Greenlining Institute where he develops Greenlining strategy to protect consumer privacy prevent algorithm bias and close the digital divide as an attorney late works with the California Legislature and public utility utilities commission to pass laws and regulations ensuring that low-income communities have the same access to the technologies and tools that are vital to economic opportunity when you look at the work of the Greenlining Institute and certainly with mr. lay you'll see that they spent quite a bit of time thinking about consumer privacy they look at the modern and digital form of redlining and looking at uneven access to technology and they look at that the role of cities and businesses play in this these using these technologies whether it be GPS broadband connected sensors cameras and so forth much of what Jennifer Dean just talked about so at this point I'd like to bring Vincent up to the stage and thank you all for having me like mama said I am a policy and regulatory attorney at the Greenlining Institute and what I do is advocate for ways to use technology to close the racial wealth gap so the green lining was for about 25 years ago and using an equity lens we work across a lot of different policy areas environment transportation technology with the idea of improving social mobility and Economic Opportunity and this is particularly we focus on areas that are red light for a little history lesson you probably may all know redlining refers to policies where the government created lending maps where they drew literal red lines around communities on the basis of race banks would evaluate mortgage lending risk whether to give small business loans based on these maps and you know areas that are red where it deemed hazardous and people living there couldn't get loans and this refusal to lend this refused little land is really key because access to credit yeah you know whether buy a house to start a business to fix your house to you know go to school is key to economic equity in the United States you know you can survive on income but wealth is what provides economic mobility what it lets you get financial security it gives something for your kids when you pass you know so these overtly discriminatory policies lasted for thirty years and from that time that federal government backed a hundred twenty billion dollars in loans ninety eight percent of that went to white people so if you put that in another way for thirty years Americans were white Americans were amassing intergenerational wealth you know to start homes buy businesses out buy how homes start businesses and you know send their kids to college and you know people of color we're locked out of that opportunity and that dynamic is the root of today's racial wealth gap right so for every dollar a white family has a black family has eight cents you know these disparities are not accidents these are been deliberately caused by government policies so as we think about how to reshape our cities using digital tools and new technologies more data it's important to keep this history in mind so to me you know the potential of digital urbanism right this new way of understanding the ebb and flow of cities using technology and data I think it's key to use that understanding to undo the effects of redlining but before you can do that let's focus on making sure we don't use this data to reinforce and recreate patterns of discrimination and an example I like to use our Amazon's Prime Delivery Service maps you know a couple years ago Bloomberg did investigation of where Amazon provide services right yeah and then this is what the delivery maps look like so if you're black you are twice as likely to be live in an area where Amazon decided you don't get service you know so Amazon says it doesn't use race to build these maps and I agreed I believe them but the point is that the legacy of redlining and the geographic aspect of racial or the racial wealth gap means that you don't have to use race data to discriminate against people of color now I know this isn't on the same level as you know denying bank loans is it's denying you know same-day delivery service but the takeaway to me is that you know some geographer a data scientist or engineer one of the largest tech companies sat down and used all the Lay's latest data and tools and managed to make a map that discriminates in the same way that banks did in the 1930s and you know this is an example of algorithmic bias you know you can call it data analytics you can call it AI but to unpack Atlas I'm just gonna really quickly talk about you know what are algorithms and where are they used to me for the purpose of this discussion an algorithm is just a set of you know a tool or computer model that processes data according to a set of rules whether created by humans or by computers and they can automate complex tasks and then why are they use is that we just have so much data volumes today you know there's are some examples up there about how much data they have and we want to derive some value some insights you know as data scientists say so the idea is we can use algorithms to do that work for us and then you know the insight could be something like you know let's look at billions of right share data from uber and they can find out hey woman more than five miles away from home you know at night with a low battery or you know 25 percent more likely to accept a surge fare you know that's that's the kind of insights that we know we're seeing this data being used for and in terms of the data that's being collected you know that's relevant is you know like we've seen it's cameras it's urban sensors it's mobility data its information from tweets and Facebook posts likes there's a lot of it and they're being used everywhere you know for predictive policing you know for whether or not you get a bank loan whether you get a job and you know what this happens what happens from this is data driven discrimination you know so we have new ways to analyze and process data to find patterns but we have organic bias but at the end of the day because humans create the algorithms humans influence the data that's made and you know we can see that when we look at you know financial lending algorithms it turns out you know even if you don't look at race even if you don't have a person involved online lenders still discriminate against people of color and they make them pay more even when controlling for you know the same economic factors like income you know for another quick example let's look at the number of you know men and women and tech companies can you imagine what will happen if we make an algorithm look at hiring data from you know say Amazon to determine who to hire well we don't really have to guess because you know Amazon made that tool and they found out hey that's biased against woman and you know it's easy to see why because you know for 10 years Amazon was hiring mostly men and the algorithm learned hey men are the ones that Amazon likes to hire and to promote so we should hire men and you know the takeaway from that is you know these tools and data you know really like will be analyzing this data and using algorithms you you can inherit really undesirable human traits like bias and discrimination you know and another aspect of this is you know when when you're looking at data you need to make sure that your datasets are representative and you know an example of this is you know facial recognition algorithms are really bad at detecting black faces and that's because the datasets that companies use just don't have enough black faces in them and you know that's an oversight that we you know a green lighting may want to make sure that when we use these technologies to benefit people like that doesn't happen and you know that gets into algorithmic equity right so how to use data and algorithms in ways to reverse the effects of redlining and close the racial wealth gap you know and in the urban planning and mapping in the context here today you know it's really important because the biggest factor affecting economic opportunity and social mobility is the neighborhood you grew up in so if you improve neighborhoods then we can improve and address the legacy of redlining and so how do you do that and you do that by optimizing for equity right you need to ask the right questions from your data you know we need to have you need to guide your data use in a way that has value judgments that prior equity and you know one really local example I like to use is you know the Oakland bike plan right when Oakland try to you know create new bike lanes back in 2014 they had a lot of community opposition and when their they restarted that effort this year they had a lot they had a very equity guided vision you know that asked who do we want to build this for what do we want to do what are our goals and you know one of those is affordability you know the other one is safety and these are things that came out of the collaborative process and from asking the right questions of the data the next way we can do this is to ensure equitable resource allocation so we can use data tools and policy making maps in a ways that identify disadvantaged communities and target those for greater investment right and the first way to do that is to have the tools and you know green-lighting sponsored legislation a couple years back where we made a mapping tool that looked at data from pollution sensors health records traffic data socio-economic datasets to determine if a community was disadvantaged and this is what happened you know we created in effect a redlining map without using rates which is really you know important from a legal perspective because you can't do that when you know you're targeting investments and you know the the next step you know after identifying these disadvantaged communities is to build you know an equitable resource allocation mechanism and what we did was we we passed laws in California that made thirty five percent of California's you know nine billion dollar cap-and-trade fund of thirty five percent of that money is going to communities identified as disadvantaged I so you know the effects of that are that you know we've had five hundred million dollars going to building charging infrastructure in communities that if you just looked at that historical data you're like no well you know low-income people don't like electric cars so we shouldn't build charging infrastructure that but you know by building equity and design you build a way to get to a future where you know low-income communities can use electric vehicles they can charge their cars in their neighborhoods so we think that's been really effective not to do that and you know just a contrast in the calenviroscreen map that mapping tool that we made you know with the you know Amazon Prime Maps you know in one we have a mapping and data product tool and process apply it in a way that denies services to communities of color that have been historically redlined and in the other way we use mapping and data tools in a way to drive greater investments and economic opportunities in those same communities you know to get back to the representative datasets issue you know like there's a big push to use more mobility data from uber from live from ride share and it's great right you can have a lot of insights but if you think about the digital divide you realize that you know the people who aren't representing those datasets who much less likely represent adversity assets are low-income people so if you are using that data you're gonna prioritize you know the needs and the the patterns of the rich over the poor and like that only reinforces redlining if you don't use this data correctly and one big part of this is that we have all this data but the there's a temptation to say oh hey we have an update I we don't need to actually go out and do the expensive process of talking to people and you know that's that's wrong that's so wrong and you know the Oakland bike plan you know I really like this because it was a very equity forward process and they they said this about actually talking to Oakland ders it was we understand relying solely on quantitive data over the knowledge and experiences of marginalized communities can lead to incomplete decision-making and you know and that's true because not everything comes up from the data you can't know about someone's lived by looking at you know an Excel Excel spreadsheet you know luckily we you know there are tools being built to lower the costs of citizen participation lower the cost of outreach but you know if you remember the digital divide people who don't have smartphones people who don't have internet aren't going to be able to use these tools you know so you really need to follow up this with follow up this process with you know actual listening sessions you need to go to where people are to gather data and the Oakland bike plan did all of this you know they had very data-driven process or they you know surveyed thousands of people but they also went to you know supermarkets they went to you know encampments they really talked to people all over the city to really understand and get feedback on the insights they were getting from their data and I'm not gonna touch on this too much because I'm running out of time but you know there's so many privacy concerns right so you know like the tide walk labs said you know no data for data's sake no technology for technology's sake and you know I really agree because you know with all this information you get more precise targeting you can really prey on vulnerable people and you know you can get really deeper you can start analyzing people analyzing people's like you know psychographic and behavioral results and get really creepy with it and you know you can see the end result of that you know in in China right there using facial recognition software - you know repress and limit the the religious freedom of their weaker minorities so as we think about where to use these tools you know it's important that we don't reduce people the numbers and we don't use these tools in ways that increase redlining and discrimination it's on right so you see that unfortunately the fourth panelist Mimi Schiller is under the weather so at this time I would love for the panelists to come up so my first question is is for you as first your bill you're starting with the community that or a piece of land that didn't really exist right in terms of a community but the first practical question is around financing so you're obviously a private entity and you're working with the city and so forth can you talk about just the financing around this type of development and just the basics of it I mean how you how it all came together just who the partners are and what not I mean that's actually something that's sort of underway at the moment because we handed in our proposal just in June so the kind of economics of the site are actually sort of in deep discussion with Waterfront Toronto at the moment but certainly we would be they've requested us and we're happy to work with local development partners as well so we'd have probably other vertical developers working on some of the buildings of the site but you know in some ways it's not unlike just normal real estate transactions where the city itself needs to decide sort of what they're interested in his outcomes for providing that land for for private development and what the value that of that is to them because certainly developing and this is less about some of the digital elements but just developing that kind of district infrastructure for energy certainly geotech and things like that is it's very expensive it's more expensive to develop green energy infrastructure than the not it's more expensive to provide affordable housing than not so to be honest in terms of the economics it's it's much like any other development project in that we need to come to a balance between a return on the project and what the city is looking at for the value of the site and what they're looking at in terms of what they'll get is outcomes from the site so what I will say is we won't make money on key side it's a first signature project we're more interested in being able to work on the implementation of so many new things and so we're actually not the economics don't pencil very well for us but we are we've proposed sort of further development on the next neighborhood as well to kind of balance that return but we're looking at certainly even in Agra sort of like a below level normal return as a real estate company would okay so my next question is probably on everyone's mind is about data and I obviously you know you're a private firm and if I was to be the devil's advocate here I'm sure you've heard this question a million times you know it don't you doesn't seem like it could potentially be a conflict of interests if you're collecting this data how do you also evaluate the project right so we know that these wonderful things come along and you have excellent metrics I've looked at some of the things online but who's doing the monitoring and evaluation and the second piece tied to that is who owns the data and how is that data being used and I'm kind of cheating here it's not fair but it's also tied to then what happens at some point if through the monitoring evaluation process we find out that data's been misuse or whatnot what are the repercussions for that so I know it's a three-part question and I can walk you through why don't start what are we going to start just with the monitoring evaluation so you are the developer you're working with the city certainly and you have a number of metrics and say here are goals and their wonderful goals right yeah so what happened making sure is it an outside entity or is it yourself or is there a firewall between that to understand if you're meeting those goals yes so and certainly we don't believe it should be us in the proposal to Toronto I mean if this really gets to that question of long-term capacity building with government because we shouldn't be self regulating that but to be able to monitor and measure that government's capacity to be able to do so so we're actually in that this is one of the big things that were debating with Waterfront Toronto now because in the proposal we actually proposed sort of a series of new entities I mean they would still be government governmental entities but there would be entities that would be stood up to really build up that capacity to be able to have the oversight for what was happening on the project you know they're they're slightly less comfortable with that which i think is actually a great thing because what they're saying is our government agencies as it exists today should be able to build up the capacity so let's not think about new entities let's think about which parts within the existing agencies can build up the capacity to be able to do that in the implementation of that practically we also have proposed a number of different tools so for example for energy management we're proposing to build a tool that we would then hand over to the government that would actually be able to sort of aggregate the energy data and show that we're meeting the metrics so there is a need to be able to you know very practically to to address how do you measure and make sure that you can measure if you're proposing these kind of thing proposing metrics that we want to be held to was the first one second question was data who owns the news has it yes so so this is what I touched on briefly in terms of data being a public asset so we don't believe that any entity should be it certainly no private entity should be able to monopolize the data so in in all of the infrastructure designs that were proposing essentially the data would be there would be data standards that would be signed off with government so it literally the standards of how do you organize and provide that data the data would all be provided openly so whether it's an urban data trust or it's a existing government agency some body within an agency they would have they would essentially steward that data data that's coming out of all of the different systems would be provided to that organization so then it could be openly used by by any other entity whether it's government or other private entities to look at other sort of innovations on top of that data so that we think that's a incredibly important and then the third question yes I mean that that's something we're also sort of actively talking about now obviously you need some sort of practical oversight you need to be able to audit you need to be able to prove the that's not happening and you need to be able to hold organizations accountable for it so I don't have a quick answer for that today because it's something we're sort of actively working on but that's to say that we agree that it's something needs to be actively worked on and you need practical solutions for that fantastic I appreciate it so Jennifer why don't we switch to you I think maybe you will be able to have a conversation here with Nerissa certainly in your work we've talked about behavior zones and you collect analyze data right and sort of how we can understand our social environments how we can understand the built environment the way people go throughout their daily lives can you talk a little bit more about the behavior zones and who are some of the people that have been coming to you to use your data or who have you in terms of partners and whatnot sure um yeah besides I guess to address your last question besides our direct customers who are usually city officials so do tea or various other agencies in the public agencies we have worked with universities before so we did a program with new lab that was a collaboration with some New York cities and we did some data sharing there I think definitely a big question on our mind is how to best open the data to the communities that were actually monitoring in because completely agreed this data is on their community it's something that they should have access to so that is kind of what we've been exploring with our API sandbox by opening up that downtown Brooklyn data well let's get that's a good spot and then the behavior zones yeah I think that goes in pretty well with the data people are interested in different aspects of the scene so for example at one of our deployments in Grand Central right now the partner we're working with is completely interested in detecting trash which is great we're happy to do that but there's a lot of really interesting activity going on in the zone as well for example one of our sensors has a great view of dedicated bus lane so we've had conversations with transit center for example with a lot of the new better bus action plan work to see if we can monitor some bus metrics about delays and maybe some violations of cars using the bus lane so I think that there's a lot of opportunity especially in some of our urban environments to make that data and especially the behavior zone data available for more people that might be interested in using it great that's a great segue you actually touched a little bit on my next question which was on community engagement how is the public interacting with this data I I think you said you were in 15 cities and three countries so how are you using that aggregate data to may better inform the work that you do or is there anything that you can say that you see that's cutting across either themes or some kind of similar challenges I know it's a good question but I was just wondering if you can speak to that a little bit yeah that's really interesting we haven't done too much comparative analysis yet but one big question that does come up especially across our urban environments is mode share versus road share so this idea that how much space do we allocate to cars versus people and how many people are actually using the space so in New York for example I know New York d-o-t recently did a study where they found you know the majority of space it's definitely allocated towards cars but we have so many pedestrians and cyclists and transit users that are using this space so how can we use the data on the number of commuters of these other modes to make the case that we actually need to make more space on the road for these other modes so it's definitely interesting to compare across New York for example with some of our pilots in the south like Jacksonville or New Orleans where maybe they are a little more car friendly another thing that we're starting to look at is modal interactions so how pedestrians might interact with cars or bikes and something that we know in New York were all very aggressive commuters so one question is about yielding behavior we are going to be deploying in Portland soon and we that their driver characteristics are more willing to yield to pedestrians so this is something that we've heard through conversations with the city but something that we'd also love to actually measure quantitatively to see how that might actually affect safety I wanted to say how excited we are about technologies like Numa because in all of the use cases we're looking at for streets or for public realm these days with the development of technologies like this which are you know protecting people's data privacy we can actually implement every single use case except one and not with with private private data essentially you know everything that we're looking at counting the cars coming in for pick-up and drop-off whether it's that or whether it's park usage it can all be done without collecting any sort of personal data from people and scrubbing it before that data goes anywhere so that's really we think it was a game changer in terms of being able to use tech responsibly in public space yes great all right why don't I okay so in I'm not a data scientist so isn't it possible that you can take that anonymize data and it's it's really easy to de-identified data these days and you can't you you add that to someone's location data from their cell phone maybe because they have Google Maps open and couldn't you like identify people and where they travel and put all that together and look at maybe that person doesn't yield a lot and then you get like an email from the government saying hey you didn't yield and they take that data with your you know behavioral data to craft a message in a way that is like most designed to make you feel bad you know is that is that possible yeah it is yeah so but I mean I guess that's the point and I know everything was shallow and quick but that's the point of having a process like a responsible data use assessment because in that assessment you're committing to what you are and what you're not doing with the data that you know that's would be a regulated assessment so if you said you're gonna do XYZ with the data you're not doing ABC or you know you're not taking it and then well you know matching with other data sets to identify people and and yes then you have to make sure that people aren't breaking what they've said they're gonna do but it's it's providing that transparency as to what the data is going to be used for and committing to not use it in other ones so that's why those processes and that oversight is so important because you can do that kind of stuff of course well Vincent why don't you continue along those lines I could just go sit down with a bucket of popcorn just watch it why am I here no this is great so I mean you've talked a lot about you know you started out with redlining and certainly it's a very serious issue we know and and you know when we thought about what some of the solutions obviously it was legal and regulatory where the be the crew community and Reinvestment Act or the fair housing laws it's just discriminatory laws on the books and so forth and then you know anything about my when I started out with framing today's discussion the time at MIT were talking about the digital divide and who has access to technology and so forth and now you've talked about algorithm algorithmic equity which is an important concept so I'm curious about solutions I know you talked a little quite a bit like the Cal and viral scan certainly as an example of how you can use technology and information to sort of better inform the public and engage with people but when you think but some of things you present it not only going from the legal and regulatory but also down to the production and the innovation of the technologies of themselves so I was curious of how at Greenlining and the work that you do are you partnering with the private sector whether it be Amazon whether it be Twitter Facebook what have you to KY to address some of the issues that you've discussed right so I think we work a lot on capacity building in government agencies right so we want your regulators your you know your government your legislators to know how to process these data's they don't we don't want the regulator's just take companies at their word right so that's one big part of it another part of it is creating the legal framework so California in January is gonna have the strictest privacy laws go into effect and we hope that becomes the standard it's kind of based off GDP our in Europe and that gives a lot of people more control over their data and their privacy but in terms of you know their of actual solutions you know I think we really need to change a lot of the civil rights laws that we have you know because we have laws on the books that prevent disparate impact right so if you see we look we've worked a lot of bankers all right we look a lot of bank data and you know getting more transparency and data is really important you know we there was a Freedom of Information Act with you know banks and insurers and they found out that you know insurance companies were charging minority neighborhoods twice more than twice as much despite having the same driving records right so we need first we need data from these companies and we need the capacity from our regulators to analyze that and that's a really important part but they're also like within companies are statistical techniques to address you know racism and redlining in the data but using a lot of them so say you find out your banking algorithm discriminates against people of color you can't legally fix that by saying okay we'll give you know all these black people and like you know all these Latino people a bonus in their algorithmic score that's also illegal because that violates the Equal Protection Clause right so we have two really competing laws that say like once you recognize that your your data and your results are racist it's illegal for you to have a race-based way of fixing it so you know that's why cow enviroscreen was an important tool race neutral but still generates equity for you know communities of color but I think in a larger scale we really need to rethink our you know civil rights laws okay so I I have a question for all of you and it's tied to the earlier discussion is each of you brought this up and when we think about planning development design all those things we also many of you oftentimes discuss process and I'm curious not only with your partners whether it be government entities the private sector and so forth but what is your process to ensuring if you think about today's society and certainly strong market cities at Toronto and or Beijing you in London whatever it is the level of inequities right and you all discuss in many ways the importance of creating either a call you've mentioned quality of life you've mentioned healthier communities economic opportunity educational opportunity all these wonderful things but through the process that you're working on can you talk about how you're actually embedding that in the work that you do through these processes sure I mean I think the affordable housing example I gave is maybe the most obvious one but you know I think a typical developer if they sort of developed the kind of mechanisms to build at these lower rates would simply take that money and it would be you know profit on their bottom line but we don't we really are not interested in building sort of wealthy enclaves that's what's got you know that's a huge problem in the last 20 or 30 years in cities as populations have moved back in we're really interested in building communities where everyone can live so you know that's part of the reason our project doesn't pencil out and you know for the first one is that where we're not interesting and building something that's not going to have that component to it and so we we think by being able to provide as much affordable housing as possible we'll end up with a much more diverse neighborhood and that'll make it a stronger and more interesting and vibrant place I mean this is a question because I mean I guess our work is to get other people to embed equity in their work right so that means that's going to you know the California Senate it's going to the FCC it's going to policymakers legislators and building coalitions where we can pressure these you know decision makers to implement the laws and and the rules that we need to kind of force people to consider equity and in their design of things and you know this is part of it right you know it's like teaching people about what are the impacts of all these new technologies and I think it's an iterative process but with enough time we can get the momentum we need to really shift the legal landscape the regulatory landscape so that you know by design you embed equity into your you know data use Jennifer yeah I think the first kind of challenge that Newman started or hope to address was to look at equity of bode so we need to you know increase our data sets not on you know car users car owners but also other users of the street okay so that was a starting point I think probably the biggest procedural decision we've made is that we do focus on working directly with cities so in that way we hope that the groups that we are working with are representing their citizens interests and taking into account the context of what really goes on there so for example I recently heard a presentation by the Auckland IOT about increasing bike use there but a big point that they made was just that I mean in many of these neighbourhoods access to transit or bikes might not be possible so not only is car do sometimes necessary but it's also a big part of culture and identity so I think just having that local context and understanding it's not like always cars are bad or always transit is the solution but embedding the data in the context of the community to tell the stories and decide what metrics are focus on is a big focus of ours fantastic so we have about 10 minutes left and I would love to hear some questions for the audience there's a microphone going around this gentleman here thank you very much I'm Jack I can ma'am I am the founder of something called gizmo which is about data geographic information systems this is a question for mr. Lai the process of redlining was reached its peak in the 50s and early 60s but there are precedents that help that happen that go back to the 1920s particularly I'm thinking of one the informal steering of the of Realtors by race and color and ethnicity whatever away from certain you away from certain sellers and landlords to other sellers but most importantly I'm thinking of something called restrictive covenants which happened all over this country and in New York City in the 1920s where land was developed and restricted to just white people or restricted against people of certain faiths Jews and Catholics and certainly people of certain colors half of the city of Seattle was laid out in such a way as North Seattle could not in this one ease could not be sold to people who were black New York City Jackson Heights garden apartments could many of them could not be sold to people of certain faiths of certain colors and the same thing was true of the coop apartments on Fifth Avenue Central Park West and Park Avenue that's a precedent for something that creates the blockbusting any comments on that did you study restrictive covenants yeah so I mean I think restrictive covenants are the other half of redlining right so you put those two together you get urban blight because you know in black neighborhoods there was a lot of white owners right and those white owners knew hey my residents you know my renters can't move anywhere so why do I need to fix my house I'll just let it fall into ruin and you know the black people who actually owned homes in that neighborhood they saw their property values decrease right and because all the houses around them that were owned by white people and rented out to black people were falling apart so that creates urban blight when and then when they created the interstate system city planners are like oh you know these areas these black areas these red line areas are blighted they're you know they're slums they're ghettos so let's just build a freeway through them and you know that created a lot of urban issues that we see today you know suburban sprawl you know pollution so all of those things go into why you know the restrictive covenants and redlining going to why no one has wealth today and in terms of solutions you know the equality of opportunity project that found that you know what neighborhood you grew up in is the biggest predictor of social mobility one of their primary solution is that we need to reintegrate neighborhoods and to re you know because restrictive covenants you know codified segregation we need to reintegrate neighborhoods and I think the challenge today that we all deal with is how to do that without you know displacement without all the negative effects of gentrification sure question what's the role of the courts especially the federal courts Supreme Court that is are there any cases yeah so I I kind of touched on this earlier there was a there's a Supreme Court case called Rizzo that pretty much made it very difficult and if not illegal to correct for algorithmic discrimination on a race-based way so you know that's the how the court interpreted the Equal Protection Clause and the disparate impact laws that we have on the books and that's the law of the land until we change you know the way that we think about disparate impact and equal protection so I'm Renee Sieber I'll be talking later today so I have read all 400 my question is it's a call to action so I'm really excited to be here thank you what're we mostly urban planners gonna do I'm in geography but my PC hasn't urban planning what are we gonna do after this conference so much of the time I see the plans of action being outsourced to lawyers as the litigation and laws are the only solution and I don't know maybe it's a structural defect in planning I am a planner but you know that we speak to the powers that be but I think so often we're just seduced by the technology and oh the data is out there there's nothing we can do but where is the Gandalf and all of us you shall not pass you know we shall not use this data set because in an error of a automated decision-making there's no such thing as anonymity anymore excellent question thank you why don't all three panelists I respond to that the core of the question being what what should we have should we act urban planners um well I mean I guess that's why I wanted to present the material in the way I did which is to say that you know digital technology and data it is potentially a mixed blessing and so when we think about developing places we have to think about them holistically and some of the most important things you can do are not related to do things and we have to find a balance of all of that and then we have to do it ethically so I mean personally I think that's what we're trying to do at our firm it's often hard for people to leave because we're a private company but we're a private company of people who come from urban planning backgrounds and governmental backgrounds so we're a mission driven company as well and I think we are actually trying to do that by the by the project itself and the way that we're designing yeah I mean I think we rely on lawyers you know it's job security I guess because urban planners you know it's kind of the middleman right between the data scientists and the plan and the policymakers right and you kind of need groups like green lining and you know other lawyers and to translate the learnings and and the insights and the negative effects of that into effective policy and yeah I wish that wasn't the case and you know I think of some solutions to do that is like we always advocate for you know diverse and inclusive teams as a way to see you know your blind spots all right because I don't think you know a lot of these cases that I present it no one was you know I don't think people went into it with like hey I'm gonna go discriminate against black people all right but there's blind spots you know like hey I'm gonna use datasets that only have white faces but when everyone on your team you know it comes from the same background you kind of create blind spots that prevent you from self regulating yeah as someone in tech definitely very very well aware that the fact that legislation will be lagging right the algorithms the datasets will be produced and developed and the pace is just not something that can be matched I think legislation definitely has its place and I hope I mean we are seeing a lot of anti facial recognition legislation happening now but of course there's more than just faces that can be used to identify a specific person right there's a lot of different forms of data and regulating every single one especially if every single city has to make their own legislation it's just not really something that is going to protect citizens so I guess as a company that does work with planners a lot of the time I think our big ask is if we can find ways to work more closely together because I think for us we think of ourselves as hardware and software providers right but the data we do create and collect should be shaped by both planners and hopefully citizens too so I think there's amount of guidance that that should happen in this this relationship as a professor it's so hard to be quiet on that questions rate question I'll just say think about what change happens and I would say it's a multi-faceted approach takes lawyers can be organizers developers architects so forth but anyway these are all great responses excellent question we unfortunately don't have time for any more audience questions but I do want to ask the panelists one last thing when you think about your work your individual work whether we had sidewalk Toronto or just sidewalk labs in general what is the one challenge that you're grappling with the most what keeps you up and thinking about the future of where we're going when we think about digital materiality and equity all health all the things that you've laid out for all of you just sort of 30 seconds if you can I know it's a big question but and we can start this order you know I always feel like arrested we're always going to you why don't we Jennifer why don't we start with you and we'll work our way that way if you don't mind I think the expanding camera networks both fixed and mobile is one of the most terrifying things for us so not just CCTV cameras that we already know are all around us but the phones that we carry the invisible sensors that are around us these are increasing and in some for some kinds of data why do I think we do have some protection but for others we really don't and so I guess what we're hoping to see more of is a stronger either legislative or even industry focused set of bebe code of ethics or set of standards that can help set a common understanding of what should and shouldn't be done I think maybe what should is a little fuzzy right now but what shouldn't it is becoming to be clear thank you yeah well keeps me up at night is that like we don't know what we don't know right like the all this investigative reporting was behind the examples that we found there are whistleblowers you know so that's how we found out that insurance companies charge you know black people twice as much this is how we found out that open datasets where how we found out that banks were discriminating against you know people of color so the thing is a lot of data isn't open and it's not free it isn't transparent there's no accountability so I you know I want to build mechanisms where there's a some sandboxes regulators can look at the data without you know infringing on trade secrets and you know all these the what private companies complain about when we ask for transparency so that they can actually look at and find out what's going on and what's going wrong and you know just even the threat of that can can ensure compliance so that's what I hope to do thank you um I mean I think my answer would be very similar you know it's amazing when you look at what's already implemented today and as I was saying earlier the kind of invisible layer the invisibility of all of it on the street and out in public spaces and so how can we work to really provide that transparency I mean also for me I think as we've what keeps me up at night is as we it's a lot about equity as we think about what we're proposing to develop in this neighborhood there are in the vast majority of the cases there are we can collect data anonymously we can protect people's privacy we have no interest in surveillance cameras but some of the larger infrastructure systems essentially would run the district and so is there truly if you come and live in the district you can choose flow there if you're a wealthy person but you can't if you're going to come and live in affordable housing so that raises real ethical questions of equity and if we run an energy system that has a I built into it you can't necessarily opt out of that and so we're really grappling with what does that mean like could we provide opt at meaningful ways to opt out of that that wouldn't penalize somebody and so to be honest though the ones I think about a lot because you know I'm happy to say that 95% of what we're doing is going to be anonymous it's like I truly ethically believe there's no issue with it but there are a couple of these use cases where because I'm so focused on environmental protection and and that being incredibly important to me the trade-off it well it doesn't have to be the point is it shouldn't be a trade-off between aiming for climate positive and having systems that can help you get there but protecting people's rights and inequity so that is happening fantastic so with that we'll end this panel discussion but I want to thank Jennifer Vincent an arrest for a thought-provoking presentation and discussion and thank you very much and I would encourage you to look at our colleagues work Mimi Schiller who could not be here today because she's under the weather but to see the work that she also does and how well it complements the wonderful work of our colleagues on stage today so thank you so much [Applause]