The following are the outputs of the real-time captioning taken during the Fourteenth Annual Meeting of the Internet Governance Forum (IGF) in Berlin, Germany, from 25 to 29 November 2019. Although it is largely accurate, in some cases it may be incomplete or inaccurate due to inaudible passages or transcription errors. It is posted as an aid to understanding the proceedings at the event, but should not be treated as an authoritative record.
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>> ALINA WERNICK: Is this on? I suggest that we will start now. I know after lunch, it's always ‑‑ it can be difficult to proceed to the next event. But we're very happy to see you here with such large group.
The topic of our workshop here is data governance for smart mobility. I'm still waiting to see my slides. But I can just ‑‑ while ‑‑ okay. Excellent. So here you also can read the title. And if I can go to the next slide, so that the flow of the workshop will be such that I will quickly introduce you to the topic. Then Professor Max von Grafenstein will tell us our research project on data governance and what we have discovered so far. Then we will have three very interesting impulse presentations on the topic of smart city mobility and data governance in that context. Then we'll proceed to the interactive part of our workshop, that being round table discussions on the topic.
Here I should note that the participants online and online participation is feasible and possible and very much encouraged. So you may already now make comments in the chat. And during the round table discussion, there will be a parallel online round table discussion on this topic. However, I must note in this workshop we will not take calls for speeches from online participants, but we very much encourage making your comments and giving us feedback in the chat box of Zoom application.
Okay. So introduction of the topic, so the reason why we wanted to organize this workshop is essentially, I think every one of us have witnessed the two megatrends in the society. First one is global urbanization and exponential growth of cities which often introduce problems with traffic, such as congestion, environmental concern, questions about accessibility. And at the same time we see a fast rise of data driven innovation such as Internet of things, big data, AI, and technologies that essentially are based for processing of data and using it for novel purposes.
This brings us to smart city mobility. So could there be an answer to the problems of urbanization in organizing mobility in a smarter way in a more data driven way, and can this be done in a manner which is bringing us more equality, efficiency, and sustainability.
In our opinion we need to acknowledge when we think of smart city mobility, all the solutions they rely on collection and processing of vast amounts of data. Sometimes it's nonpersonal data. Sometimes it's data collected from citizens that are in the city or in objects of transportation. We believe that data governance is prerequisite for smart city mobility that respects our right to privacy and data protection but also other fundamental rights. And also we take a view that data governance also supports the fulfillment of UN sustainable development goals in the context of smart city mobility. After this brings balancing act, for example, for fostering innovation. On the other hand enabling equality in cities.
We have also noticed that when we talk about data governance, especially in the context of urbanability, it's a dialogue between different stakeholders. We have on the other hand here municipalities who have interest in more efficient public transportation. There are interests of citizens, only one person in the picture but actually there's a large variety, a lot of different interest groups. And essentially transportation concerns everyone who lives in a city and outside the city.
We have big tech players who have traditionally collected a lot of data, and then we have automotive industry and other actors in the area of transportation who have now encountered digitalization of the industry. These are only examples of the tensions that might be present when one organizes smart city mobility solutions.
And next, I will give floor to Max von Grafenstein.
>> MAX VON GRAFENSTEIN: Before we get to the keynote speakers I will give you a brief overview about the reason why we are now organizing this workshop on data governance in the con be text of smarter city mobility, because actually Alina Wernick and I were assessing the question of data governance almost two years in very different context.
And the overwhelming question there is always how the different stakeholders in certain contexts like the advertising context or automotive context or smart city context, whatever, how the different stakeholders involved have actually coordinate to unchain their data driven capacities and on the other hand simultaneously to protect effectively against the risks of these innovations. This is the overarching question.
And in order to give you an insight or an impression why this question is rather complex, I would like to give you an example of data protection law. So the complexity stems initially from the fact that we are all now deeply embedded in highly dynamic network economies. And this means that all the different stakeholders rely on each other, and this brings all different levels, like the technical, the organizational, the normative level. This makes these coordinative efforts so difficult.
To just give you an example from data protection, most of you or many of you probably know that this very new famous European law, the so‑called the General Data Protection Regulation. There we have a very new requirement in it which is called data protection by design which requires data driven companies, so let's say data controllers, to implement the legal requirements into the technical organizational design. The tricky thing with this is that the companies, the data‑driven companies are primarily legally responsible to implement the technical conditions, but in fact they rely on other third‑party IT technology providers.
So, but these technology providers are not ‑‑ at least not fully responsible. So both parties have to interact in order to finally meet the expectations, the privacy expectations of the citizens. So this is just an example.
So we did this research for almost two years in different context and always analyzing the different analytical dimensions. How do they interact on a technical level and rule making level and shaping the organizational level in order to connect both levels? This was always the question. After two years, we finally came up with five qualities of data governance models. So these are very generic models but it might give you already an impression on how the results of our interactive workshop later on might look like. So we came up with five very generic but qualities of data governance solutions. So the first one is, of course, is the single source solution in data protection law, this is the constant based model where each must consent to the collection and processing of its or their personal data which, of course, has some advantages and disadvantages. The advantage is it has full control but in fact it does not because it's overwhelmed about the abundance of consents required by them. A more feasible solution may be the third one, the data pool where different controllers interact with each other and collect all the data and store it one common shared data pool. This is a much more scaleable solution but in terms of data security law, this is more risky because in the moment one hacker breaks in. Then it's a huge security breach.
So we have more compromised ‑‑ we discovered a compromise. This is the data clearinghouse where the data is not stored centrally but where the different data controllers somewhat interact through standardized APIs, maybe also on the rule making level and then they only share different data for certain purposes.
In the very end we have the future picture, the fully decentralized models. For example based on a blockchain, as you already know it from the decode project in Barcelona.
So this actually already our first impression. Alina will give you now before we come to the key note speakers a brief overview on how the interactive workshop looks like.
>> ALINA WERNICK: I will introduce the workshop after the impulse presentation. I will introduce our first speaker. Mr. Choi is the coordinate of Korea government alliance. He conducts algorithmic decision making and relationship of infrastructure and mass surveillance. We'll give floor to him.
>> EUN CHANG CHOI: Hello, good to see you. As you know South Korea is a tech savvy country in the world. To hear about networks South Korea is more faster and very acute information chains. Network comes with a privacy concern because it collect many things. I assure you somehow the combination of privacy networks and smart city are going to be a very dramatic change over your lives.
If you come back to maybe 20 years ago, when I was in college there was no email address, no internet. But today's it's changed. Is that the same thing going to happen? This is another new phenomena of the wave. The global use there are several things. As you see, just please, as I strongly suggest you remember it mostly consists of sensors, IoT devices collect everywhere even though you don't recognize it. In daily life we encounter 500 (?) every day. This is huge. Second there is a network, there's 5G, 4G, LTE, in case of Seoul has full public Wi‑Fi. All data goes to one data pool as professor Max von Grafenstein mentions. It's difficult in South Korea. It comes everywhere. The third one is a cloud‑based infrastructure. We call this data hub or data clearinghouse. It's inevitable.
The important thing smart city is not considered by government officials. Together with some ICT company, Google or Samsung everywhere. It's a lucrative company in the ICT companies. That's why they're going to design smart city infrastructure. The problem is they have no incentive to protect privacy. That's our concern. That's what the Civil Society voice should intervene there. I strongly believe.
The integrity from the city center. Even though somebody control this, this is a problem. Who going to control? Who going ‑‑ who is allowed to control every citizen's privacy? This is a key question.
Here's a breakdown tech elements of smart cities. This IP network, Cloud, I already told that. Okay. Here's ‑‑ this is mobility of the grid, smart mobility there. All of the smart city, this is the mobility which is the topic of today. As you see, there's ‑‑ there's Internet of things device everywhere. Here's the case in Seoul. High speed communication network all over the world like here. This is much more highly dense network amongst all of Seoul. Seoul is a vast network of connected society in the world.
Yeah. When it comes to traffic, they use this data, Topis, CCTV, all this together. That means people just cannot go back to home in the night and they just ‑‑ the city analyze big data and they made a new line (?) here's the expansion of the shared parking system. When I went to New York City I travel down to find a parking lot. In this case, IoT device just notify where it is available. Here is AI‑based taxi. Even though AI we can say is analysis of data. The functionality is AI. This is a better way, taxi because of data we have. This is smart city have some crosswalk. That means if there is somebody just go ‑‑ or female just walk down or small children, but the IoT device notice there and notify to the moving mobiles. This is really amazing functionality. But it is ‑‑ street lamp does not only capture crime. They crime whole traffic volume and they capture some private activity. You can't urinate in front of the street lamp. They will capture you. This is a really ‑‑ it's too much.
That means city design is totally focused on the functionality and efficiency of how data analyzes big data for the public use. The problem is that comes with that is privacy infringement. You may heard internet never forget. Smart city data never deleted. This is how I met up with this. Seoul had a lot of money with 50 thousand IoT devices across the city. This is really amazing.
We just ‑‑ there's no guarantee, no policy framework that guarantee privacy of citizen. This is what I am saying.
Okay. I mention the GDPR, I want to ask a question, are we ready for smart city? Are we ‑‑ ‑‑ smart city ready for GDPR compliance? As far as I know smart city not much to consider how can they comply GDPR. This is a big question. Thank you.
>> ALINA WERNICK: Thank you very much.
(Applause)
>> ALINA WERNICK: So our next present is Dr. Sauer who is head of political affairs and government relations at Bosch. Bosch, I think it's the biggest supplier for automotive industry.
>> MARTIN SAUER: Thank you very much. I would like to say thank you for inviting me and giving me the opportunity to share some insights with you today. As was already mentioned, where can I go with the slides? We're one of the leading international providers of technology and services and are heavily investing in transforming our manufacturing company into an IoT company which means investing several billion Euros or dollars every year in R & D activities. What does Bosch consist of? Bosch consists of approximately 410,000 employees world‑wide, which is more than Ziemann's I would like to mention.
(Laughter)
>> MARTIN SAUER: Slightly more than Ziemann's. We have almost 270 plants worldwide, 130 engineering locations, hiring more and more software architects and software engineers as you can imagine and about 460 regional subsidiaries in 60 countries world‑wide. We're active in every continent on this planet.
I will most probably not ‑‑ I will skip that. Most probably not stick too much with my presentation but rather give you some examples from the Bosch universe. We heard something about data. We heard something about AI today already. And often people say data is the oil ‑‑ I think oil is something dirty, so I prefer to call data the air of our time. And it is, of course, a prerequisite for AI‑based business models. When you think about AI, most people do not know that AI is already part of our daily lives. It's fundamentally changing how we drive, how we shop, how we work, how we travel. So it's already there.
At Bosch AI is becoming part of our products too. That means products will assist and support us and make life easier overall.
However, when you consider it from a societal point of view, it's pretty obvious that you have to think about what it means when you have a technology that learns itself. So it's a point you have to think about. It's an ethical discussion necessary about it. What do we have to take into consideration when we are dealing with AI, smart cities, smart living and so on. We have to take into consideration, boundary conditions. What do I mean with boundary conditions? We have to work within boundary conditions to develop smart things in three domains as a company. In mobility, in residential, and in industry.
I want to give you a practical example about this. City streets. In San Jose, California, for example, we are at the moment piloting together with Mercedes‑Benz an on‑demand ride hailing service with automated vehicles in San Jose, California, not in Germany, unfortunately, which is (?) based. There will be automated driving vehicles going through San Jose. The aim of this project is not only to gain valuable insights for technical development, but rather to answer the question of how self‑driving cars can fit in the urban mobility puzzle.
So we are learning. So we do not know how it will look like some day, but we are trying things out. That's what I want to say. Okay. Yep. I will be a bit quicker.
I already told you something about boundary conditions. As you can imagine, we are always dealing with political boundary conditions, technical requirements, standardization, industry specific standards, standards for data exchange, and all things like this. We of course are trying to influence them, but usually we are only one of the players who are trying to do that. We are then heavily relying on guiding principles which is to say the foremost data sovereignty. Data sovereignty is a term used very heavily. For example, from the European Union and the new commission so nobody really knows what it means. Is it infrastructure or other things? So we are working on defining that. But we are as politicians and as scientists are using the term.
My last slide is about public interest. So there's always a limitation in data sovereignty when there is public interest to be taken into consideration. And one way to deal with this contractual questions are voluntary data partnerships. When different parties form these partnerships there can always be a contractual agreement. That's it. Thank you very much for your attention.
(Applause)
>> ALINA WERNICK: Thank you very much for your presentation. Our next speaker is Dr. Hung who is founder of Pineapple Laboratories and the IN_Visible Project. Her work combines independent art‑based research and open science practice to address the issues on global self, intersection design, gender diverse communities sustainable development goals. She's trained in natural sciences and clinical research. And holds a PhD and is a Max Blanco alumni. Born and raised in Venezuela. She identifies as trans and has been a speaker at Open Con '17 and '18 and UNESCO chair on technology for development. A few to mention. Welcome to your presentation.
>> KAMALANETRA HUNG: Thank you Alina. I met Alina at the symposia. There I was talking about the issue of safety in public space and the topic of trans identities and access to public space. We need to understand that public space must be face. Not everybody navigates public space in the same way because it's subjected to our experience as humans. It's not the same to navigate the world as a white man and to navigate the world as a woman or a transgender woman of color. When it comes to design trans people intersect and nonbinary people are at risk and vulnerable position. It's kind of funny because I just arrived an hour ago and it took me like 20 minutes to find my badge because my ID didn't correspond to the data that I entered in the database. So like infrastructure is actually not from the digital in this digital space is not accommodating these topics.
So it's important to talk about intersectional design. We're talking about intersectional design we need to address issues as migration, issues as race, and gender. I am immigrant, I'm a rationalized person I have an education of 100,000 Euros. I have a PhD in Germany. I have international reacher in Israel. Yet it is a barrier for many of us. It is very important when we consider the design of technologies to think that we need a collective process and we need to include also minorities into this equation.
So I don't represent an entire community. There are many transgender people all around the world, and they have also different ‑‑ they are very constrained and limit by political boundaries. So he cannot easily navigate public space. So when we're talking about designing technology, we need to gather collective information from different parts of the world, because it's not the same for a trans person to be in public space in Latin America that is to be also in other places, let's say, in Europe or the Middle East.
So this is very much like let's think about the design and how can a future technologies can embrace this type of question. Thank you.
(Applause)
>> ALINA WERNICK: Thank you very much. Dear speakers, thank you very, very much for your contributions. I think it was already really great background for our group discussions on how to govern, what values should govern data in smart city mobility context. So thanks again.
And if we can get back our slide sets, I would explain then the workshop methodology. So I will go through the workshop methodology. We decided to go with the method which would allow hopefully for all of you to participate in the dialogue about smart city mobility. And it's called one, two, four method. Don't worry. I will explain several times during the workshop how it goes. We will start with individual brainstorming. You see in front of you there are Post‑Its. And I invite you to write down your thoughts about data governance. After this stage, you can start now or you can wait until the actual three minute timeline starts.
Then the next part would be is that you find a person close to you who you don't already know, and then you discuss together what have you found about data governance, what values do you find important, and I will show the questions in the next slide.
The next part of the method is that two pairs will pair up again, so groups of four. And then discuss the topics together. And towards the end of the seven minutes, you will select one person from your group who will then represent you in the last stage of the workshop. In the last stage you may see that there are kind of flip chart papers and four points here. Our speakers and also Holger Dieterich will be moderating or facilitating this discussion by this table by this flip chart. And in the final stage we'll wrap up the findings on data governance in the discussion here where the moderators will bring the results discussed in the group here to the stage.
And the questions which we invite you to discuss is for you to identify from your perspective from the country and the continent where you come from, what are the different at stake in relation to data in smart city mobility. We encourage to draw from your experience because it's rare to have in a room so diverse group and so international group of participants. And also, we invite to discuss which of the interests may be in conflict? And finally how data governance may address these problems?
Just to illustrate with an example how this can go, I'm drawing from my experience of being Finnish. Right now in Finland it's extremely dark. So a lot of smart city initiatives they coordinate with smart street initiatives. There are pilots for example for more environmentally friendly and smarter control of street lights. I recently read about this initiative where they suggested that there would be a system where you can order with your cell phone street lights into a nearby park when you walk in the evening to go for a run there.
So we have here ‑‑ I will ‑‑ so interest in sustainability which we can recognize. As a follow‑up in this project there was the idea well actually either cities or companies could ask for reimbursement from these services so you pay for the lighting as you go. So it looks like a business opportunity. But this suggestion I think it raises a lot of questions. So first, I don't think there were many women involved in the design of this pilot because ‑‑ are you comfortable with somebody knowing from the data that you're going to the park alone for a run? Also not all people have smart phones, so those who don't have smart phones they can't use the system, so should they walk in the dark? Also, finally, people who can't pay for these services. There are a lot of questions related to equality. There are questions related to public safety. And finally questions about provision of public services. And obviously privacy because what happens with your running data at night?
So finally, if we look at the possible solutions, we might think that on the level of data governance maybe there's a possibility to devise sustainable solution that doesn't require that personal data about you. So we could refer to the principle of data minimalization. Then we would have to have a dialogue, what services should be public and what could be private, privatized. Finally, as we discussed earlier we need inclusion and intersectionality into the design of such systems. I'm sure from where you come from, from your city you can think of other interesting tensions or examples of how smart city mobility could be executed in a way that supports the UN development goals.
>> KAMALANETRA HUNG: I would like to address a word. It's not about inclusion design because when we talk about inclusion, who is including who and what and how. So we need to talk about intersectionality. I wanted to address that. Who is included in who, what and how.
>> ALINA WERNICK: Thank you. Our next stage is to actually start the workshop. So now we take three minutes and please jot down the ideas that you have on the Post‑Its on these three topics.
(Group activity)
>> ALINA WERNICK: So next stage in the workshop, please pair up with someone you don't know and discuss together what you have written down. And you're encouraged to group up your thoughts. And if you find connections, arrange them, so you can work creatively with what you discover in the discussion. Five minutes for this stage.
(Group activity)
>> ALINA WERNICK: Okay. Ten second left. Prepare to close the sharing and write down your ideas. Thank you for paired discussion. And now find another pair and discuss with them again.
(Group activity)
>> ALINA WERNICK: Please break your intimate discussion with one person and include another pair to your discussion. And as a message to online participants, you're very welcome to answer to the questions online.
(Group activity)
>> ALINA WERNICK: Now groups of four, a reminder, at this stage please select one person from your group who will report about your data governance findings in the next stage.
(Group activity)
>> ALINA WERNICK: Okay. We're approaching the next stage of our workshop. So in the next stage, first, I will invite the moderators to approach flip charts. So Mr. Choi will start with flip chart A. Then Dr. Hung will be present at the flip chart B. Then we have Holger at flip chart C and then Christian at the flip chart D. Now do the leaders or rapporteurs of the groups of four raise their hands? Could you please select rapporteurs from the groups who haven't selected it yet? Okay.
Here these two groups in front would come ‑‑ not the groups but rapporteurs will discuss with Mr. Choi what they have found for the next ten minutes. Then the two groups in the back will discuss ‑‑ sorry, the rapporteurs, but groups can be present. Rapporteurs will discuss with Dr. Hung. Then the leaders in the two groups here in the back will join Holger Deiterich here and the others will join Christian. Rapporteurs please approach the respective moderators.
(Group activity)
As a note, invitation to groups creatively and discuss whether they can be regrouped in a way that makes even more sense to you. Thank you.
(Group activity)
>> ALINA WERNICK: One minute left. So thank you for the lively group discussions and very collaborative efforts to discuss data governance. Now I would kindly ask the moderators of the group, wrap up and come back to the stage and to bring the flip charts with them. Group B, please proceed to the stage.
>> Okay. Can I start?
>> ALINA WERNICK: Max? So we will start with our first group, Mr. Choi, would you like to share what were the core topics that were discussed about data governance?
>> EUN CHANG CHOI: Let's go‑cart started with group. Our group consists of very, very smart people. I think I'm very lucky. I never imagined that I moderate this very important session in my life in Berlin. (Laughing) okay. Starting with A group there are several concerns and interests. My group just focused on the polarization, polarization is our group's interest. Multi‑stakeholder for accountability. You know the accountability is the different from responsibility. Responsibility is somebody make a mistake and they're supposed to take some burden to take care of everything. But accountability means that how you use data for what and what kind of outcome has come out and how, what kind of way. That is the explorability.
Usually data has been a useable asset but people cannot explain what kind of data has come out like that. This is very important. Although A group debaters suggest that private companies should cooperate with public city officials because without public city intervention or some kind of technical requirement asking, private company will have a leeway, whatever they want. This is laissez‑faire and no guiding principle. That means cooperation means in the other way like public needs should be asked when they design technical for smart city.
The second is the identification. This is a very core value. Because even though data governance, data governance ‑‑ smart city data going to be need by some region, without some specific all of the data should be anonymized. Without this the data is portable and identification doesn't happen (?) in China. Democratic countries. And the third one is reconciling value. Each though we have smart city, we need privacy and citizens should get noticed, what kind of data they already use or sometimes not.
The monitoring of the marginalized people. Monitoring is not suggested. Monitoring is considered. For example, my grant. Marginalized people have been targeted with hatred. In Hong Kong there's a lot of protest and there's an application to track down the protest groups. This the nightmare of the democratic nation.
This is the ultimate is the tech design, the infrastructure consist of tech design which should guarantee privacy. There's coding. Coding is made by law in Congress. The second coding is the tech giant should have (?) on design principle. The tech giant, for example, why they have full ability to make tech design without respect of privacy. That's a problem. Thank you.
(Applause)
>> ALINA WERNICK: Thank you very much. Thank you to the entire group who contributed to this flip chart. Then next, group B. Each group has two minutes. I kindly also suggest that you would mention if you had something very similar. Then just say our group also found this important and highlight the things that haven't been mentioned yet. So the group ‑‑ oh, yes, microphone.
>> KAMALANETRA HUNG: Hi, I'm from Pineapple Laboratories. We identified first who are the players, the actors or the users in this problematic. We had an urbanist who is a designer who identified the conflicts of these different players. We identify first the users who are affected by this problematic. We can talk about, one, citizens of everyday. Second, signs. Third, public policies. And fourth, businesses.
So how these things are related to each other are explained in a little diagram that was visualized by our friend from Sao Paulo, right? There are issues that are connecting these issues which are basically privacy and data access. How do we provide both things, access and data protection? And how you make these people vulnerable.
This is also a huge cosmology that in my opinion it's a collective process. I just want to start with the question, who is in this room who is transgender and can talk about these particular issues? At moment it's only me that's in the room. But I just want to speak also from a bunch of people that I know outside who cannot be in this privileged space. And how these people actually have a voice in a democratic system.
We also have here an intervention a colleague from Rio who is basically concerned about safety in mobility in public space. This issue needs to be rethink because we are also need to think why are people so scared? Why are people so scared about talking about these issues because people are traumatized and who is the person developing such technologies?
So I work as a designer and I work also as an artist and my approach is to also feel the concerns of the user from an art perspective. I'm not a politician, but maybe you have a little bit more of something you would like to add?
>> AUDIENCE: From the (?) perspective like the best practices are ‑‑ that are being developed concerned with the participatory measures in general. So if you put all these players should discuss it and if you create an application or something, if you use the platforms of a smart city, you should put the players together to discuss and to develop it as a process, as a continued process, that's the best practice we have.
(Applause)
>> ALINA WERNICK: Thank you very much. Thank you to entire group. Well, group D is ready. ‑‑ sorry, C.
>> HOLGER DIETERICH: Okay. Our discussion was ‑‑ the topic was about governance in smart city mobility. We had more like smart city data and governance, but one group like we came together, two groups came together to one it was focusing on mobility which means public transportation. So we had some things in common which have already been mentioned like the dilemma between privacy versus open data. I classified this as surveil who will share with whom. This is a whole case of problems and challenges. Having a say which means transparency in decision making, what is being implemented? Will there be corruption maybe? How can people have the capacity to enter as citizens into intervene in such processes to build a smart city mobility applications? That is this cluster. Practical implementation, if you do something really big and implement everything and it will be outdated when it's finished. How can you move fast but be step by step to stay up with technology. And what data formats are there and where can I find documentation? Who can use this? A very technical practical questions.
And also the equality in general there should be universal access for everybody to services. You don't have to have this technology to use this. This is this other cluster. The center for all this are all the promises we collect the. This one group collected which is what you said at the beginning which is safety, efficiency, maybe quality of travel, maybe better pleasure, less traffic jams, and clean air. The list goes on. This is more like the promises of all this smart mobility solutions and around this there are different forces challenging all this. This is what we did in our group. Thanks.
(Applause)
>> ALINA WERNICK: Thank you so much. Thank you to the entire group for the input. Then group D.
>> MAX VON GRAFENSTEIN: We're doing this together. We collected from two working groups. We defined ‑‑ our rapporteurs defined something like an ecosystem where different interests have to be taken into account which means that you have a societal sphere, you have a data management sphere and a private public interest sphere which have to interact. And now I'm handing over to my colleague.
>> So I think we tackled mainly the same questions as in group C. We have the part of the data management where it was mentioned that data is an important aspect which needs to be taken into account and basically the individual data control is important in the entire design process. And then that basically feeds back into the question of the public good or the societal impact, however you want to call it. So one case, one particular case was that data can help blind people to navigate in the city. So actually that's a question of the data impact but that, of course is strongly linked with the question of data management and then in the question we have the again of public and private interest. If you have private companies in between that section, they of course have a different interest than the end user. That's basically the ecosystem which needs to be taken into account in the design process.
So did I miss something?
(Applause)
>> ALINA WERNICK: So thank you very much for the speakers and moderators and for the groups. I thought that maybe you have an idea how to wrap up. I'm overwhelmed because these are really sophisticated analysis of data governance models. And we will write two reports on it. I think we might reach, let's say, most conclusions. Maybe if you have something.
>> I have a practical conclusion could you maybe leave all your little Post‑Its right on the table because one of the very original ideas to organize this workshop was that this is one of the rare occasions where you get so many different people from so many different regions together and to finally overcome the typical cultural national perspective.
And when I was just dropping from group to group, I was so actually enlightened to see, that's really interesting this problem, because I hadn't seen it before. So even though some of you might think, hey, not all of the interests, conflicts, et cetera, are really now on the screens, please leave it there, because we would like to work with it in our academic research later on.
From this perspective I would say it was really even more what I expected and I expected a lot. (Laughing)
>> ALINA WERNICK: Thank you so much for your participation and to the speakers and moderators and also to our working group.
(Applause)