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IGF 2020 - Day 7 - WS 75 AI solution and governance for global public emergencies

The following are the outputs of the real-time captioning taken during the virtual Fifteenth Annual Meeting of the Internet Governance Forum (IGF), from 2 to 17 November 2020. 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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>> CHUANG LIU: We're waiting for Daisy from South Africa.  Daisy is an active member in the Executive Committee Member and we have worked many, many years for Developing Countries.

This is a good time for us to meet together in this special time period.  Greetings to you and we'll have discussions on what's the situation, what's our common understanding and the methods and actions for us.

>> Hello.  This is your IGF host speaking again.  Please be informed this session is recorded and hosted under the IGF code of conduct and UN rules and regulations.  Please kindly respect that.  Be informed that the chat feature is for social chat only and only the Q&A feature is used to ask questions.  Have a very successful session.  Thank you.

>> CHUANG LIU: Thank you very much.

>> CHUANG LIU: Daisy, will you introduce yourself for us for one minute?

>> DAISY SELEMATSELA: Okay.  Sorry for that.

Good morning.  Yes.  Good morning from South Africa.  This is Daisy Selematsela, I'm dying in from home because we're waiting from home from the University, I'm hoping the internet won't be a problem.  Thank you!  Looking forward to the discussions!

>> CHUANG LIU: Daisy, she's a professor and President of WVO and a lawyer in Mexico, and you know Horst, and you know the Chair of coded groups, and then myself.

>> KE GONG: Hello, everyone.  Good day.

We'll start our panel discussion.  At first, I welcome all of you to make your time to Joan this very important discussion inning.  It is so important.  Especially important in the context of the COVID.  Today's panel is titled The role of digital tech in environmental sustainability.  We have the panelists have already introduced each to other and I think we just save the time to not have introduction again.  Please allow me to welcome you all on behalf of our team here in China and we're very happy to have these discussions and even though that's very short, just for one hour, I think we could continue this discussion in the future since we have already built a link between us.  WFU is an organization that consists of a hundred national members from different continents representing the engineering community.  We also are working with AI and we think AI is an important tool for to us implement SDGs and it is a powerful tool to combat the COVID matter, public crisis.  We're going to have six panelists to give 10‑minutes speech each and then have another 20 minutes for discussion.  Please allow me to give my presentation at first and I'll share my screen with you and ‑‑ could you see my screen?  Okay.

The title of my speech is AI, a powerful engineering tool for combating COVID.  Please allow me to use a few examples to show some WFU related works.

Here, on the first world engineering day approved by UNESCO was 4 March this year and unfortunately because of the COVID we couldn't meet together to make the plan with several regions of UNESCO.  On that very day, there was a statement made to call engineers worldwide, stepping up to the challenge of the COVID, at that time we called it coronavirus and other global threats.

Going back to AI, the first Ray alarm to this crisis is made by AI.  That was the 31st of December, last year, a company of Canada used an AI driven algorithm which looked at the foreign language news reports and other networks and officially proclaimed these crisis ‑‑ this crisis and gave alarm to the clients and also gave the alarm to the U.S. center disease control and also the Canadian ones.  Here you see AI plays an important role in areas like automatic body temperature modeling because the fast grasp of everyone, it allowed the very strict temperature monitoring rain the ‑‑ AI is used to support, to trace the affected people.  Very powerful usage of AI, it is to help doctors to identify a coronavirus city image that's widely used in many countries.  And MIT researchers found that people were asymptomatic and it may differ from healthy individuals in the way that they cough.  These differences are not decipherable to the human ear.  It turns out that they can pick up by artificial intelligence.  It is a very interesting finding.

One of the other challenges with COVID‑19, it is that it becomes apparent during the pandemic that patients can expect symptoms and then go downhill very quickly.  The system using AI will acquire, analyze, fuse relevant signals for realtime monitoring of the physical situations of the patient there in the hospital.  Here is another example offered by Google using the AI to accelerating drug development to treat COVID‑19, but so far we have not got a very decent track and it would help to find them, hopefully we can find them soon.

As pointed out by Secretary‑General of the United Nation, he said while this crisis is imperiling progress toward the Sustainable Development Goals, it also makes their achievements all the more urgent and necessary.  Moving forward, it is essential that recent gains are protected as much as possible and a truly transformative recovery from the COVID‑19 is pursued, one that reduces risk to future crisis and bring much closer the inclusive and Sustainable Development required to meet the goals of the 2030 Agenda and the Paris Agreement on Climate Change.

He stressed that we will ‑‑ it will require a surge in international cooperation and multilateralism.

It also requires a synergy of global partnership on AI for technical environment to make it more powerful and also for its governance.

In the practice of AI in combating COVID we do find some shortcuts of the current AI application.  For example, even though AI is widely used in combating COVID and its application has been accelerated in many fields, because of the new need coming up from the pandemic, shortcomings of AI applications have also been exposed such as the quality and accessibility of medical data for further process by AI.  That is an urgent need and it's a short come and the standards of data protection and sharing, the technology of test, the verification for AI application to ensure the standard is implied to protect privacy, so on, so forth.  The capability of the AI to find out an abnormal phenomena like human doctors, it is not exist so that AI could not very well to making early warning in the current situation.  Inclusiveness of AI applications for different groups of people, especially those elder people is another challenge for AI applications.  Going through these challenge, first let me introduce what's been done by WFU.

Our Committee for engineering ‑‑ for the engineering for innovative technologies has proposed 7 principles for responsible conduct of AI in engineering practice.  I list these 7 here from good for human and its environment, fairness, inclusiveness and public awareness, privacy and data integrity, transparency, accountability, peace, safety and security, collaboration.  I think collaboration, it is also very good proposed in these principles.  To carry out these principles, we need really to work hard.  We call on governments, foundations and corporations to conduct cross‑disciplinary, cross‑sector and multinational collaborations to establish consensus on AI ethical principles and we call on strengthening collaborative research and development of AI governance technology to keep pace with rapid AE progress.

Third, we call on open AI development apps platform with built‑in ethical development tools to support different AI system in evaluating its function and regulation compliance to define AI moral scenarios with significant social impact so that experts from different disciplines can work collaboratively on addressing AI ethical challenges.

Finally, to promote ethical education for every stakeholder in AI research, development, application, management.  All people should allocate it for a responsive conduct with AI.

Here I close my speech and move to the next speaker with Professor Horst Kremers from Germany.

>> HORST KREMERS: Thank you very much for this very interesting first presentation.  Being an engineer myself and also working in information management in the applications.  I personally have worked with UNDRR, the program of the United Nations national European National global level.  My presentation, I would like to start.  I want to address a few selected principles that I would like to consider.  This is by far not complete, but it is some of the things that are not addressed at the moment in all the details and I would like to highlight a few of them.

I start talking about mentioning the data program and the challenges of information management at the international ‑‑ indicated on the international level and the background, it is a real big one, and it is also addressed by the Committee of data technology and my presentation, I want to highlight that I probably will only speak in aspects on the reduction and the specifications, the three accessible interoperable and reusable data principles.

On the historic background, it is not so far historic, but at least I go back to the year 2008 when I did my first international symposium on risk model application, that was also the time when the data, it started a Working Group and document archiving and those working in though this field, they know it is a real critical topic that's not done very well and open access to the disaster information, it is just to indicate that we're working on that since quite a long day.  The risk information management, the pillar, it may be more detailed across this prevention phases, preparatory and response faces and volatile, uncertain, complex and ambiguous environments and this is particularly the aspect of going there with global methods of representation or with artificial intelligence methods.  I will not compete with both, I think we need both.

In addition to comprehensive management, each of those pillars is subject to its separate information management elements, phases and processes.

What's the basic management principles, it is first of all, critical thinking, it is general management principles which in not all cases are really followed, sometimes people are more interested in doing certain methods, exploring certain aspects but management of those aspects are ‑‑ we have to talk about the analysis and we have already talked about decision action support and so on.  I don't want to go into details, all the presentations will be made available after the session.

Knowledge present takings, that's my main topic today with a few aspects, it is about setting up Ontologies, modeling of information representation, analyzing, the reasoning, deciding model, the acting and the goal‑reaching.  We made a formal representation of this.  This is not just doing it and saying that we have some data, we have some knowledge or some ‑‑ we'll just give it away and full stop.  All of that, I will certainly tell you all of that has to be done in an organized way.

The dependency is the other way around also, from deciding and acting, that is something that's actor, roles, facts oriented, we would have some rules and that also would reflect after model into the Ontologies, it is a permanent process, it is not something that we do one day and do for many years and then we see it is a precursor.  The goal reaching, it is something we have seen sometimes, to really reach the goal, it is the information and the knowledge that we have received, have we reached the goal as intended, it is not a typical management task, if not, we would have to go back to ontology modeling.

A thing I want to mention, it is really a bit problematic in the data spaces that are algebraic properties of information spaces, and it is information denseness, information homogeneousness, isotropy, it is working the same in all directions, is it continuous, is there no efforts in between or something or a prediction, a different liability, some steps or you have to model the steps, not just to street them at homogenous.

Knowledge generation, it is in the field of recognition of facts and that is in the formal reputation, you have the artificial intelligence measure, they're really positive in this field, the recognition of methods, the signal change detection, the coalitions and the dependencies between parameters and data spaces, sub data spaces and the verification of the hypothesis and the open area that we have to deal with in this session, it is also for knowledge representation, not just for a single dataset, you have the open data, then we would need the open software, we have to talk about and find out and describe the open analysis which is kind of available sources of how to do artificial intelligence, the models behind that, there is the intention also to make the artificial intelligence analysis so everyone can understand it and redo it.  The open context models, the scales that are applied.  The process models, that's those one where is all of the information flow comes together, some people, they call it also the quality measures of the quality processes to reach a certain result from complex input and analysis and then we need open quality measures that need to be transparent and what is not just precision but quality measures for instance like use oriented, it is the use of the data sufficiently and in the right way supported, not just the very best and precise data that we need to support the operational quality in all cases.  We need the open knowledge base and first of all, as an engineer, information people, they say including transparent, holistic documentation of this.

You know, I'm not talking about a single aspect, the whole thing become as bit complicated but don't fear engineers are dealing with that complexity to the user.

Operational decision support, that's the challenge.  We're working in risk domain and we're working in volatile, uncertain, complex and ambiguous environments and under certain boundary conditions and a few of them, they're much more, it is that we have to work our things about certain groups, data changes, analysis change, the situation changes in time, sometimes very rapidly and we have to do possibility in our analysis and decision making.  Don't forget we're not only mathematicians behind that, but there are managers of taskforces that would need the possibilities of the actions to be checked out.  Compliance checking, restriction, we come to the technical procedural, legal, financial, ethical problems that are also touched in the sessions, and I'm very happy to help.  And we have speakers today and talking about legal and ethical things, I always like to refer to the data protection guide in humanitarian action where a lot of these detailed aspects are treated.  Integration in open environment, there is some action field I recommend, the question of incompatibility, inconsistency of data and functions and results.  The similarity aspects, actions of normative meeting and processes government, there is a discussion on doing the same analysis, doing different analysis so we compare the analysis, so on.  Consistent multilevel generalization, that's a matter of we're doing support of knowledge for dinner levels of the decision makers so that has to be a little bit consistent with each other, otherwise all things get rough.

Preference and confidence measure, and what I always request, what I suggest, engineers do it, information people do it, set up test beds so that they have a situation A digital situation, they're a little bit complicated, people can test their AI methods, what we said, misrepresentations of results, dynamics of the situation, vulnerability, so on.

Main conclusions, knowledge representation for operational purposes is based on formal methods that are enriched by artificial intelligence methods.  Open access is a key scheme.

The UN instruments information in its complexities is in due need of a broader systematic integration, that's not addressed as I have seen too much in the really broad because of the complexity behind that and it is not always in this process.

There is a need for the public and private sector and Civil Society and academia and scientific and research institutions to work much more closely together.  I see a very slight joining, opening of the science actors to work together with practitioners and it is very hard work I know, it is not easy to get all of these people together but for the practice, for the humanitarian aspect of those who will rely on our support, it would be necessary for people to come to one place.  The application of informatics and state‑of‑the‑art that's available for innovative work and that's something that we have can adherence and require for this situation.

For this, I close my presentations, and I would be happy to stay in contact with any one of you in the panel and also the colleagues that are joining us in the presentation in the audience.

Thank you very much.

>> KE GONG: Thank you. 

Now may to invite professor Chuang Liu to give her presentation.

The floor is yours.

>> CHUANG LIU: Thank you.  Hello, everybody.

I have been working with the COVID and data hub.

Ever since COVID‑19 happened, there is a need for what kind of knowledge, data and what's out there in the world.  The China Association for science and technology supported our emergency project, COVID‑19 knowledge & data hub with a focus on the publications and the publicly available data so connected to each other, so up ‑‑ this is a system, this is the science.  There is 50 million cases as of yesterday in the whole world.

I notice from the chat, you can see in the different regions, and now we have ‑‑ hold on one ‑‑ we have 132,000 documentations and the data science in this system and this is a joint action, the COVID‑19, there are more than 50 experts from a dozen institutes joining this project, and this is launched in Jan and it is up to date.  We work together in the past and now we're working on this.

We have the documents and the datasets published in the world in each month, you see since January and in October end and until today, so we have this number, each month, it increased more and more and the different languages, the different reasons and the different publishers also and then most of them are the research publications and we have seen the situation reported, more than 800 research statistic databases.  In this case you can see almost every day, every workday, there are more than 600 documentation, publications and datasets.  You can see that this is very hard work.

How to deal with so big much resources?  We turned them into 16 groups and then terminology, daily briefings, press and remark, guidelines, review, epidemiology, virology, clinic aspect, Chinese medicine and pharmacy, public health, psychology, socioeconomic science, geographical environment, new tech applications, actions and platform, scientific popularization.

You can see all of this and the data side, it is the different formats.  There is so much information coming together, we need a new technology.  Under this one, we needed all of the information either timely to be findable, searchable, linking to one another and the classification statistics, all of this things, and the traditional methodology is not good enough for dealing with so many kinds.  We need a new technology to find the resources and we're quickly dealing with the data and also the quick classifications, the quick analysis for the summary and so this is an example for the guidelines.

For people, they don't need the information, the guide line information, it is different, so this is ‑‑ this is the challenges, the governance, we need to deal with the exact information very quickly in the system and we also are protecting this information.  This is all of the information we can.  There is the titles, the authors, the abstracts and the link to the original documentation.

Also there's a big sense that a different kinds of data, metadata, and then the metadata, it is ‑‑ you see this organized in a different way and it is very quick for classification.  This is the challenge to us, so this is a challenge for us to organize all of the information very orderly.  We need to be clear and deal with the data so that is why we need the new technology, it is the Internet Protocol technology including AI methods to dealing with these kinds of things.

Not only this, we also are publishing all of this kind of data and the information and people can easily access this.  We have a journal and we have the global changes on the data and the discovery, so that we can publish all of this related information and call people to work together.  Not only the research paper to summarize and also the analyses paper but also some are regional datasets.

This next slide, it shows you ‑‑ I need a bit of a wait, there's a delay.  Okay.  Here.

So there you have another journal, this is the digital journal of the global trend repository.  This is data published in this journal.  So we have two journals, one is publishing the paper and the other is the publishing of the data.

We publish a paper, and we call together, then it is easy to access them and then we have this video, so on the dataset, we developed this summary on the COVID video report and we have several video reports of this and it is in two languages.

We have artificial intelligence related to this COVID video.

This one, it is Chinese medicine.  This is a clinical one, it is the Chinese medicine.

This one video, it is artificial intelligence applications.  This one, it is granted by COVID for the United Nations, it is good information.  I think it is one of 200 cases in the world.

Thank you very much.  I'll show you a video report about AI.

>> KE GONG: Thank you.  Thank you, Professor Liu.  Thank you for the hard work to make this COVID knowledge and data hub.  I think that's open access, open and accessible.

Next speaker, from South Africa, Dr. Daisy Selematsela.  She will address the issues of technical issue for research data management and the capacity building in developing countries.

Daisy, the floor is yours.

>> DAISY SELEMATSELA: Is the screen showing?  Okay.  It is coming.

Thank you, good afternoon from South Africa to all of the participants and the panelists.  I want to share the perspective from our side, especially from the developing nation on what we pick up and with regards to knowledge management, what we pick up with research with regard to the research data management and the capacity building when we look at the SDGs and now linking with the artificial intelligence.  The first aspect is how do we actually harness artificial intelligence in relation to SDGs?  Where there are complexities between the SDGs, capacity building and artificial intelligence and open data.  There are high expectations that are placed on artificial intelligence in developing solutions.  We have to look at different sectors that are effecting human wellness and human wellness as we know, it is part of the SDG3.  Wellness, it is interconnected when looking at artificial intelligence as developing solutions because when you look at human wellness, there is no simple model to analyze the effects and the implications when we look at, for example, human health, biology and the psychological aspects.

When we try and analyze and look at it from the knowledge side and look at artificial intelligence, we need to understand that this effects human wellness and this needs to be looked at holistically and you need to insure that you have solutions that require the understanding of social activities as part of understanding the treatments, a particular example is when we look at a pandemic, COVID‑19, you need to look at the social aspects, the psychological aspects and not just the biological aspects or the medical aspects related to COVID when you try and use artificial intelligence to analyze the situation.  In this regard, knowledge is key in addressing such complex issues when we have to deal with data issues and also artificial intelligence and like the three previous speakers alluded, issues around data management, the type of data and so forth.

African leaders have actually responded to the SDGs, Sustainable Development Goals agenda, by setting regional priorities within the common Africa position on the post‑2015 Development Agenda which is part of the African Union 2014 and later agenda 2063 and that's what South Africa is looking into, agenda 2063 on human health and how we address education and my focus will be on human health and education.

The African Union agenda 2063 places prominence on research and innovation for Sustainable Development.  An important development is the formulation of the ‑‑ in the formulation of the Sustainable Development Goals, it is the universal recognition of the importance of the quality of education which is Goal 4 and it is key for us in Africa.  The SDG target in Goal 4, that are of particular relevance to us that are working in the knowledge field, those of thaws are data stewards, and information specialists is with alignment to Goal 3 and Goal 3 look the ensuring healthy lives and promoting well‑being for all, at all ages in human life.

When we look at the knowledge management SDG challenges, Goal 4 on education poses the following challenges for us in the knowledge field, what are the issues and as part of the issues, who are the actual role players in assisting and addressing the knowledge generation and feeding and assisting also with issues around data management.  Who bears the responsibility on the complex and interrelated issues of accessibility and affordability of such knowledge resources.

Does this perception that knowledge specialists, that they have also to consider the appropriate application of the four interrelated and essential features to the right to education as depicted in the United Nations' Committee on Economic, social, cultural rights and these are the four elements that I'll share with you and you have heard some of the presenters alluding to some of these elements, the first one will be availability, accessibility will be the second one, third one, acceptability, and adaptability.  When you look at availability, you have a number of activities that are involved in the establishment of Committees of practice to share and facilitate knowledge sharing and learning and cooperating in pursuit of sustainable solutions in what we're going to achieve.  Accessibility will look at the roles in information literacy programs because when you look at what the pandemic is doing now, and all other climate change issues that we have to deal with, citizen science is quite important and all of these areas, these are key in assisting and educating and orienting the population at large. 

When you look at acceptability, it is not about making available open resources or sources to citizens, science and to the academia at large.  Adaptability will also look at training of researcher, policymaker, citizen sciences and public outreach support to ensure that everybody understands the application of knowledge in solving disaster, health problems or other disasters.

So knowledge management, when we see an information management have an impact together with citizen science in this research aspect.

University researchers are being encouraged to collaborate as part of their research in an aggressive way of funding for the research that addresses ‑‑  the respective countries and the Sustainable Development Goals, what this implies, it is that in Africa, most of our research from the University side is conducted with findings from the global side but the countries are actually pushing for academics to ensure that they collaborate and focus on the grant challenges, those issues that are impacting on the SDGs.

Another expectation, it is for the knowledge specialist, those that generate knowledge, those that are in the forefront to provide information and data to reinforce or facilitate innovations that can be after harnessed for improvement in health and we're talking about artificial intelligence in this regard and whether that data provided on innovation are conducive to bring policy change and these are the conditions for transformative innovations to succeed as part of public good.

I just want to share the knowledge society indicators in relation to SDGs.  For us, like I indicated, I'm focused mostly on Africa, a lot of amounts are spent on research and development as a presentation ‑‑ as a percentage with the Gross Domestic Product.  It depends on the country.  For example, 50% in South Africa offers unwell uninvestment in research and development, it is formed and comes from international partners.  This is from funding agencies.  Qualitative measurement of the use and access of ICTs leads to the indicator for us to be a knowledge society.  The technology also impacts on what we want to achieve and when you talk about artificial intelligence of now, the issue around higher education internationalization, it is quite key because this is where researchers are coming from to be able to be innovative.  When we're looking higher intelligence here, we're looking to integrate all of the dimensions of all aspects of higher education to enhance the quality of education and research for all students and staff to make a meaningful contribution to society.  That's what we're looking for when we look at internalization in higher education.

The other I wanted cater for a knowledge society, it is the number of scientists in the country and the number of patents filed and the number or impact of Articles published or in high impact journals.  You have seen with the professor what she indicated around all of the Articles that have been published for the past month because of the pandemic.

It would be fitting for us to also look at the emergence of a revised social contract between science, institutions, the state or government.  The expectations from government, it is the private and public funds, scientists and universities are expected to address the needs of users in the economy and society, furthermore, researchers are subject to much more explicit accountability on the money they receive for research.  This impacts also on policy decision making from the government side.  What it means, it is implicit, as implicit as it is, it is much more complex, the model is not saying that when you get funding, we expect you to account in this way, account in a way that we want to do research that benefits us.  You can't just do blue sky research when you're a researcher, you need to do that research that addresses the needs of society and you see that happening now with the pandemic.

This is making it harder to place politicians on the merit of public support and public funding for research.  When we started with this array a few years ago, it was in southern Africa, the state was also asking questions and especially saying how is this array going to add value and how will it put food on the table when people are suffering and people are hungry.  How do we deal with that when we're looking at rural areas where people are hungry, so forth, that's things when we do research, we look at artificial intelligence for now and we look at how Sustainable Developments are also impacted in what we want to do and generating knowledge and the management of that knowledge, what are the impediments in this regard.  That's what I'm sharing here.

On the revised social contract between the researcher, scientist, the Universities, the states, that there expectations of science, these are more direct and concrete.  The issues around the trust mechanisms of the scientific regulation and the linear model of innovation that have been replaced by the benchmarking practices, performance measurements and the indicators of quality because that's what you want when you put out the research funding, you want to see the feedback and indicators that would benchmark on quality indicators.

In closing, what does this tell us?  When you look at the SDGs we look at the data issues and in particular the open data, knowledge generation, the role players in the process, how do we actually organize ourselves as knowledge practitioners like Professor Horst Kremers indicated to better support the knowledge economy for Sustainable Development.  How do we actually demonstrate the data‑driven and the artificial intelligence driven and the knowledge management value through impact and usage measurements.  How do we have data driven and AI driven practices and how do we actually capture and communicate the value of AI, the value of data‑driven practices within the Sustainable Development Goals.

I will end my short presentation there.

Thank you, Chair.

>> KE GONG: Thank you very much, Daisy.

Let's move to the next speaker who is from the very early morning in Mexico to join us, Professor Ricardo Israel Robles Pelayo.

The floor is yours.

>> RICARDO ISRAEL ROBLES PELAYO: Thank you very much.

I will present.

>> KE GONG: Please, go ahead.

>> ROBERT GUERRA: Good morning, everyone.  Thank you for the invitation to be part of this workshop, especially to share with you and the colleagues of this workshop.  I'll try to be brief and I'll talk about the importance of the use of artificial intelligence as an effective instrument to solve a public emergency such as COVID is specifically in the case of Mexico.

I want to explain as a lawyer, a law professor, this involvement is about finding a legal solution.  Fortunately, scientists know the technical path and I'm sure that they're already working on the proposal that I will explain in the end.  I have followed the previous workshops and observed enthusiasm in finding solutions to solve the global emergency and the importance of protecting the use of personal data held by public, private institutions.  However, I agree with many panelists in this IGF about the importance of using the data to seek favorable solutions against this pandemic with the responsible care of the data that we feel that artificial intelligence is a viable solution.

Therefore, I would like to talk first about the background, our current situation of the pandemic and COVID‑19 in Mexico.

The first reported case and impacted person of COVID‑19 in Mexico was on February 27.  The first patient of COVID‑19 was on March 18, death.  Since then, the number has not decreased and we're placing a plateau at the highest place of the curve.  As we can see on the map, on the other hand, the Mexican government monitors the number of cases of infections from COVID.  It is logical to think that most of the infection, it has occurred in the industrialized and more populated states of Mexico.

On the other hand, the Mexican government created the epidemic group to establish the mobility of people to activate economic activity and social convenience.

We have looked at the transmissions between people and the responsiveness and the consequences of the epidemic of health and life.  However, although they have had good efforts, it is not enough to at least contain COVID cases.  One case, it is the information that's coming from the data provided by people, unfortunately, their involuntary ignorance when they give the information to the health authority, it has been a factor that has to be brought by the right information and communication.  The issue of information transparency, it is also a concern of health authorities and the general population.  From a point of view, it is an issue that has been working on, however again, it is necessary to improve so that all of the data that's needed to feed artificial intelligence, it is real and efficient.  The technology, the use of the smartphone, through their different application cans contribute to face this and other public emergencies.

In Mexico, there are 80.6 million internet users and 86.5 billion smartphone users.  The reach of the internet has increased and will increase more in the coming years.  Besides 48 million users installed applications of the smartphones.  Most of the applications are for instant messaging and social media.  We have audio and video, games, mapping navigation, et cetera.  As we can see, the people with smart phones use application, however, 48 million is a considerable number.  As generated from this data infrastructures an economic point of view, the smartphones, it is easy to obtain, easy to move to different parts, artificial intelligence will use the data generated by smartphones to predict those people that are infected by COVID or has the probability of being infected.  The health authority has the information to act in advance and to take the necessary measures to help a large part of the population with optimal results.  In Mexico, the other countries of the world, some applications were developed or were improved to combat COVID‑19 through different methods they attempted to establish different strategies to perform promptly and efficiently against the pandemic.

Applications like close contact detection, WhatsApp, other apps from China, coronavirus from Peru trying together from Singapore to name a few, they're good examples.

The Mexican and federal local health authorities have created as well applications referring to COVID and they're mainly used to answer questions and to provide instances for people who believe that they're infected with COVID and locate cases in the territory.

In other countries, this kind of technology is used to place and take urgent measure, including to go and assist people personally.

The Mexican government knows the importance of using technologies as a tool to deal with public emergencies.  On June 23rd the government boards of the national council of science and technology published an agreement in the official gazette of the federation issuing the institutional program 2020‑2024 of the national council of science and technology.  It recognizes science as a Human Rights based on the provision of universal declaration of Human Rights and the international covenant on economic, social and cultural rights.  This official document recognized the importance of strategies for national priorities.  Using artificial intelligence to combat COVID can be achieved by replicating the Best Practices from other countries.

Mobile phone companies must send the data from their apps related to COVID‑19 combined with the health authorities and using artificial intelligence to predict the mobility of people and using economic and human resources efficiently, trying to obtain the national health priorities.  Implementing artificial intelligence at the local level and in collaboration agreement with the rest of the countries and the international level it would be one of the important solutions to act efficiently in the face of the pandemic.

It is important to remark that the international agreements must ensure the protection of identity of the users and only will obtain the information related only and exclusively to COVID.  This protects the Human Rights of the protection of personal data.

I'm sure that with the contribution of colleagues that participate in this workshop and the different participants in this IGF we will obtain important ideas that will help find a way out of this pandemic and the use of the internet for human resilience and solidarity.

Thank you very much.

>> KE GONG: Thank you very much.

Now we're going to the last speaker from China, Professor Zhou Xiang talking about Big Data and academic information services. 

The floor is yours.

>> ZHOU XIANG:  Thank you, Chair.

I have to share my screen first.  Okay.  I'm from the Aerospace Information Research Institute and Science.  My colleague already has talked about a very important issue for AI for public emergency like supporting SDGs, even though some critical applications in different countries.

I would like to speak on Big Data for information services.  Please allow me to start with a brief background introduction of my topic.

As you know, information technology, influences our daily life, our social, economic development like Big Data, artificial intelligence, geospatial information so that technology, the science, it is predictable but also will reshape the political economic and military and social and cultural ideas, I think information has a lot of impact on our planet.  As we all know, the pandemic comes to our work, I think it is also working differently, maybe in the face of history, most of us have to stay at home by working on connecting with the support of information technology and we have tried to use different means to keep connection and transfer the information in the different communities and we have to give up shopping and being outside, dining, it is a great change for each of us.

I think the issue for Big Data, when we're talking about the pandemic, we need to have the utilization, that's how we use the data and analytics to fight against a pandemic.  That means that we have to propose the solution to improve not only technology but also the information.  I think we really need the information for our work and sharing worldwide, academic, that requires the information to be provided in an open, transparent, timely, accurate manner.

What can we do, I think there are two fundamental functions, voluntary and prevention, it may be ‑‑ here is a very brief comparing of the different information technology.  Actually when we are talking to the Big Data or AI, we're not deploying or utilizing only one of these, but I think we compare all different data resources and different techniques and means to produce accurate information.  That means that we have the integration of data and the integration of technology.

If you look at this time, there is a lot of methodology for the data analysis cluster.  Just like professor Ricardo Israel Robles Pelayo had shown, the very vivid picture to give you understanding of the coronavirus so I think it was a lot of information for you, not only for decision making of government, but also the public, so those technologies, they can be used to monitor the organization of the personal and also the resident block and also the title applied, AI technology, mainly titled the data analysis, the rapid temperature measurement, the different equipment like a sonometer or a video detector, a sensor, and also there is a lot of new imagers installed at different places so we need a new algorithm on the image interpretation and maybe recorders positioning and people's personal information at the same time.  These are important tools for us to fight the coronavirus.

For integration, I think all of the Big Data, this type of technology, it can be used to complete early warning for the pandemic and also rapid screening.  This is a table for this kind of technology, but who can use those technologies to fight against the pandemic?  I think this already giving us a very good example of the multistakeholder approach so we can confirm that there is a lot of information released from different sources, from different orbits like research community, government, commercial sector, public, Civil Society, the internet organization, of course.  So the stakeholders actively participating in this coronavirus, for example, the universities selects data from governments from different countries and the data from WHO and producing that map for the coronavirus monitoring.  There are public platforms to release this kind of information.  Also we can Google the virus, and as you know from the beginning of the pandemic, you already produced new apps which can automatically notify the people who not been exposed, so I think ‑‑ I think from the aspect of a multistakeholder, the Big Data, it shows how important it is, the importance of the multistakeholder approach and the solution.

If we talk a bit more about Big Data, we have finished a lot of work about information management, we can convey mobilization monitoring and the tracing and the source to people and to each resident block and also we can do the risk evaluation at least in part of the ‑‑ the information from the statistics and it is really important information to support the decision making on control and the provincial pandemic.

As you see from the right of my slide, the various apps on the house phone, the municipal government, I think it is a new kind of government service for public by using Big Data analysis and also we can use Big Data for virus research.  The Big Data can provide surveys and computing, but also it can introduce a lot of machine learning forecasting to promote the research on the coronavirus.  There's a new application.  Personally I'm from ‑‑ we are working on monitoring and assessing using satellite imaging.  If you look at the right side of my slide, you find from the beginning various situations, but from the end of February, the area became active again.  This can be very good information for the supply chain management and also guiding the work for the government and enterprise, also various potentials for data articulation and the technology creation, location‑based service, the generation communication.

And we also have a lot of cameras and civilians, the equipment to help us to get the data and we have the new advancing director which we can see everywhere from this pandemic.  It is a service norm.  We have portal, we have the public from the messenger and also a lot of apps, even a lot of countries, Italy, United Kingdom, they're releasing these kind of tools for the mobile phone to send the notification message to the public about this pandemic situation.

Okay.  We still want to solve a lot of programs.  I think that there is a full prospect, the first is the technical ability, I think for a lot of the country, we don't have a very strong infrastructure or platform especially for detection between user and the public so for that country, they node to enhance the capacity on network and also on the software system and also the commuting and the storage facility as well.  The Cloud computing, this kind of situation, they are in the international community.  I also have the equity and the diversity.  We have to consider the people like children, elder, disabled, they are ‑‑ they have difficulty to access this kind of information.  We have to think about the model information about the pandemic, this second one, it is also responsibility and we all know there's a lot of fake news and rumors ‑‑ okay.

SAQ and other medicines for of the pandemic but I think we need more separation with the governments and we're ‑‑ allow know go ‑‑ the data security, we don't want to speak too much about that.  Everybody knows that privacy is very important for each of us during the pandemic.  I think it is a very big problem.  Everywhere we're collecting permanent information by video, by ‑‑ I mean, this kind of data.  Okay.

The following years, infrastructure as I mentioned, it is very important, different countries have different solutions, I think all of this innovation technology has to be compared and make most of use of it for the implementation of the accurate information delivery like Big Data, smart city, also I think there is one principle that has the utilization to save the resource and investment on that, and also vacation and service, we need to do a lot more work, especially the data openness, the integration analysis, the seamless connection to use control and treatments, it needs to be encouraging and also data security again, we need a unified classification and monitoring as well.

At last, very brief summary of the topic I mentioned, I think internet ensures that people are connected and have access to information.

We're committed to ensure that people get equal access to this and I think Big Data is a good solution.  Big Data, artificial intelligence, not only have intensive application in responding to public emergencies but also it can promote the intelligent processing and services, also the city operating efficiently and technology development.

The final word, Big Data governance, it will improve the ability of society to cope with various public safety and health challenges and also more important, the use of the development of the economic and social development all over the world.

That's it.  Thank you.

>> KE GONG: Thank you.  Thank you.

During the presentations, we received three questions from the Q&A box.  Two have been answered by Horst I believe.  There is one still open.  Let me share my screen to show these questions.

I don't like to ‑‑ I would like to have some answers to this.

Can you see the question from my screen.

Everybody has to open this, it is an open question on ‑‑ let me read it.  To what extent will AI work in land   looked countries during the emergencies, not only COVID‑19, also flood, earthquake, agriculture, et cetera, such as in Afghanistan.

Who wants to answer this question?

>> HORST KREMERS: I don't have the real answer for this situation.  The question is very essential.

I think we're in a stage at the moment, we're in a stage where we are doing experiments with artificial intelligence but the question is how to put that into operational practice.  This is not common practice, and that is why we're lacking this gap between research, individual results and the broad practice and the importance, as I said, also in the comments, on the chat, I think it is very important, good you're raising the question, because it is in the interest of all our citizens so we have to be much faster and we would have liked to have seen more research and development on national, international level.

Let's try to join forces, finding right sources of doing all of this detailed work which cannot be done by small projects and we have to have an initiative on this.

>> KE GONG: May I add some words to this question.

Actually AI, it is very strong internet, reliable technology.  In the emergencies, to keep the information internet to work at least extent, it is very important.  That is information network infrastructure for today's societal life.  AI is strongly depending on the interconnectivities of if we lose the interconnectivity, AI may have some offline application, but by large it is not useful.  That's my answer.

For the question, the answers, comments, any further.

Another two questions have been answered by Horst already.  The panelists, they could give further answers and comments to those questions.  We'll open the block of answered questions.

>> HORST KREMERS: A small remark?  I have to leave at time at 11:10.

I want to thank you for this very good presentation from the introduction to the end.  We see that Big Data has potential of doing all of this.  It came to my mind that for all these topics, we're missing the coop operational counterpart which is the complexity thing.  Data management plans, we're sometimes missing to control this when we talk about open data, how to make that operational at the time of service versus in the disaster and in the aftermath, transparent, it is a good attribute but how do you have to experience that in the operational level on all of thesis basis.  Timely data, it would need service level agreements or something like that or quality‑oriented service level agreement for doing what type of analysis, at what moment and what period of time every 10 seconds, every half hour or something like that.  So on.

We're missing all of these standards where our citizens would say, hey, probably you're working on this since a long time.  The role of digital tech in environmental sustainability.

 

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