The following are the outputs of the captioning taken during an IGF intervention. 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, but should not be treated as an authoritative record.
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>> MORTEN M. NIELSEN: So welcome, everybody. My name is Morten, I'm from UN EGOV. I'll be moderating the next 90 minutes. We're going to wait a couple minutes because we see that the coffee breaks are still on, but then we'll line up.
Just a little bit of household. We'll have an online moderator, so please don't hesitate to put questions or observations in the chat. And Carmen, our colleague, will chime in when she's prompted with highlighting some of the questions and observations that we can then discuss in this panel.
We will have a couple of rounds of discussion. We will start with the panel, obviously, and we'll subsequently open up the floor both here in Lillestrom and online.
(Pause)
>> MORTEN M. NIELSEN: Okay, I see some more people coming in, but let's get cracking. Again, welcome to this open forum.
We'll be looking at ways to advance digital inclusion through improved segmentation of data collection for better and more targeted decision-making.
We have a number of excellent panelists, two of which unfortunately will not join us. Helani from LirneAsia is stuck in space because of issues with flights stuck in the Middle East. And Waleed Hamdi from the African Union's Information Department has similar challenges getting from A to B, Middle Eastern conflict issues.
So they sent their sincere apologies. But they will be commenting on the report and feedback on this session after the fact.
We have an excellent panel otherwise. From the far left, we have Guilherme, he's from UNESCO. Then we have Pria, she's from Research ICT Africa. We have Onica from the Global Digital Inclusion Partnership -- or is it program?
>> ONICA MAKWAKWA: GDIP.
>> MORTEN M. NIELSEN: And then we have also Fabio from CETIC Brazil.
My name is Morten, I'll be basically facilitating.
Now a couple of things that I'd like to start to set the scene. I think we all agreed the Internet is not new, it's been around since yeah, the previous millennium even, but really took off in the late 1990s, early 2000's.
That said, about a third of the world's population is not included in the worldwide web as such. We see there's some segmentation differences on that, low income, seniors, people who are in unique situations or have physical disabilities or even gender segmentation of factors in relation to that and we'll dive into that.
Similarly, we see that most frameworks promoting digital inclusion tend to recognise the problem but not really measuring it. We see a little bit of ham and egg situation that in communities that have the biggest potential, community of excluded people are the ones that have the weakest data.
This is particularly emerging economies, lower income countries in particular.
We also see that these frameworks are often looking at annual assessment cycles. We've had some earlier workshops this week already with examples of how to increase that segmentation or those cycles to be more -- more active in terms of giving quicker snapshots for decision-makers to target their initiatives.
But what they have in common is that they're still very much supply oriented, as in have you used the Internet in the last 12 months, yes or no. Limited focus on the type of activities, the type of demands that we're looking for in terms of gauging the inclusion or the degree of people's use of digital opportunities.
So again, without this knowledge, how can we, as decision-makers from the public sector, the private sector, from Civil Society or research community propose more targeted initiatives that meaningfully aim to include those who are not yet included?
If we don't know who they are, where they live, or their features, how can we develop policy initiatives or charity initiatives or technical initiatives or capacity initiatives to get them included?
So this is some of the elements that we are looking at in the coming hour or so, and we'll have an active discussion on.
But let's get cracking with some questions to the panel. So Guilherme, from the programme at UNESCO, you're developing a set of data segments to monitor not just digital inclusion or exclusion, but also other things. But how do UNESCO promote that as a global standard and what are the type of things that you find is really interesting to compare across different national context or socioeconomic context from your perspective? And do you have an example of how that has led to better policy?
>> GUILHERME GODOI: Thank you, Maarten. I'm very glad to be here. First and foremost, because I do think these Dynamic Coalitions under the umbrella of IGF is also a sort of multilateral policy, concrete tool for this kind of interactions.
If you see the members of these coalitions, some of them, the speakers here, but many others that are not in this room necessarily, they provide different pieces to this puzzle. Therefore, these kind of discussions and also the concrete outputs that this can generate are already something very relevant for this.
But on your concrete question, the information for our programme is an intergovernment programme under the umbrella of broader UNESCO governing bodies.
What the programme does it is to leverage the different aspects of the multilateral policy that is approved by our different governing bodies, our Member States. So I could speak here a lot on different elements that would help to respond to your question. Let me take two or three examples that are in a way complementary. The first one that's probably more well noun is the Internet universality indicators.
Something that was approved many years ago by all UNESCO Member States, have been refined in different moments. I guess Fabia will speak about that.
But that's a complete set of indicators based on these five pillars, rights, openness, accessibility, multi-stakeholderism, and there are very several cross-cutting elements. Gender, children, and so on.
And so this is a concrete set of indicators proposed and validated by a multilateral understanding like UNESCO, but doesn't necessarily mean that you UNESCO needs to apply it.
What we are offering is something that then the different actors can use either if they are governments and they want to use that to prepare and produce better policies, evidence based, or if they are Civil Society that wants to hold governments accountable to tell them, you are not doing what yourselves are saying or like UNESCO or UN in Geneva and so on.
And then in some cases we are invited by our Member States to help with the implementation.
But the first thing is this comprehensive set of indicators, I'm using the example of the ROAMX, but there are several other things connected with meaningful connectivity, sorry for the redundance. For example, the Rams, recommendation of ethics and AI, this is another concrete example. There are others in the area of the 2005 convention connecting all these digital issues with the diversity of cultural expression.
So this is one type of logic.
The other type is when we have mandatory monitoring with our Member States based on the things they have approved. For example, there is a 2003 recommendation on the multilingualism on the cyberspace. We know that multilingualism is also a critical element of meaningful connectivity.
So these recommendation, every four years that Member States needs to report back to UNESCO what they are doing. So it's not a concrete dataset, per se, but what we collect from this mandatory exercise can become that and then be used by the different stakeholders as they see, for example, right now we are in the middle of the international [?] of Indigenous languages.
This kind of data we collect should in 2003 recommendation or the world atlas of languages, et cetera, from the [?] for that.
There are others like the other recommendations like that, for example, there's one on documental heritage. All the digital evidence of preserving heritage, the Member States also need to report that every four years.
And then finally there's the guidance related to this need to keep monitoring and evaluating and producing, for example, risk assessments and so on. More recent UNESCO launched this document that I'm sure several of you heard about that is the guidelines for the governance of digital platforms.
That document is suggesting concrete ways for the different stakeholders to produce risk assessments, to look into what's happening in the digital ecosystem from that perspective of protecting, promoting freedom of expression.
In a nutshell and I finish, we have concrete set of indicators, we have the mandatory exercises, and we have this guidance for them.
I could keep speaking here on different ways that these impacted reality, but I must say that in the 40 countries that have already implemented ROAMX, several have changed these. And they were fulfilled by the application of these kind of indicators, for example.
The last thing I want to say to all of you can good news and bad news. The bad news is that I need to leave. I need to go to another session. Good news is that this will offer more time for more intelligent people than myself to discuss with you.
But thank you very much.
>> MORTEN M. NIELSEN: Thank you, Guilherme, and thank you for warning us before we started. Fully understandable that you're going to be leaving. I'll take the opportunity to quickly jump to another angle. That's for you, Onica Makwakwa. From the perspective of GDIP, how can segmented data particularly related to gender and things like affordability, safety and trust concerns, digital literacy, but also classical literacy, how can we be better that the and what are you doing at the GDIP in order to promote that segmentation of data collection for better and more targeted decisions?
>> ONICA MAKWAKWA: Good question. Thank you very much for that and good day to everyone here and online.
You know, all the research that you've done in this area is the Connected Resilience Report that looks at gender experiences of women through meaningful connectivity, taking an approach of both research as well as qualitative and doing some policy ethnography as well to really understand what it meant.
This segmented framework actually became a powerful fool are tool for us to detect and address very gender specific barriers that women experiencing through -- through meaningful connectivity.
Such as affordability, safety concerns, as well as digital literacy issues that came up quite strongly in that report.
And ensuring that the programs that are then implemented truly focus on women and digital technologies are targeting the needs that women have identified to effectively address the inequities that exist in their connectivity.
I would say three main areas for us that the segmentation actually has shown importance in is that it helps us to surface our hidden gaps. That we may not have otherwise been fully aware of, especially the qualitative approach. I think it helped us really truly understand what the hidden gaps may be.
Taking, for example, the example of online safety, as well as monitoring and surveillance of women's activity online by sometimes family members. That's something that we just do a regular survey asking people are you online, not online, all you would find out is I'm not online, but not understanding what are the drivers behind that, you know.
It could be affordability, it could be safety, and more and more through our focus on women specifically, we are finding all of these other hidden factors that influence their access.
As well as being able to identify some very context specific barriers, you know. We tend to classify, for example, women as, you know, just like one monolithic group, or even [?]
But we're learning there's a divide among those already connected. It's most just about the ones who are connect and those who are offline. But including the ones that are connected there is a difference in how they are connected and being able to -- to have a segmented approach in understanding the classifications around age, gender, income, you know, we tend to do this [?] populations will tend to actually look closer to the experiences of rural communities because of resources and urban inequality that exists.
Especially when you look at a country like South Africa is a good example where within the urban sector you can't just take the population as is.
And then one other one that I want to highlight is being able to tailor interventions and resources. You can't do that without having very specific, you know, information from that particular population.
National averages are just simply not serving women. They're not serving everyone else as well, and I'll give you an example from South Africa in particular where we continue to be the most unequal society with more than half of the population living on GNI. So anything that you're going to do in South Africa without stratifying the income quartiles, you're going to get an overinflated income that doesn't fully represent those who are at the bottom of the income quartiles. And women tend to be very much represented in those lower income.
In order for us to be able to recommend gender specific and gender responsive policy interventions, it's important for them to be informed by lived experiences of women. And that segmentation helps with us that.
>> MORTEN M. NIELSEN: That leads me to a follow-up question, if we have a bit of extra time.
So have you got any good examples of where either -- either your programme has been able to do better recommendations for policymakers or where policymakers have made better decision, more targeted pinpointing initiatives based on better segmented data for gender, for instance, or affordability in that context?
>> ONICA MAKWAKWA: Yes. So one of the things that we did with the connected resilience report is to introduce a method on -- that we tempt policy ethnography, where we brought policymakers together to understand how we make decisions and what informs them.
One particular country, and please allow me to withhold the name of the country, had gone out to build this digital centers in rural areas because women did not have their own personal devices at home and they did not have a way to connect.
So the idea was that these digital centers that were funded through the universal access fund would enable women to have access to connectivity.
And over time, they realized that women were just simply not going to these centers. And it was actually through assisting them to do stakeholder consultations and meet with women and be informed by women that we learned several things.
One was that the hours that the centre was available for just did not work for the women in that community who have to wake up very early, fetch wood, take care of children, get them to school, come back from the market before kids come back from school.
But also the other issue was the issue of safety, for them to walk in a direction where -- that is not as well populated by, you know, people that they would feel comfortable walking past for them to be able to utilize the centre.
So it had very little to do with whether they have the skills or interest or any of that. It was really predicated around their own safety and their own lived experience navigating that community that had not been factored into this huge investment of building the centers for, you know, with the aim and purpose of women being able to use the centers to access.
So we've got quite a few of similar examples in our report as well that just really shows how you -- when you design for women and with women at the centre, you have to actually design with them informing you so that they're part of that solution.
>> MORTEN M. NIELSEN: Excellent.
We've spoken a bit about the South African context in the last example. We'll move to another country with similar challenges in some ways and similar success stories.
But Fabia, [?] is increasingly segmenting their data for the Brazilian context. We're talking about different types of segmentation already, but what are the ones that you're finding particularly useful for decision-makers in Brazil, both at local, but also at the regional state or federal level? And is there a difference between the type of segments that those decision-makers need in order to -- to do better policy?
>> FABIO SENNE: Thank you, Morten. Thank you very much for the invitation and it's a pleasure to be with partners in this -- this discussion.
Before I answer your question, I think it's interesting Onica was very comprehensive in making the case of the advantages of having this type of information.
I'd like just to describe a little bit the institutional model that we have in Brazil that I think it's useful and I think other governments are having this as a reference.
Because while we're -- CETIC is a nonprofit organization, we're responsible for the service, the [?] services, and this allow us to have a specific centre which is CETIC.BR that allow to us have this and sharing with policymakers.
First of all, I think this strategy is allowing to us have the continuity of surveys and other type of research and to make the case of the relevance of this for the government and the society as a whole.
And another -- another thing that's in our DNA that I think it's very relevant is that we are not only stakeholder in the process of the organization, but we are also multi-stakeholder when we do research.
So every time, I think this is useful thing for other experiences. Every time you -- we will start an investigation and start a new survey, we invite what we call a group of experts around a stakeholder group of people who will first define what to measure, what are the topics that we need to measure.
What are the demand for data that we have in the government, in the private sector, in the other sectors.
And so this is useful because we can adjust the data production to the demand of the decision-makers. And this is very useful to us.
And just to mention that we -- for the -- this type of agreement has growing relevance among governments. We have been participating in the G20 processes for the past two years, so last year we have the Brazilian presidency of the G20. And we supported along with ITU a report that was -- the G20 launch this report, there is just connecting the idea of meaningful connectivity and the need for segmented monitoring.
So in this, just to mention, in this report we -- we argued that we cannot only use to access, but we need to use to understand connection quality, availability for use, affordability, device, digital secure, and safety and security.
So also in the members recognising that there are a lot of other dimensions that need to be monitored.
And more importantly, that this data needs to be disaggregated by demographic variables such as age, gender, household size and others. Economic variables like income, employment status, and others. And geographic disaggregation. Because we know that [?] are also digital inequalities is also expressed in the territory and you can find differences between them.
>> MORTEN M. NIELSEN: Yeah.
>> FABIO SENNE: So in this year in 2025, we also along with research Africa, we supported another paper on this discussion on the G20 South African presidency, and stressing a little bit more about the funding issues. How we can -- we need also to guarantee that they have funds do this type of research.
So we can discuss more after the --
>> MORTEN M. NIELSEN: I think it's interesting --
>> FABIO SENNE: I think it's --
>> MORTEN M. NIELSEN: This is maybe a little bit of a side track from the discussion, but we can come back to it. We see that it leads to a logical profit monetization attempt from the company. Particularly the Ministry of Finance.
I used to work for the Danish Ministry of Finance, so I have to mutt that disclaimer in.
But it tends to often result in slower rollout of next generation technology. After the license is secured, the Telcos will want to make a profit. This is logical. But they will then sacrifice either the rollout of that technology or underserve less attractive areas in remote areas, rural communities, urban communities that do not -- are not seen as profitable.
Or the price is -- the profit or the price is transferred to the customer.
Which is, in fact, yes, helping the government to profit maximize, but kills other government objectives and targets all digital inclusion, affordability, and reliability.
So there's some interesting elements, and the universal access fund is often seen as an ability to try and then reinvest the profits from the license into that, like in the case for research or in Tanzania where it's then to fix gaps in the infrastructure in remote areas or increase the volume of hotspots or free wifi hotspots, et cetera, with all the pros and cons they have.
So there's some interesting elements around that. But will data really help us in that record? That is maybe more of an open question.
So a little bit of a side track. But what are the type of tricks you have at CETIC in terms to nuance the data collection? Is there alternative sources rather than the classic we go and collect and do surveys. Are there any tricks to the trade in from your experience that could help increase the segmentation?
>> FABIO SENNE: Methodological innovation can a lot. So we're trying to integrate more just special data and other sources of data to combine. I can give a few examples.
For instance, in the field of connectivity in schools, we have a system called CMET which is a system for software that you install in the -- in anywhere we want to test the quality of the broadband of this organization or households or so on.
And in agreement with the Ministry of Education, we put this -- we installed this software in part of the 70,000 schools in the country, having real-time data on the quality of the connectivity. And we can cross this with the survey data that CETIC has also on what teachers are doing in the same schools and so on.
So here's an example of you can combine different sources of information to provide more -- more granular information.
Another example that I like in terms of geographical segregation that I think is interesting, we did some -- we tend to think that the urban areas are always well connected. But this is not the case.
If you take the number of disconnected, for instance, most of them are urban areas very close to -- to [?] because the population is also concentrated in these areas.
So we had one study that we did a few years ago that we could segregate combining the different sources of data. The city of St. Pao, we could disaggregate the data.
And then for instance we understand that one particular neighborhood in the city tend to be very high connectivity but low level of socioeconomic status.
What's happened there? So we can refine more.
And we discovered that because there was a road passing through close to this area across the different other sources, there was a very -- that there were a lot of young people living in this area and young couples and we could track the differences that we have in this particular area because of the data.
So having this type of data, of course you can lead to policies that are more attached and focused on different perspectives.
>> MORTEN M. NIELSEN: Just to close the first round before we open up to the floor also online, so Pria, at research ICT Africa, you're also working with segmented data, obviously.
But what are some of the examples that, again, you find particularly useful for your research but also for policy recommendation that your centre is doing on a regular basis?
And again, how -- how does that become helpful in identifying these location or community specific or user specific gender divide?
Is it around income quartiles? Is it educational?
What do you see from your perspective?
>> PRIA CHETTY: Thank you very much. And thank you for the opportunity to be on this -- on this panel. For us, this work is core to our organization, and so we've been running for a number of years our after access research where we've prioritized segmented data.
So we collect data directly from individuals and households and we ask them very specific questions. And I think our findings really reinforce what Onica mentioned about the value of qualitative analysis and what Fabio mentioned about the -- those insights that come from when you're uniquely able to combine the data.
The questions around their access, do you have a smartphone, computer, are broadband at home or affordability. And then we also ask questions about their usage patterns. So what do they use the Internet for. Is it social media, work, education, health, government services. And I think this is to combat some of the assumptions that we make about who's using what.
We also ask questions about their preferred platforms. And then we ask questions about the level of digital literacy and skills that we're dealing with. And so, you know, what are they able to do and can they send emails, use online banking.
And then some questions around trust and I think also reveals very, very specific, yeah, nuances.
And then you know, I think when it comes to the question of barriers, this is where that contextual information really, really becomes valuable. So we ask questions about, you know, why don't you use the Internet anymore and is it because you don't have a need for it.
Is it about safety concerns?
And then of course in our data we have that valuable demographic segmentation that's by gender, age, income level, we include the urban category in there, but also disability status and language as well.
So this allows us to draw out very specific insights and the work that we've been doing is now absorbed globally into UN reports by the ITU, OECD and so on.
And more regionally to define indicators set for specific targets. It's not producing I think for the continent these insights that can never go away. So inputs into the ecosystem that just have to be longstanding.
So at the very minimum, we can confirm that data costs are a primary barrier, but we can do that in a granular way. 70% of our respondents cited affordability issues. When compared to Uganda, it was 61% that cited affordability. And we've got now some contrast between the different countries.
Then we also pull out these additional barriers. And one of them could be even just the lack of perceived need for some of the services that are offered. And now we can get quite contextual about the association between what's an offer and the demand.
The digital literacy gaps at a granular level, barriers such as electricity access, privacy concerns, and it brings out, I suppose, the multidimensional element that comes from these contextual nuances that isn't just about the segmentation, in fact, but, in fact, it's a multidimensional qualities.
And these insights, importantly, need to inform very specific recommendations. So I suppose the segmentation and the approach and the methodology, that's a big learning. But then how to present this information in a way in which it can be absorbed and utilized effectively.
So we know that education and income, as Onica mentioned are key drivers of access and use. But it means that we need targeted policies to address these. And our recommendations themselves need to be nuanced as to how this will actually take place and who would be the custodians of those kinds of efforts.
So it broadens, in fact, our policy engagement audience.
So while it's not strictly, I suppose, you know, data samples, I think there's also that value as Onica mentioned in understanding the lived experiences. Particularly in our context where we're seeing this huge variation.
We also need to understand attitudes across the ages and across the different segments. Cultural barriers. Specific use cases that draw, you know, particular people in.
And then also what they consider trusted community channels. And are we, you know, are we exploiting that to the extent that we can.
We know that cultural and linguistical barriers in schools, so how we are getting them connect and how we see those linguistic barriers and cultural barriers that are preventing children from being able to meaningful leverage online services.
And we also know that digital exclusion is now coming out of this data and I suppose it's longitudinal value is compounded when these values intersect. I suppose that's one of the challenges we take into this conversation, and I hope we return to the funding conversation, because that's on important one.
But as we progress and as we get more adaptive in this space, I think we need to be able to deal with this data in a way in which we appreciate the value, the intersectional data that's coming out and the range of inequalities that we're seeing and how they intersect. Especially for women, as Onica mentioned. But to know there are intersections between drivers and barriers.
For instance, young people anyone formal settlements access the Internet through shared phones because they might be hungry for job content, but they're accessing it at much higher costs. What do we can with that information?
The data is very telling, but I suppose the challenge for us is what do we do with the data.
>> MORTEN M. NIELSEN: What I'm hearing from all of you, including Guilherme, reflects around the discussion we have internally at the office. You can look at data at different levels. The national level data will be a heat map. The classical data have you used it for XYZ, what's your feature, what's your use of profile on a high level gives us a heat map that allows to us say we don't have to worry. Everything's bright green. Here's something going on, let's wait and see. And then here's it red, we need to work on that. In this geographical region has these type of inhabitants, then we dive down and look at the context and see what is it really going on.
But it means that we can target our decision-makers. So we layer our data and we dive in where we see the red lights flashing, so to speak, but where everything is green, we don't have to worry. Yeah.
Is that correctly sort of pulled out on sort of the logic in terms of the data segmentation when it comes to the granularity or did I misunderstand you? Any thoughts?
>> PRIA CHETTY: I would say maybe the challenge that it's a dynamic space. And so I would also express caution around the green. And as you mentioned, I think when you started this session you said there's value in the timing.
And at the moment the cycles, they're long. And so how long does the green stay green and what are the variations impacting the green.
I mean, we've learned lessons coming out of the pandemic. So you've got -- yeah. So we have to be cautious with the green.
>> MORTEN M. NIELSEN: Oh yeah, for sure.
>> ONICA MAKWAKWA: I think at best the segmentation just really allows us to be able to monitor, you know, what's happening. But also to begin to think about frameworks of accountability, right?
Because a lot of these divides happen within the context of a slew of policies that are there to drive inclusion, you know. Whether we are doing it for the results in access service funds or we have policies that actually very explicit about closing the digital divide.
But you know, I think that this segmentation really helps us to be able to monitor are we truly being effective, are we being targeted in this.
And what is accountability framework after 20 years of having a policy that says this and yet things on the ground look starkly different from that.
It's not a silver bullet, you know. I want to -- I think as we're talking also about how do we fund this data collection. I was just thinking about just sort of an underutilized resource that exists in data collection right now, but it's underutilized because it requires a lot of transformation.
And that is national census data. Every ten years without fail most countries find money to collect national census data. But how many of us have engaged with the national census bureau to get them to change their line of questioning, to collect even digital related data.
It was successful with Mozambique. But once that happens, you have another challenge. You've collected all this incredible data but about use of technologies within the country. Then what?
You know. That's another resource to invest and analysing the data and being able to make sure that it truly is utilized to inform intervention strategies, inform policy making.
So it's a continuous cycle. I think it's one of those where we have to continue to walk while chewing gum at the same time.
>> MORTEN M. NIELSEN: I'm smiling because I was studying in the UK and I was [?] twice for the census twice because I had two addresses. And I wasn't actually living in the UK at the time. And being Danish we hadn't had a census for I think 50 years because we don't need to do it. Our population registry is 99.9% proof, so they do a sampling and they do a direct survey out with all these things on an annual basis.
Again, different contexts means to your data and your data collection may vary. I think that's a key thing. But censuses can be one tool, but it's a snap shot every ten years and the data is already outdated the next day because things change, as you are saying.
Anyway, Fabio, before we open up to and ask comment for any reflections online and audience here, any thoughts?
>> FABIO SENNE: There's a very strong correlation between the left the GDIP of countries and the availability of statistics on digital matters.
You can ask it's because statistics help country to develop or the other side because they are developed they had money to fund the statistics.
I think it's both. And what Onica was saying I think is very key to us is sometimes with more segmentation you can -- you can make the green area very not green. And one example is the discussion on meaningful connectivity in Brazil, because we had -- if you take our general figures, we are about to complete 90% of the population having online services or connection to the Internet. So the country's in the green line that we only have 10% disconnected.
But when you include devices and availability of the collection and affordability and skills and other things, our figure is much worse.
So we only have 22% of the total population that we consider with meaningful connectivity.
So including this -- these hidden gaps, also it works for genders. So if you take an overall picture, there's not significant difference between women and men in terms of basic access to the Internet. But when we go to the meaningful connectors, we see 10% in the country in men and women in terms of connectivity.
So disclosing these gaps is very important for this type of messaging. But of course, the connection with good data and good policy is not immediate. So we understand this in statistics, that's why we do also capacity along with you and U.N. and other partners. We do strategy because we know it's not sufficient to have good data and the policies will get better immediately. So you need to invest also in this connection.
>> MORTEN M. NIELSEN: I think there's some interesting observations over the years on also stat statistical feedback. We were looking from a strategical perspective, sort of metrics and statistical analysis. And sub-Saharan Africa there were more people with access to the Internet than access to reliable electricity. And electricity is a precondition for charging your device and your router.
What does that seen in you need to dive into the context to see how many people have access to connectivity. But how can --inventive people can be in order to ensure that they get access to something where they see a perceived value, which is something that he with talked about earlier.
I'd like to hand over to Carmen, she's also from the global digital inclusion programme.
Is there any observations or questions from the online audience that you'd like to bring to our attention?
I think Carmen is saying something but it's not coming up on the audio. No. Could we maybe turn up the sound for Carmen online?
>> CARMEN FERRI: Hi. Can you hear me now?
>> MORTEN M. NIELSEN: Yes.
>> CARMEN FERRI: Great. Sorry about that. So we have one question in the chat which is how can we ensure that the segmented data collection respects the privacy and dignity of marginalized communities?
>> MORTEN M. NIELSEN: Anyone from the panel that would like to tackle that one?
And in the meantime, if there's any questions from the audience, please raise your hand.
>> PRIA CHETTY: So maybe everyone's looking at me because they know that I'm a lawyer and -- but let me start with that.
I think -- I mean, there's -- there's many facets to this and they aren't just the legal regulatory aspects. And so you know, at the very outset I suppose in defining the data that you're collecting and understanding the value of the segmentation, it doesn't necessarily include personally identifiable information.
And so -- and so in some ways you are -- you are freed from the -- from the data protection legislation of the policy legislation to the extent that you're not collecting personally identifiable information. That's the first cautionary.
The element that stems beyond just the legal regulatory elements and the concerns around privacy and the perceptions around privacy and the willingness to participate in this, I think our methodological learning has been the value of having local participation in the collection of the data.
So what you need is also buy-in for the process. And I suppose to distinguish good data from bad data, that you've got a willing data provider that is willing to give you the data that you need.
And so by using local researchers who are able to also deal with language gaps, understand maybe the concerns of the community and participating on such a survey, you're likely to address not just the privacy concerns, but the overall concerns about participation in the data collection itself.
>> ONICA MAKWAKWA: I would actually also say just stepping away a little bit from even regulatory and legal issues, I think ethics, you know, we -- you know, coming from a continent that, you know, for the most part we feel overresearched, over this sort of gaze on Africa in general around our way of living and all of that. It's really important to make sure that we are working with local partners, you know, and not sort of coming in from a global organization to study these people.
And I think we've got a history and a baggage that comes with that very, you know, approach. And so maybe also just assuming a decolonized approach to what's collecting the data so that people -- concerned people understand also how we are going to utilize this data. And you bring them along so that they are part of the programme as well and understand why you are collecting the data.
And I smiled when you made the example about the number of people were connected to the Internet vis-a-vis the number of people with electricity, because I think one of the studies that raised a huge gap was one compared to the number of people with mobile phones and tooth brushes. And it's those type of narratives that when we step back and look at them, what exactly are you trying to argue, you know, in comparing number of mobile phones and number of tooth brushes.
Because it anything, it actually exposed the ignorance of the researcher themselves because there's other ways of keeping -- of dental health that's not just only your western toothbrush and toothpaste kind of methodology, right?
So local context is not just kind of coming in and having taken me out for coffee and having a conversation with me over coffee, but it's also really about allowing me to also within the community lead some of that collecting. So we're not just kind of soaking in and having the consultants that we have.
But you know, empowering local communities also to be part of that data collection process as well.
>> FABIO SENNE: Those questions are discussed a lot in the data community debate. UN has a UN data forum which is a space where those types of topics are discussed.
And it's interesting because there are some friends related to what my colleagues just said, for instance, there's a concept of citizen-generated data which is now trending in a lot of countries where in specific countries you can have the citizens involved in the process of generating data with more quality, less cost and so on.
So this is trending.
And another discussion that I think is relevant for this is that when -- when -- with this brand of mobile connections and increase the number of Internet users and digital platforms, there was a general expectation, especially among governments, that the problems of data will be solved because everybody is connected to some device and there will be traces, you know, where people go, what they do online.
And we have data, in a data society, we don't need monitoring or service anymore because the problem is solved. Now the data community is -- there's a pushback in the discussion, okay, we need complementary sources of data.
Sometimes we did not solve the thing of the digital platforms. Much of the data are private and not shared for policy purposes. So there's this discussion on how to access those type of data.
Now we have satellites that provide other types of very interesting data and can be combined.
So I think the data community's now in a process of okay, we need to combine different sources to get the better solutions from citizens and generated data to more technological tools that can provide the best information you can for policy making.
>> MORTEN M. NIELSEN: I just want to double-check, is there anyone in the audience that have any observations or questions that they would like to highlight? If so, please feel free. I see no one moving.
Yes, please come up to the microphone and please introduce yourself. Thank you.
>> KEHO: Hi, I'm Keho, and I'm student in University in Germany I'm studying digital media in society.
I was wondering like you mentioned about privacy of citizens and how do you see, like, the consent to the use of data for citizens, so especially those who are marginalized systematically, like maybe they are people of course that would like to share their data because of the sensitivity. And also there are cases like those data are used to profile them and expect, like, a criminals or something like that. And that's used in a way to reinforce marginalizations again.
So how would you consider like including digital rights, education, and also providing opt in and opt out options so that they know not only their rights, but also how to exercise their rights?
Or would that be too big scale to implement or something like that?
And another thing is, yeah, so how do we prevent the use of segmented data to be interpreted or used in the way that could reinforce.
So when we use data, how do you prevent and those in a way that would reinforce marginalization.
Yes, I would like for you to --
>> MORTEN M. NIELSEN: Excellent questions I think. We can start with the privacy, but like the -- how do we use data so we don't reinforce insisting patterns of exclusion, for instance is really, really interesting.
Anyone want to --
>> FABIO SENNE: I think these are very good questions. From the perspective the privacy, we have a very interesting survey that we did with individuals in Brazil asking them about what they perceive about their privacy online and data protection and so on.
The survey is very interesting because from one side there's a growing concern among citizens about how the data are collected and used.
The main topic in the case of Brazil is facial recognition, for instance, or when there are this type of data collection is considered the one that they are more concerned. And also health data.
So health data is ultra sensitive and people feel worried about those two particular types of sensitivity.
But it's also interesting that still there is not much literacy on understanding how -- how digital platforms collect data from -- from people. So there's more concern regarding, for instance, financial frauds or something that has to do with payment than when you go to a social media and put your photos in.
So there's not much understanding of how this -- this -- this model of data collection works. But when it comes to financial data, they are more concerned because it's very --
>> MORTEN M. NIELSEN: Very, very interesting. But if we're looking at addressing or collecting data to address the digital divide, do we need that level of -- that type of personal data as in, what have I paid for, what is my health data? We do not, do we?
>> FABIO SENNE: Yes, I think they're two different -- traditionally, typically in the data for policies collection, you have an anonymized data even for survey and administrative -- you know, for in Denmark you know that you have a sophisticated administrative data that you even don't need to ask people things because you have --
>> MORTEN M. NIELSEN: It becomes a political risk assessment whether or not we want to use it.
>> FABIO SENNE: And there's a discussion, there are different solutions whether in some countries you cannot ask for ethnicity issues because there is concern of this can -- so society will have different solutions. And I can speak about the case in Brazil.
There's not a distrust necessarily distrust when you provide information to the government. For instance, we have a very important social income programme that is national. And to be in this programme the government needs to know where the poor people are. So there's this trust in some cases who have to provide data.
It's also the case of public health. So public health data can ensure that you are focusing on the right person.
>> MORTEN M. NIELSEN: Yeah.
>> FABIO SENNE: But there are discussions on what type of data needs to be collected.
>> MORTEN M. NIELSEN: Because that's an interesting discussion or topic for discussion on access to opportunities through technology, accessing to banking services online, shopping online, government services online. And that's a very interesting element.
But to continue.
>> ONICA MAKWAKWA: Yeah, I like that question because I think that it's important for us, especially in forums like this to continue to raise awareness for this as major concern about consumers in particular.
I think it's an indication of the trust deficit that exists even amongst all of the stakeholders and even IGF for example.
And during COVID-19, most countries adopted COVID-19 apps where you were encouraged to download something on your phone. I just know that for a lot of people my country personally, we learned that after we signed up for something related to COVID-19, we started receiving SMSs from a particular political party for the next local election campaigns that happened immediately after COVID-19.
So there definitely is that bridge of data privacy, right?
But what I also want to encourage as digital rights advocates is that when it comes to data, the offline and the online should not be any different. All of us probably, I don't think it happened here in Norway, but in most countries, when we visit and you check into the hotel, including in your own country, the receptionist takes your passport and makes a photocopy of it.
And you leave and leave that photocopy of your passport behind when you check out, right?
You know, let's ask this question around data privacy and safety, even if these offline instances where a security officer at a building asks for a copy of your passport or your ID number and all of that. With all of those are interrelated and they're not just unique to data.
I think we'll make better progress in educating people around also protecting themselves in terms of their data.
GDP uses anonymized data, so there's no way to track who or what. But from an exclusion and location point of view, certain communities could come out as vulnerable, right?
And I think there needs to -- we need to be honest and open to exploring some of that. You know, the challenge is if you don't come to gather it, it doesn't exist and no one has the opportunity to address these gaps.
>> MORTEN M. NIELSEN: Pria, you've been indicating you have something to say here, so I'll let you continue on this track.
>> PRIA CHETTY: This is so close to, I suppose, the work that excites me the most. It's in the data for good space. And I think your question really, for me, triggered this question about data for good or data for bad.
And you know, how do we manage that.
And so you know, we said that this kind of data might fall outside of the data protection kind of regulatory space. But I want to say that even for nonpersonal data, there is no reason why we can't exercise some of those standards that we would apply to personal data.
And so you know, if you're collecting personal data, you are limited by the purpose for which you collected it and you need to use it within those confines of why you collected it.
You've got to uphold security standard, you've got to exercise a level of restraint and collect only what you need.
And so there are valid principles that can be taken into how we do this kind of work. To Fabio's point about the technology running away from us and citizen generated data, we have to get very serious about, you know, not just personal data protection, but the protection of datasets, even aggregated ones.
Because there are those harms attached to it, and there are those opportunities also linked to it.
So something that we're working on is just trying to understand in the way that the datasets are compiled and made available and the data that sits in them, who actually gets value from it and what about the citizen and the communities that have contributed their data.
To what extent can they also then have access to it. So one of the questions we can ask ourselves is that, you know, have we meaningful engaged with the community when we receive the results to, you know, to talk, to have a conversation with them about what this means and the decision set that they have to improve their digital inclusion, you know, characterization.
And so do we take that data back to them and we allow them to use it?
There may be budding entrepreneurs in the communities, and once they have access to this data to know who's connected and maybe there's some level of enterprise that can emerge locally. There are many cases like this where we have to force ourselves to think about, you know, when we have the data, what do we do with it. And have we created a pathway to go back to the community and back to the citizen and make sure some of those benefits are there.
I think on again, Fabio mentioned in some ways the digital inclusion problem globally in the development world is big business. And so in some ways we've got a willing buyer for this kind of data.
And I think that doesn't excuse us from the freedom to say how can we use this data responsibly, but also to get value locally from the data.
>> MORTEN M. NIELSEN: There's actually a very -- do you have a follow-up?
>> KEHO: It's really intriguing to me that to balance approach and marginalized groups and to do that we need to use the data. But at the same time, they need to be protected because -- because they are marginalized at the same time.
And I think really intrigued to this balance and how it will be realized or maybe it's really difficult because even for me, looking at like terms and conditions take out -- or I would not read every sentence, but I also think that those designs can be improved so it's really user friendly.
And I would just see in one site that okay this I would want to say yes, but this no. Also for like cookies. Yes.
>> MORTEN M. NIELSEN: That's interesting. It comes back to that layering of data, I think. And there was an interesting example here in Norway a couple years ago where the journalist at the national broadcasters went and bought data from the worldwide web, marketing data from social media, from the banks, et cetera. And they could basically layer it and they identified a Ministry of Defense employer to his local commute to and from his home -- his place of work who he was married to, where his kids get to school, when he dropped off the kids and when the wife dropped off the kids.
And it was really interesting in terms of that -- that anonymized data, but when you have a specific objective in mind and you start layering, they could actually identify those type of patterns from this individual. It was obviously generalistic investigation, but it raises some concern even around anonymized data.
But how do we take and build that into some of these sort of regional and global frameworks? How much data do we need also to compare ourselves country to country or region to region, city to city?
What are the type of experiences and the type of datasets that you would say are really key in order to ensure that we get as many people online so they can enjoy the opportunities of the worldwide web and similar while minimizing the negative impacts of the data collection?
>> ONICA MAKWAKWA: I would add to that. I think this brings us back to open data, right?
So do I need to still collect the same data if they have collected the data for a community?
So they may be looking at, you know, access in terms of digital skills, but I might be interested in something closely related to that, maybe women specifically. But with disaggregated their data, they've collected the data.
I think the other question is how do we leverage existing datasets amongst organizations by standardizing the collection to make sure that it's, you know, we've got this segregation of communities so that not everyone has to collect the same data from the same group of people as well.
>> MORTEN M. NIELSEN: It's a very interesting challenge. We had this discussion with our colleagues at CETIC in Brazil when we were doing a study on youth and young adults.
So the under 18-year-olds and what it meant to them. And in Brazil they were defined as 13 to 18-year-olds. In Europe it's 13 to 15-year-olds. And we're seeing research on the gender divide, where even if it is data collection by research teams, they segment it, they do excellent work, excellent data collection, but they forgot to use the same able group as the national statistical data agency, when means it makes that data work and the data analysis more time consuming or less impactful because there was some small mistakes or small missed opportunities in alignment of data from different sources.
So there are some national, regional or even global sort of standards or rules of thumb that you would recommend when it comes to segmentation?
We've talked about age and income levels. We've talked about activities online. Are there any ones that you think are almost like universal that you would recommend that you look at?
>> FABIO SENNE: What I can comment is there are different levels of data that you can -- and at CETIC we try to do both. For us, there's a global decision on minimal standards of data. There's the UN partnership or measuring [?] for development, with set meaningful rules.
And because of that we can compare data from Brazil and other countries. So we more or less follow this -- this international standards.
And there are debates on what measure and how to measure. Including a list of disaggregations.
I've also mentioned the G20 case where there are also recommendations for disaggregating the meaningful connectivity data.
So this is at the global level. But what I think is important is that different from the past when we normally have all the data from [?] concentrated in one national institute of statistics or our institution, now there's an eco system of data, the data's not only in public settings, but also in Civil Society, in the private sector.
And coordinating these efforts, I think it's key. And so and also within government, sometimes we have silos that don't talk to each other. So educational data is not linked to financial data and you cannot cross these types of things.
So there's an -- so more or less we believe that this discussion on the system that manage data that is not only public, but public and private. It's key for making solutions.
And of course, the data that one municipality wants for planning urban mobility is totally different from a national Ministry of Education planning -- so this type of granularity will come depending on the policy need you have.
>> MORTEN M. NIELSEN: Pria, Onica.
>> PRIA CHETTY: It's very clear from the discussion we're having today that even though it's not broad based, the segmentation work is reaching particular level of sophistication and maturity.
But as Onica mentioned, we're not comparing notes and so the opportunity from there was a regional initiative at a global level is to bring the key players together, to understand the different methods and UNESCO has set up some tools and indicator sets that can be leveraged.
But what is it that has come from this qualitative work and from these unique combinations that we mentioned and these intersections that also add to those frameworks that exist? And how can we develop it so that the data's more reliable and more accessible?
I think what we're being challenged to do as an organization now is -- is make the data more accessible to people who have questions that we never conceived of. And I think to also anticipate that they aren't researchers and they aren't policy experts, but they will have very unique requirements from the dataset. And how can you create something that is accessible and allows them to use that data in ways that you haven't conceived of.
So imagining that it's going beyond this community and is being used in new ways. Because that's what we're seeing now. We can't really identify how the data will be used, but we want to make it accessible for uses that we -- that we didn't conceive of.
I want to exercise, I suppose, some caution, I would say, in bringing the data together. I think we need to be very deliberate and well intentioned because there's a potential and propensity for massive harm. The data is brought together in ways that we will lose that element of control or accountability for why we initially put the data together.
And there's so many examples where, you know, data lakes have gone badly. And just a range of vulnerabilities. So it isn't -- I'm not sharing an excitement to bring all the data together without thinking through very carefully what is the process that we use and how do we get a little more adaptive, I suppose, in the way that we --
>> MORTEN M. NIELSEN: You've had some very interesting reflections from policymakers and civil servants and including in regions like East Africa where you have these massive drives for data lakes.
But without having a data classification scheme that says this is data that you can easily rely on, because it's high quality, this is not as high quality because that defines how will I interpret the results. How reliable is the data.
But also, I can their data with other data, privacy, security, et cetera.
Is coming up from the organizations within the public sector in East Africa itself, because they see that they've sometimes made the wrong decisions. Not for bad intention, but because the data was not of the quality they thought it was.
Or it couldn't be compared because it was defined differently.
So there is some really interesting self-reflections both in the Global North, because in Denmark we've had the same reflections in the public sector about the reliability of the data.
But also in -- in emerging economies where data is available but are we sure of the quality to make the right decisions.
So that's a really interesting dialogue.
Onica.
>> ONICA MAKWAKWA: I just want to share an example of just one of the exercises we did in Ghana several years ago. And that was to create data and research working group where they mainly focused on kind of mapping where existing ICT data was within the country.
So that as researchers they talked to each other, they know what has what information and how frequency is that data updated, including some level of success at bringing some of the operators, the mobile operators in.
Because they're obviously also sitting with tons of data that we may or may not find useful. But it wasn't about bringing all the data in one place, but mapping who has what data and being able to also negotiate some openness for researchers who may be interested in doing research and looking at particular issues to be able to know, you know, where they can rely on.
It also reduces the size of household surveys that you have to do when you realize you don't need 40 questions because the three operators within the country can tick off maybe ten of those.
>> MORTEN M. NIELSEN: I'm loving that, because it was actually the topic of some recommendations CETIC and others did in G20 policy brief last year under the Brazilian presidency as in 155 countries, you must present legal identity in order to get a mobile phone or Internet connection.
Is there ways and models that we can use that and that Telco licensing for the Telco providers to provide us a snapshot on gender, on age groups, on basic elements that are anonymized to the Telco regulators so they get that snapshot.
But then also provide certain usage data like on certain types of IP addresses like online commerce, banking. So you get that data in snapshots, but anonymized to create sort of the initial heat maps, lower the burden of data collection that then allows you to also dive into the context of the green flashing lights. But it gives -- it's a new model and with the Telco regulators, you can potentially create those type of partnerships by making it part of the operational responsibility to provide certain types of data so decisionmakers can address the digital divide and spend more time on diving into the specific challenges and the context rather than these blanket decisions.
So there are some potential models at play there.
I would just checking the audience to see if there's another -- another set of questions before I raise the last question to the audience. I've seen online there's no more questions online.
But if you were going to do one recommendation that you would ideally like to see happen in the segmentation of data collection to address the digital divide, to make those better decisions, what would that one recommendation be for the next 12 months?
>> FABIO SENNE: I can start. This is a very difficult question, but I think there's one thing that we kind of discussed but I would like to reinforce is that for a long time that's because we do -- we do mostly surveys or they interview one individual.
So we try to think about digital inclusion as an individual [?]. So you take gender and age and all these factors and everything. We're talking about [?]
But most the problems are collection problems. There's lots of discussion on community networks, how they -- how communities can view normative models for digital inclusion.
Schools are also important in this debate. We have -- in Brazil we have libraries as one of the most spread public infrastructure that also need to be engaged.
So I do think that -- try to think more in collective -- in collective measures or collective exercise to understand better the situation.
One -- one more example, in this meaningful connectivity study, apart from having one indicator, does the household have a computer, which is a traditional indicator that everybody measured, we decided to say if the household has one computer and one person living there, you're okay. But if you have one computer and ten people living there fighting for the device, what's the quality of use? So we decided to calculate a ratio of [?] for device or you can do this with income, what's the percentage of the income of the device that is -- so these types of things takes the household as a collective of people rather than individuals. So I think facing the collective challenges is useful for policy in digital inclusion.
>> MORTEN M. NIELSEN: Yep.
>> ONICA MAKWAKWA: I won't answer the question but I'll pick up on what you just said around looking at household versus individual. And that's because we actually did an evaluation of a model, a subsidy model of one tablet per household in Uganda which was very successful.
They focused mainly on female-led households, used [?] funds to provide a tablet to the households. And very interesting stories, please do look it up on our website, on how that actually helped to empower, you know, the least suspected subject for that intervention.
And you know, even though the focus is maybe the women in the households, it's the children who actually benefited the most and it's education that was highly impacted by that initiative.
So I really like that notion that, you know, it's really important for us to also just sort of look at communities the way they are organized.
>> MORTEN M. NIELSEN: I mean, there was some interesting discussions between the ITU and the Arab regions technical working group for statistics where actually the recommendations from the Arab group on the definition of household had changed at ITU because it wasn't -- it was based on a traditional nuclear family, you know, Global North concept and not on multigenerational or alternative household structures that you see all over the world, even in the Global North.
So it was really, really interesting in that regard. But I'm taking away the time from Pria to either address the same question or come up with an actionable suggestion for the next 12 months.
>> PRIA CHETTY: Yeah. Maybe I -- I suppose maybe put differently what is the question that keeps me up at night for this kind of work.
And I would say it's about the sustainability of the work. And the adaptation maybe of methodologies and how we do it.
And I mean, it takes a long time and it's quite costly and do it well you've got to really have the local participation and so on.
So how do we build on the current methodologies and how do we make it more sustainable?
And how do we make sure that there's continued interest in the process to get this data, because there's high levels of interest in the data but not always in the process to collect the data.
And linked to that sustainability question for me is then what is the compelling way in which we approach sharing an exchange of data?
And you know, as you spoke about the opportunity with the telecom firms, I would say to you the biggest challenge, because they would probably be doing that analysis already and have all that kind of segmentation there, they have different reasons for it.
But to get them to share it as data for good, how do present a compelling case to them had the how do we build those data centers, and if we don't figure that out, I feel that this work will not be sustainable, because it will be replaced quicker, technical measures that don't necessarily have the rigor attached to it the.
We've got to balance it, adapt it, and how are we going to do that to make sure we don't ever lose these insights.
>> MORTEN M. NIELSEN: Just to try to summarize on the key takeaways. There's a consensus that we need better data around gender, income levels, so forth. But there's a recognition that will only get us so far to address the digital divide. We need to then go down and analyse the contextual, which becomes to pair prize Onica is more of a qualitative assessment. It's not a statistical empirical assessment.
And there's also more or less a consensus on the panel and with the audience that, yes, we need to balance this sort of anonymization when we start laying across different elements.
And lastly that there are some opportunities for different types of partnerships for data collection both with the private sector, particularly Telcos, but also with local communities and getting them involved in driving the decisions in order to also target the more tailored initiatives that will include them and give them the opportunities for being digitally included and benefit from that.
And there is some interesting sort of user segmentation divide in there that doesn't just go by gender or income level or marginalization, but are cross-cutting. I forgot what you called it Pria.
>> PRIA CHETTY: Intersectional.
>> MORTEN M. NIELSEN: Intersectional both on the divide and the inclusion.
With that note, I'd like to thank the audience and the panel for your time and details.
We will be summarizing the discussion of this panel and provide it online in the next couple of weeks. Obviously the panelist will have a chance to also comment on that draft. But we'll be sharing that.
In the meantime, don't hesitate to reach out to any of us or to our organizations if you have further questions. Information details at least to the websites of all the organizations both present here and those who unfortunately couldn't join us because of -- of airline logistics. Thank you very much and enjoy the next couple of days of IGF.
>> PRIA CHETTY: Thank you.
(Applause)
