IGF 2025 - Day 3 - Workshop Room 4 - WS #288 An AI Policy Research Roadmap for Evidence-Based AI Policy

>> Good morning. We are extremely happy to see all of you both in person and online. My came is Tatjana Titareva. And I'm going to moderate this session in person and I also have a co‑moderator online. Isadora, would you like to introduce yourself?

>> ISADORA HELLEGREN: Hi, everyone. Welcome to this session. My name is Isadora Hellegren.

>> TATJANA TITAREVA: Thank you so much. Today's discussion is to discuss AI policy research roadmap that we have developed in the community on AI policy researchers. You can see the QR codes both to the roadmap as well as to the community that we are going to launch soon and we would like to ‑‑ we would like to achieve the following goals for today's session. Discuss key concepts of the root map and secondly to discuss with you both in person and online how can AI policy research support global corporation in AI governance while preserve think the regional diversities and certainly, what mechanism best supports the access to an effective integration of AI policy research into AI governance processes. So our session is structured in the following way. We will start with the background of the roadmap by Isadora and then we will have a keynote speech by Professor Virginia Dignum who is based in Bangkok and we will go into the speaker interventions from different regional and sectoral perspectives. We will open the floor to the discussion hopefully for around 15 to 20 minutes and then professor Virginia Dignum will close the session with her final remarks. Isadora, the floor is yours.

>> ISADORA HELLEGREN: Thank you, Tatjana. It is a pleasure to be here with all of you today. I will share a share with all of you online participants as well which we would like to share one word on what is the most important thing to consider in AI policy research. So I will be checking back in on this question a little bit later. But now, let me begin.

We are very glad to be having this conversation here especially at the Internet Governance Forum. Many of the challenges and opportunities that we face in AI governance are although sometimes treated as such not new. We have much to learn and we do find ourselves at a moment right now in AI governance where we are coming out of a strong embrace of the many possibilities of AI, much like the early days of widespread and popular access to the Internet where much of the AI landscape and ecosystems remain untested and unregulated, but we have also reached a point where we have been able to identify and document many of the risks, harms and impacts of this widespread adoption.

Tackling the multi‑dimensional and challenges and opportunities of AI requires us to move to action and move forward in an informed way. So for responsible AI governance to be able to respond to actual needs as defined by those who experience them, we must bridge the gap between research and practice to ensure robust and evidence‑based AI policy. This need led us up to the inaugural AI policy research policy by the AI policy lab and Milan. Eager to address this need for best synergies between research, policy and impact on realize responsible equitable and sustainable AI for the benefit of all. And following the summit, we established the global AI policy research network or global poll. I am not sure we have the slides up and running. If we do not, we'll have them up in just a moment where you will be able to access the QR codes where you will see more about the global poll network.

A core objective the global poll network is inform approaches to AI governance by sharing best practices and fostering collaboration on developing AI policy. This includes advancing research that meets the glowing need for governance grounded in ethical, transparent and evidence‑based practices to shape inclusive and trustworthy policies. The global poll network is guided by the AI policy research roadmap central to our session here today. This roadmap was developed through collaborative discussions at the inaugural AI policy research policy. This roadmap provides guidance on how to ensure advancements in AI aligned with local, ethical, legal and social priorities. And in a minute, it will be my sincere pleasure to hand over to an initiator of the inaugural AI policy summit and founding member of the global poll network Virginia Dignum. Virginia will open the session by highlighting the critical role of shaping inclusive, context aware and globally relevant AI governance drawing on insights from the AI policy research roadmap, she will outline key priorities for responsible AI and strengthen networks while respecting regional diversity and interdependence and frame this session focus on practical pathways into policy for ethical effective and future proof AI systems. Welcome, Virginia Dignum, professor of the AI policy lab.

>> VIRGINIA DIGNUM: Thank you very much, Isadora. No pressure, I see. In the 15 minutes, I will be able to meet the task, but let me start by sharing my slides. One second. Always changing from one to the other. Okay.

So the AI roadmap for AI policy research and thank you, Isadora for the shorted introduction that you made about how we started and where we are now and also about the community, the network that we are hoping to launch today. Trying to give a little bit more of the background on why do we start this, how we see the roadmap being used and what for can we use this roadmap. Why first in order to understand why we have this roadmap for AI policy research if, of course, is important to understand what we mean by AI policy research and why we need the AI policy research is now a surprise to any of that you AI is shaping societies in a profound and very impactful way and it's affecting positive and negatively in many different ways in our rights, our decisions and our behavior and our agency, but also shaping and modifying global power dynamics. That is more visible since the last few months and geo politics are changing. Did you despite the change of society, responses are fragmented, reactive and dominated by short‑term interest. There is no way or no continuity or globality on addressing policy the impact of AI. The get between the development of AI and our understanding of its impact is also widening. We are seemingly able to change quicker the technology and the systems that we are developing than our understanding of what exactly the systems are doing. We are researching and multi‑disciplinary research and I would say further we need to go beyond the disciplines because AI is not just the technology. It is a technical system. It's a system of systems and one discipline alone is not sufficient to address it. So we really need to look at how we can go beyond disciplines and create new field to address all this complexity. This is not just the technical challenge. It is really a societal imperative as we look at the battle of AI in society. If you look at that, it's always powerful to use this image by the modular Internet foundation showing our AI since the world and this is in 2022. If anything, this image is even more execute than what is showing in the slide. Of course, even within those blue areas of the world, the acuteness of access and participation in AI is different communities for different groups and different demographics. We do need to understand the power or capability of AI to shape and to affect our abilities of decision making around there and also the power structures like I just said. The current deployment tends to reinforce existing inequalities and keeps marginalizing no western worldviews and Indigenous knowledge and other types of knowledge. So responsible AI needs to go among the technical fixes. It needs to ‑‑ would require a much broader understanding and the instruments to do that for inclusive governance for our understanding and accountability of all of us.

So the context in which we are now with regulations and policies which are many and growing and in one end. On the other end, we also see again and again a pushback towards regulation with this completely false idea that innovation is hampered by regulation which has been proven again and again that is not the case. We see all these regulations and policies. I am in Bangkok in the UNESCO forum and it is one of the things we discuss here. On the other hand, we see pushback on regulations and policies because the corporate power on developing AI remains. Monopolies determining what AI is and why we are using AI, what can AI do or not and very much outside of the control of any Democratic process. The impact on states is huge. It may be dependent and much more evident now given the changes in geo politics before. This affects smaller countries in the south much more than other countries and economic interests and all this development are much more laid by interests than by the impact and the ability to serve communities.

If look, we are at IGF today. We also ask to look at the infrastructure shaping the global power and assess. The interest of the decisions are very deep implications to the assess AI to surveillance and digital rights and also to human rights in general. And also here, we see a fragmented governance which are new and accountability and therefore, the need for inclusive transparent and equitable participation is again very important. And again, we are seeing more and more of this debate showing our different and the impact is in the north and in the south.

So regulation of AI is one hand, one part of it, but we also looking at our AI enforcing regulation and our AI systems enforcing existing regulatory systems. And we are also seeing AI in forming the way that the regulation is put forward. And looking at this cycle again, we need to address the research, the development and understanding of this complex feedback loops from perspective of multi‑disciplinary around comprehensive research approach.

Because AI doesn't happen to us, the current narrative is something like the weather. We have no idea how to control it. The only thing we can do is to take an umbrella if it is going to rain. Address the effects of the weather, but AI is not weather. It is developed by organizations by people and it is ultimately depend edge the way AI looks and what we're doing with AI is dependent from the choice that we make with making this choice which are considered in the choice and how we prioritize in the values. If we're not part of this conversation, someone else is taking the decisions for us. And also, it is increasingly important to understand and to provide both the research and the tools to ask the questions there. The question whether AI is the best option. It's not about using AI because we can, but we need more and more to be able to understand how to use AI because we should. And also when not to use AI because the impact of AI might be bigger than not using AI. All the solutions are much more about societal humanities and social science than just about the technological science. And please stop me when the time is over because I cannot see my clock somehow on my screen.

We talked about innovation versus regulation so Let's move and say a limitability more about the roadmap for responsible AI policy research. What we want? We recognize the risks and the responsibilities of AI, but this is just the beginning. We believe and think that coordinated scientific research across disciplines, behind disciplines and participatory and integrating not only the academic research but again different types of knowledge from contextual knowledge, from the knowledge of the affected populations needs to be integrated in order to guide policy in a way that it is really grounded in ethics and sustainability. We need the tools and opportunities and methods to identify the gaps to prioritize global inclusivity and the mechanisms for the responsible AI development. For too long, we have been in the field of AI talking about principles and guidelines and now it is time to go on and deploy AI responsibly. These are what we want to achieve with AI policy research roadmap.

I think the slides, the QR code has been shared already. We need it because change is political. It's not technical. It's technology alone that's not really moving and going to create the change that we need. The social change that we need, the way that we had addressing the impact of AI. Digitalizations raise many questions which are still unanswered, but we cannot wait for answering the questions about digitalization and then go and address the questions of the next wave, the AI wave. This feedback loops again and again is the core of the issues why AI policy is especially important.

The core principles of our roadmap are the following and I will quickly go through them because we can discuss that later in the Q&A. Human and planetary welfare, accountability and transparency, inclusivity, diversity and capacity building. Ethical research practice and ethical governance and equitable economic growth. Those are the principles we believe should be guiding and leading research and application of AI policy, research and AI policy and application and deployment of AI policies.

Our research priorities are around trans boundaries AI governance around means and tools to define and measuring the benefits of AI and at the same time, be able to define and measure the challenges and risks of AI foresight and proactive regulation, looking at codes of conduct, looking at how we can embrace and do research based on collaboration on participation and not just make that a kind of side sort or check lists. We also need to look at different sectors and how the policies and research policies need to be done to be aligned to the needs and the characteristics of different sectors.

The guiding actions that Isadora already talked about. We want to establish the community practice to which we invite and welcome all of you. We are working on visiting AI fellowships at different groups involved in the community. As AI policy lab already starting fellowship programs in place. We have this annual AI policy summit. Save the date. It will be middle of November in the Netherlands. The call for action that we are sharing with you today, but we also want to work on AI policy briefs and supporting capability for student and staff exchange and are the ways to support capacity building and AI. So the time to act is now. AI is shaping our collective future and if we don't act today, we need to act today and we need to act today not only from the perspective of desperate policies, but comprehensive and scientific ground research on the policies. The implementations that we are making. Thank you so much.

>> TATJANA TITAREVA: Thank you so much, Virginia. We are looking forward to a discussion later on after the intervention part reflecting on your presentation.

Now we will move on to several interventions and we'll start with an EU perspective by Alex Moltzau. Alex, the floor is yours.

>> ALEX MOLTZAU: Thank you so much. My name is Alex Moltzau. For today, I'm not speaking on behalf of the European Commission or representing any kind of official views or perspectives thereof.

So first, I wanted to say that I was there in a personal capacity as well as this workshop and I found it to be really wonderful to see everyone gathered together to kind of how to collaborate as different research 60 ears and how to engage with policy and trying to build this bridge. My background is in social data science. So in a way, with the data science background and also this social science aspect, but also Artificial Intelligence and public services, however, I'm working now directly trying to shape policy making in the European area and in the internal mart as someone who is working in the European Commission. As a policymaker, it is invigorating to come and participate in the research community. Five years prior to joining the AI office, I worked with the research community of nor way nationally with AI and robotics.

So maybe I'm biased, you know, but maybe that's a positive bias in a sense that if we want good policy making, it's really crucial that we have an evidence BASIS upon which we act. In many ways, we are running after all these targets and we want to hit all these milestones and I'm working directly with the AI act and I'm writing and implementing act with regulatory sandboxes. I think this way ‑‑ what way can we work to create feedback loops? How can we think of ways of involving the scientific community. I'm also really happy to say that is kind of like a core part of the AI act itself. A lot of processees surrounding this work with AI act. And I have two points in this regard and the first one being the scientific panel. So engrained within the AI act itself is this mechanism that establishes a scientific panel. The implementing act for this has already been accepted and the call has been published with the autism. In a way, we are kind of like actively recruiting experts in systems, AI impact or related fields. 80% will come from the EU, but it is also open to international experts who joined this scientific panel. It's not just a nice gally to look at. It has a governance mechanism. In a way, the panel can raise qualified alerts about certain systems as kind of general purpose AI models that doesn't immediately fit those explicit requirements. I think this is quite an important function, but also an important way the scientific community can directly engage into the regulatory framework of the AI act.

The second way I think is a process that is called shaping the code of practice. So this is the code of practice for the general purpose AI models and you have about 1,000 stakeholders and many of them are researchers and most of the Chairs have research backgrounds. It's really incredible to see the reflections in that community and I think, you know, we also have to think about our regulatory work because the field of AI requires the deep main knowledge. How can we not ignore scientists. I think this is something that our trends and policy making that we want to avoid. We have to listen to scientists. We have to listen to researchers and receipt facts. If we do not do that, then we'll be worst off. I we won't have best outcomes or we won't know what the outcomes of. What outcomes do we want as citizens? What outcomes do we want as communities? And talking back to Virginia's point, AI is social. It is part of our lives. It is part of our societies and we really have to work hard to make sure that it works in a way that doesn't create any or worse in the climate crisis or the Sustainable Development Goals. I think one of the inspiring papers from Virginia and mapping sustainable goals. So I think there is so much to explore, but if we do not listen too research and scientists, we have a big problem, people. So everyone here today, let's make sure that we bake that into everything that we do on our regulatory practices to shape a better environment for a responsible AI.

>> ISADORA HELLEGREN: Yes. Can you hear me?

>> TATJANA TITAREVA: Yes, we can.

>> ISADORA HELLEGREN: Thank you so much for providing your perspective on this, Alex. It is great to hear from someone working very concretely with this. So thank you for this.

Now, I am sad to inform the audience here that due to circumstances, our third speaker, Neema Lugangira is unable to join us today. I am happy to say that we'll have I chance to hear from Vice President consulting CGI in the netter lands who will be sharing his reflections on the AI policy research on IT consultancy. Welcome.

>> ELTJO: Thank you. There is a little bit of circumstance here because I ran into Virginia when we were working on a project together. I hadn't seen her for years and she say you can say a few words at this IGF forum. I had an inspiring session with some of the experts within CGI. And hence, I can say something about or position on this.

For those of you that do not know CGI, we have an IT services company. About 100,000 employees in 40 countries and we do mostly IT consultancy and integration. We're also very involved in AI policy making. We have our own responsible framework and I'll tell you why in a minute. We are also one of the first signatories of the AI impact. The people working there and they know CGI.

The first thing I would like to say is this role of consultancies and system integrators falls into a gap in the policy so far. Looking at the AI act, you will see that there's a lot of attention for the providers and for deployers. We tend to be in the middle of that. We integrate systems. We give consultancy and give recommendations about deployment, about we are not a provider about AI systems. So that gives us some unique problems. So one of the problems is divergence of AI policy in different geographies. Some of our clients are multi‑nationals. I was happy to look at your focus. It is system integrators. That is also one of the reasons that we have our own responsibility framework and we have to keep it in line with all the various policies around the globe that our clients have to comply with.

Another thing I would like to say is about the level of detail of the policies. And here there are two points that seem to be paradoxal. It is very important that the policy is level of principles. If policy becomes too detailed, then if becomes very hard to maintain and especially when it comes to technology. This is a technology that is evolving very quickly and if the policy is based on things like the number of point operations like we meet in AI, then is not really maintainable. It presents problems.

So in that sense, it has to be on the level of principles. On the other hand, there have to be very practical guard rails. It will indeed, speed up innovation and you need to stay away from the road blocks that slow innovation. Organizations have clear guidance and they speed up innovation in AI terms because they don't have to look over their shoulder. They have no uncertainty about are we breaking laws and are they more at risk. The sooner this gets clarified and becomes very clear, the easier it is to fast innovation. Policy does not slow innovation, but it speeds it up. So those are the most important points that I would like to make. In order to do that, the policy researchers need to collaborate closely with the Private Sector including the systems integrators. As I said, we are already doing that. Yeah. This is also the first time that I heard from the network. So I will definitely make sure that we join that.

>> TATJANA TITAREVA: Thank you so much. And now we would like to move on to our intervention by Dr. Jason Tucker, an agent officer as well as officer for research studies. Jason, the floor is yours.

>> JASON TUCKER: I wear two hats. I work in public policy and this is why I'm taking interest in these questions. I work in AI and global health. We hear repeatedly how AI will revolutionize healthcare. Over the last two sessions alone, I've had 6 or 7 accounts and massive potential for AI. But to meet complex challenges. And it's true. There are areas where AI is make advances in healthcare. Critically, some areas and some people and this isn't necessarily always positive. We shouldn't take for granted these advancements will continue all the benefits of this will be equally distributed globally. I think one of the facts that's limiting the potential and global health is the lack of international governance on AI.

I won't talk so much about the reasons for this. Virginia touched upon these shifting global economy and healthcare result as AI and the concentrations of power and non‑additional health actors. And new healthcare providers. And the impact it is having on the norms and the roles of traditional healthcare actors like clinicians. And these relationships are quite opaque and there's also concerns about security and the environmental cost of relying on AI to fix healthcare systems as well as increasing evidence that this works with some situations and creates new health concerns and new problems and it can cause more harm than good. But one of the things that worries me the most is if we throw resources, we can fix the healthcare systems. It is underfunded into healthcare in the hope that a magic pill will be created to fix the healthcare system. If it works, that's fantastic, but this is risky. We have to be strategic in how we do this.

So this approach to AI governance is bad for health and business and bad for innovation and we need to coordinate the multi‑stakeholder framework of governance and for the use of responsible healthcare. My argument is based on historical presence. The innovations weave had in global health have been based on regulation to incorporate and incorporate new technologies.

Some examples. Modern medicine is developed on regulation. A group of international experts in Brussels signed a treaty which regulated the part in medicine. When you enter the pharmacy, you had no idea what you're getting. You can tell them anything and they would sell it. It doesn't matter where we buy it from. It will be safe and it is going to be efficient. The fact that also global health is relying on international corporation I think have tongue about the COVID pandemic and the benefits of this and if it wasn't corporation and the devastating consequences. Health doesn't exist within national borders.

Healthcare is useful because it is rooted in scientific method and evidence‑based research. When we go to the clinician and ask for a diagnosis, we expect this to be rooted in scientific research. We can cut through demands of greater clarity in the systems. Healthcare is also multi‑stakeholder. You can't do interventions without including complex diverse stakeholders. You see this again and again. So what are the next steps? We have great foundations from the world health organization which are ethical guidelines on Artificial Intelligence and health. We have ACD and the AIA act as a high risk area and extra concerns around that is fantastic. We're really looking at research and inform the policy. Some of this exists and it is siloed within academia and some of it doesn't exist and it is desperately needed. This is why I got involved with this roadmap and AI policy research to try and figure out this disconnect where we can better connect policy making and where the gaps we can address through scientific research.

>> TATJANA TITAREVA: Thank you so much. We are moving to the exciting discussion part and we are opening the floor for both in person and online participants. In order to ask questions, we would appreciate it if you can use the mics on the side as well as when you speak to introduce yourself. And for the discussion, we are going to address, start addressing the three questions on the screen and I believe Isadora would also like to take it over.

>> ISADORA HELLEGREN: Thank you so much, Tatjana. I want to invite everyone to be active in sharing the discussion. I shared discussion questions in the chat for you to refer to. We want to go over in the last remaining 25 minutes or so before we move into closing remarks the following three topics. How can AI policy research support global corporation in AI governance while respecting regional diversity? What mechanisms can best system the access to and effective immigration of policy research into AI governance processes? We heard some examples here from the previous speakers, but we would love to also hear your thoughts on these or other potential mechanisms as well that you might want to bring up. And what are the most pressing areas in sustainability and digital rights protection where AI policy should be targeted? Where are we not looking and redirect and provide more attention? These are our questions and we will open up the floor to all of you. speak spinning yes. One thing ‑‑

>> ALEX MOLTZAU: I'm currently part of writing the implementing act for the sandboxes which means kind of operationalizing the way that these are being rolled out across European region. And I was just prior to this in a discussion with sandboxes globally as well. For me, personally again, I think we don't have all the solutions obviously in Europe and we should look at ‑‑ all the different locations across the world. A lot of people forget that digital pavement didn't originate so much from Europe and Africa. I think what ways can we create an evidence BASIS. For the sandboxes as well, they're in the shape of exit reports. So the dissemination and communication of how do the SMEs are in health or in other areas work for citizens? Do they work well? Do they fulfill existing requirements? Are they interplayed with different regulations they have and how to solve that in the best way possible. We want innovation. It's like amazing ‑‑ a used car. I know it's a bit silly. I have a daughter and when I put in the child seat in the car, I don't think this is not so innovative. It is comedic to see people talk that there's this binary opposition between innovation and regulation. Regulation well applied creates better innovation for citizens and for communities and like you were mentioning the flop count. I think one of the speakers from CGI, the VP at show port and I think this is really good example, right? Do we think that speed limit is a bad idea? It is only a threshold. It only means that over a certain threshold, there are other requirements that may apply. We're not trying to stop innovation. If you train models of a certain size and I think you are still working out what is the best way to work at this. Maybe there are requirements to think about. We haven't figured out everything. That's why we're working with now 97 people. 97 people in, I office, we want to recruit 140 by the end of the year. These are just some points from my side.

>> ISADORA HELLEGREN: Thank you so much, Alex. I would like to jump in here and open up for two comments or questions from online participants. We have two hands raised.

>> Ana Brazon, would you like to go first?

>> JOANNA: Thanks so much. I wanted to come specifically to your question about global cooperation. So while I strongly agree, it is nice to make things easier for the ‑‑ for the, you know, people who produce the AI. I also think that it is a mistake to have a single governance structure that would probably get captured. A lot of the people pushing very hard for you to (?) are coming from the country where the concentrated power S. I think it is important that we recognize that all different countries have priorities, capacities, risks and it makes sense that we have at least some diverse and legislation. I think with the EU demonstrated that if you do harmonize your legislation, then you can have a bigger ask and people are still willing to come just to give access to your government. So ‑‑ not to your government. To your economy. Right? So basically there's the real Brussels effect is the proportionality of how hard is it to do business where you and are what is your GDP? What are you paying back for your effort to comply with regulations? Create modules that is similar in different places. But I think the things that happen the last few months really show us that we want resilience through diversity. So thank you.

>> ISADORA HELLEGREN: Thank you so much, Joanna. This resonates with what we heard earlier.

>> I am Anne Flannigan. I'm Irish but based in San Francisco. I also worked in one of the largest AI model providers. Coming from both of those perspectives, I want to click on that first question about how AI policy research can support AI governance while respecting diversity. We really need to Zoom in. We're never going to have a global regime and single governance structure. It is not realistic and not appropriate. When we look at AI, when we're looking at the harms and protecting people, what harms mean can look different in different regions and different cultural context. It can be different even within the same country from person to person. So you have people coming up from different circumstances. One of the biggest challenges for me when I was in the Irish government, I used to work on Telecoms, infrastructure, data policy and early AI policy. One of the biggest challenges is when you're looking at something like this where there's a step change and you're affecting multiple sectors, the evidence is thin as to what the impact is. By that, I mean that it is really, really difficult to legislate or regulate something that hasn't happened yet. We know as human beings we know from other policy areas there may be dangers that are eminent. It is very, very challenging when there's a black of evidence based ‑‑ lack of evidence based. Those are tricky spaces to be in. If you look at the OACD guideline, we really should have an evidence based. But where does that Lee us? The role of policy researchers is so incredibly crucial here. I will explain the sandbox initiatives. Having those environments where you can test, trial and find out what the harms are and how they play out particularly, for example, for systems is really, really something where the scientific and research community can help policymakers to make better decisions. The Private Sector has a role to play here as well and I think having encouraging the Private Sector to engage in partnerships with the research community is always a healthy thing. They would be less to come forward and help governments. It is encouraging to see the EU Celt up. They will come forward in that respect. But this really is the case that it requires a multi‑stakeholder effort to one surface harm to test and unearth that evidence and three, really, really bring forward diverse perspectives around potential harms around AI.

>> ISADORA HELLEGREN: Much appreciated and important comments there as there for reflection here as well. There are more comments in the chat, but I am happy to come back to them to see who might. To be speaking in the room. Over to you, Tatjana.

>> TATJANA TITAREVA: We can continue with ‑‑ I'm sorry. Jason?

>> JASON TUCKER: Thank you. I also think I agree with the comments made about a global governance approach not being appropriate in this case. The role of complexity and pushing forward the agendas. I think one of the other areas in healthcare, the public health authorities are huge plays and they can drive markets and demanned standards that force the Private Sector and public/private partnerships. We need to be creative in how we aligned values. I think this speaks to the conversations we had earlier and principles and then operationalization and how we sort of ensure so we can meet the oath and provide good public healthcare and regulate and maintain this.

>> TATJANA TITAREVA: Isadora, would you like to tick to the on ‑‑ take to the online?

>> ISADORA HELLEGREN: Yes. PETTER.

>> I would like to bring up the question of military and AI or was it the AI society workshop a couple of weeks back. (?) made a very good speech about the military and projectories of AI. It goes into how the immigration has been between AI research and the military industrial complex in the U.S. in particular, but also globally. I think that's maybe a place where we as researchers with academics and policymakers can lead on a global harmonized effort even if all areas of AI may not be appropriate to have as a global harmonized regulatory space. I think that is something we can take inspiration for the work of the '50s and '60s. And consider areas where it is not appropriate to apply technologies and gather a global perspective on that and make for a global push on limiting those potential very actual harms of AI technology.

>> ISADORA HELLEGREN: Thank you, Pettra. I think a question many have on their minds as well regarding military use of AI nowadays. Thank you for this, Pettra. Another question here from Canewt. Going back to the questions and see where we should be addressing more. Are you with us in voice? Otherwise I can read your question from the chat. Okay. I can read it outloud. There. Question, the Norwegian tax administration. How can scientific research help the public sector better hit the mark in Safeguarding privacy when using AI? Our experience today is that we swing from one extreme to the other either neglecting privacy or being overly cautious which hinders our ability to be innovative in the use of AI. Do we have any responders to this question?

>> TATJANA TITAREVA: Alex would like to address.

>> ALEX MOLTZAU: Because I'm 94 week an, I feel partly responsible here. (?) to a national expert. But one story from a friend like Norway as well who was out of a job. Like it was the pandemic. She was pregnant and then she was going to switch over from kind of like pregnancy pay and then back to unemployment pay. And like that didn't go automatically because of some slowness in the system at the time. I think there was a 3‑month wait to getting back on and you have a mortgage running and you have things happening. When I called them up because I was angry about this, what is happening here, just as a correspond person, their argument was that for privacy reasons they were not sharing data. I love privacy, but we need to be competent enough to understand when we are frying to protect ‑‑ trying to protect people and when an organization is appropriate. We have fed rated learning and privacy and enhancing technologies. But that still means we have to be careful. There are cases that can go horribly wrong when we try to model is the s as shown by the Dutch welfare Scandal and people were praised in prisons and lost their rights to their kids even. There are some serious potential consequences for citizens that are sometimes hard to predict. I would say raise the competence in the authority and work with research communities and there are many research centers that are well established and you can reach out to in nor way.

>> ISADORA HELLEGREN: Thank you, Alex. I would like to say that part of the roadmap is the need for capacity building initiatives as well raising the general awareness whether it be with public effector or the general public on how to navigate the systems and use them well indeed, as crucial priority as well. So thank you for highlighting this, Alex.

>> JASON TUCKER: I agree with Alex. But I also think we need to take a step back as well. An assumption that AI is a broad range to challenges and complexities and in some cases, it would be beneficial. We need to remember there's a cost financially in terms of energy and data and privacy and how we do this. We need to be very strategic and think is AI the best and was this a sort of problem we try to address and a solution we do a lot of work with the AI policy as well and try to discourage people where it is going to be wasteful and more sustainable.

>> TATJANA TITAREVA: Yes. We call it question zero in terms even if it should be in the first place and here we're talking about organizational levels when organizations are having a formal moment. It is an understanding of what kind of problems they're trying to solve. I don't see any questions in the audience. Do we have any online?

>> ISADORA HELLEGREN: We do from ELTJO.

>> Hi. This is a question about either of this, but it is relevant in a matter. We see some very strong pushback against AI regulation in the United States right now. It may affect policy research because in one way, it may be considered in the future to be active research and thus putting funding at risk. Do you see that risk and do you see any ways to protect this very important research from such a risk.

>> VIRGINIA DIGNUM: Maybe I can take this one. Yes. Thank you for the comment. Yes, it is a risk and it is an issue that is concerning many of us. The line between AI policy research and active AI is becoming very, very narrow. With all the pushbacks not only in the United States against regulation, but we see that in Europe as well and just recently, the Swedish prime minister has proposed the union and stop a moratorium and implementation of the AI act. We see that all over the place. And it shows, I think, if anything the importance of grounded evidence based scientific research around these topics is not just a win of politicians. But we really need to more than ever work on the fundamental grounds on which we can measure and determine, understand the impact of AI. Several of my colleagues have talked about the questions there which is one of the main issues. It's not about can we use AI. Using AI because we can, but also because we should, but this understanding why we should use AI is not a technical question. It is a fundamental societal and political question and depends on the way we ask the question. The answers that come. We really need to look at that also from again multidisciplinary perspective because it is not just letting the technologies or the politicians answer the question of whether should AI be used or not because what we do with it or what we lose with it are questions that really require deep participatory and fundamentally participatory way.

>> Thanks, Virginia.

>> ISADORA HELLEGREN: Yes, indeed. The call for building resilient institutions for academic integrity when the wind blows in different directions is important. Over to the room.

>> TATJANA TITAREVA: Alex has an intervention.

>> ALEX MOLTZAU: I wanted to speak to this question on the importance of evidence‑based policy making. We should not take it for granted. A look around the world and a look around the politics and a look around the statements that are being made. We have to work really hard to bridge that gap. That is why this initiative is so timely. This is why this initiative is so important because we need people to work on this actively AI policy roadmap is what has been created and it is also fairly clear actions to follow‑up on it. This is a global concern and it's something that we can contribute to. There are actions that we can take to improve this and people that are listening in this and people in the room, think about this. Do something about it when you get back to wherever you are working. You can be working in the government. You can be working in a private company. This is something that you can do something with. It's in your hands and it is something that you really can take responsibility for. I would just say encourage that and not take it for granted.

>> ISADORA HELLEGREN: We do have one last question online. This might be our last question before we move into the closing remarks.

>> Hello. I'm also a researcher in the AI policy. One comment on this relationship between innovation and data pushback against regulation. I just want to push for going really to the ground with highly contextualized descriptions with engaging problems from the generalized standpoint. If you go down and talk to the action problems in Private Sector in public sector which you are actually dealing, from the perspective of lacking regulation or lacking the safety net that regulation creates, as long as the problems concrete enough. But when they become sweeping and generalized, we lose this connection. So I really think get public support for the use of regulation we really need to bring up the concrete problem.

>> TATJANA TITAREVA: Thank you so much more bringing that to the ground. Isadora, I think we can move. Thank you so much for the online participation and Virginia, would you like to take over for the concluding remarks?

>> VIRGINIA DIGNUM: Sure. Thank you for that. Thank you all for your participation contributing, especially the ones who have been listening. Thank you so much, everybody. I'm sorry I can't be there in person to discuss further with you guys. I have been given the task to try to come up with some type of key take aways from what we have discussed and I think that it is clear that one of the issues in the mind of many of us is around the governance, is around this eternal discussion between policy and innovation, regulation and innovation and I think that there is exactly like I said just before where we need much more effort into of evidence‑based research to where we show the benefits of regulation. Comparing different ways of regulation because we tend to think of it as being set in stone. When we think about technology, we see a dynamic and adaptable way of willing it. We use the tools we have for tech in logical innovation to support on regulatory and organizational innovation and looking at innovations and looking at organizational and societal transformational from the perspective of understanding and comparing evaluating the capabilities and the weaknesses powers of different types of regulations. So that is where research is important. I think that we looked also at the issues of interoperability and the danger of governance capture. So it is not, I think, the aim at all for research to come up with the one and only governance model, but exactly to understand and to deal with and provide the means for interoperability between regulatory and governatory models. Again, it is an issue for governance and I think that we hold different types of directions to consider the importance of participation and of inclusivity. Inclusion of different groups, inclusion of different demographics and different communities, but also the inclusion of many different disciplines. If I look back at the words that Isadora asked to share,those are typically not the topics or the words that we associated with technology, but from societal issues like inequality, humanity, inclusion, environment and so on. I think that's where we need to ground going forward on AI policy research. Thank you.

>> TATJANA TITAREVA: Thank you so much. Isadora, would you like to finalize?

>> ISADORA HELLEGREN: I will extend our sincere thanks to all online participants for your active participation in this hybrid event and for all of our in‑person as well to our esteemed speakers, for your interventions contributing from diverse perspectives on these crucial questions at this pivotal point in time as we move forward and need actively to take action. It's been a very enriching conversation and we do hope you will continue following updates from AI policy research network. Please sign and endorse the roadmap. We do hope to see further adoption and adoption of these principles and networks as we move forward.

Also, of course, thank to IGF for allowing us to host this session. We do hope you engage through various channels as we move forward on this active pursuit per policy, practice and research. Thank you, everyone. And thank you, Tatjana.

>> TATJANA TITAREVA: Thanks. Have a great day and lunch.

[APPLAUSE]