We want to start by acknowledging that Harvard is located on the traditional and ancestral land of the Massachusetts, the original inhabitants of what is now known as Boston and Cambridge. We pay respect to the people of the Massachusetts tribe, past and present, and honor the land itself, which remains sacred to the Massachusetts people. Our guests today are two friends and colleagues in bars, Dr. Dora and Dr. Funke. Doc is a law professor and the director of the Digital Law Center at the University of Geneva, and Leah is the assistant dean for learning, experience, and technology, and the Meyer Research Lecturer of Law at Harvard Law School. They're here to illuminate big picture challenges around data governance and altruistic data sharing models. The EU Data Governance Act describes, quote, data altruism, unquote, as when individuals and companies give their consent or permission to make available data that they generate voluntarily and without reward to be used in the public interest. So we will dive deep into the specific realities of this model for children and teenagers, both in everyday and in crisis contexts. Please note that this event is being recorded. There are some cameras in the ceilings. However, audience members will not be shown. For the virtual audience, if you wish to ask any questions or leave any comments, please use the question and answer function on Zoom. And this is also a friendly reminder to ensure that your mics are muted during the event and for folks in the room to quiet your phones please. We strive to create inclusive, accessible events. For any questions, comments, concerns, or ideas for future gatherings, please contact us at events at cyber.harvard.edu. Your feedback is critical to our success. And to stay in the loop about future events at the Berkman Klein Center, head to cyber.harvard.edu slash get involved. And for students, I know there are a lot of students in the room. We have a student Harvard student specific listserv as well that you can sign up for to learn about those kinds of opportunities here at Center as well. Thank you for joining us today. Jacques and Leah, take it away. Thank you so much, Becca. It is wonderful to be here and wonderful to welcome you back to Cambridge. We miss having you here in person last January when you were teaching online to the visiting professor and the lighted you are here now. So warm welcome. And with that, I may actually put you on the hot seat and ask you a question about life across the pond. And I question this, data altruism. Could you tell us a little bit more about how it is defined as a legal term of art over in Europe as well as from a policy perspective? What does it mean as a US based lawyer and legal scholar and US based that subject data altruism sounds delightful? But what does it mean? So first of all, thank you very much. It's an immense pleasure for me to be back in person also with a wonderful weather and to discover the new Brooklyn Klein Center office space. A tech related discussion without a tech related moment. All of it is on. That should work better this time. So as mentioned by Becca introduction, so we are basically exploring and having to discover what this concept of data altruism is supposed to mean. It has been now adopted in the data governance act that will come to force in less than a year from now in September 2023. The regulation has been adopted back in May. So we'll have to see what that means. And what I perhaps want to do from the outset is just describe a bit what that means without going too much into the details, to keep that for the second set of the discussion and also to try to keep it at a high level. So as mentioned, one element is that data altruism relies on the voluntary process. So it's based on consents. And also one important element is to keep in mind that data agreements does not focus only on personal data under the famous GDPR, which I'm sure you're familiar with, but also covers non personal data. I mean, industrial data, that means also you can have potentially companies or individuals sharing their non personal data and that can be part of data altruism. One thing which is important and Becca, you already alluded to that is that if you want to speak about data altruism, there should be no remuneration. So the person and the company is sharing the data in the context of data altruism activities shall not be remunerated for that, except for covering the cost. The question is what does that mean to cover the cost? But anyway, there shall be no basically a financial interest in incentives for sharing. But more importantly, and perhaps most importantly, in order to speak about data altruism, the sharing must be done for a purpose of a general interest. And there are a few examples which are given in the Data Governance Act that can be promotion of mobility, promotion of environmental purpose, also research purposes. There are a few examples which are given, which mean basically that there shall be, of course, not a commercial setting, not a commercial purpose for that data sharing. And one key element that I insist on that is that it is really based on consent, meaning that and perhaps also weakness of the entire system is that the consent can be withdrawn. So there is a dependency on concept on the consent, which is at the core of the data altruism mechanism. And one thing which is, of course, important to keep in mind, too, is that in Europe, but also in other places, no one basically waited for the Data Governance Act to do some data altruism activities. And there are plenty of examples of such activities. For instance, the city of Barcelona where basically they had launched a project called Decode in which people were invited to set sensors for the purpose of collecting data for air pollution measures on this type of activity. So that means we will have a new framework which basically should facilitate and that's going to be part of the discussion, but which certainly doesn't create data altruism as such from scratch. It has been in the air for some time. But that's basically what is data tourism. If I may just briefly turn to the who. So who shall be doing that? So there are, as you could expect, so-called data altruism organization that are to be governed by this Data Governance Act. They should be and they shall be. This is a mandatory application, non-profit organization. They shall also do business in a manner which is totally separated from all business entities. So if there is a business entity which may want to engage in data altruism activity, they should create some kind of independent foundation or another non-profit institution in order to run that. And the EU framework somehow is based on some kind of incentive system. And we get to see whether that will work and whether the incentives are quite efficient or not in the sense that there is a system by which these data altruism organization may get registered at the national level in registry for data altruism organization. And there is no obligation to do so. But if they do so, there is an incentive, again, with the question whether that would be effective or not, that they will be in a position to use a label and that label will be a data organization which are recognized in the Union, so the kind of certification, meaning that they are trustworthy partners.  How do we create trust? How will we make sure that the system works in the sense that people and company will do an engage in data altruism activity? So there's labeling system and as you can expect, there is also some kind of a supervision system by state authorities in order to make sure that those companies who wants to deserve that label do comply with the relevant obligation. So that would be the pools or would be data altruism organization and some kind of a supervisory system at the national level. And now as to the how also very briefly. So one thing that somehow transpires, so to say, from the regulation. And again, I couldn't mention that from the outset, of course, observing from the outside of the EU being based in Switzerland. But one thing that transpires from the regulation is that there is a strong intent of the regulator to make sure that the data shall not be misused. So there are a lot of publications which are imposed on these data altruism organization to make sure that they do protect also in terms of cybersecurity and connected to the events today. Make sure that there shall be no leakage and that there is a strong obligation to protect the data that they get access to that are granted to them by these data owners. Now, one important piece also of the regulation is a so-called rulebook. So there shall be a rulebook that shall be established by the EU Commission in the coming times that shall somehow define what are supposed to be the obligation of the data altruism organization, which will be a very important way to implement that in practice. And that rulebook, interestingly, will also have to raise awareness about data altruism. So that's also a question of as we discussed in preparation of the session, how do we incentivize the people basically to share the data? So the rulebook will be an important element of implementation as to how that shall work. Hopefully another very important document will be the so-called European data altruism consent form. As mentioned earlier, the entire system is based on consent. And the idea is to create trust so that people who will be will have the required level of trust in order to untrust the data to third party on an altruistic basis. So there shall be some kind of a uniform format to do so in the way of performing that shall be developed at subsequent stage. Now, in terms of policy action, that's going to be my final report on that. If you look at the regulation, you'll see that there is a mention of the potential development in supplementary, but a potential development of so-called national data policies. So it would be interesting also to see what will come on that front and perhaps in terms of future academic policy work, whether there might be supports to be offered on that basis, perhaps from a transactional perspective to see how these national data altruist policies shall be implemented in practice. Another question, of course, as we've discussed, is to see how this type of evolution, how this type of regulatory approach might have a success even beyond the European Union. As far as Switzerland is concerned, even though I was too early somehow to predict whether that will be implemented in Switzerland, but I'm pretty sure that we are, of course, observing that very closely because as a matter of principle, we're trying to be EU compliance as a matter of principle, not only for data policies, but more generally. So I'm sure that this will have an impact. But the question is perhaps turning to you is to think about how that type of regulatory model might be perceived as a potential model in the same way as the GDPR of course, different but slightly connected, of course, matter. The GDPR has been set to kind of become some kind of a global standard with respect to data protection. So what do you think of the Data Governance Act as to the data altruism function? Thank you so much. Is this working OK? OK, quick touch up before I talk about tech. So here in the United States, since we don't have the same type of national or in the case of GDPR, supranational data privacy protections and data governance framework, we have a long way to go. First, I think to serve as a model in the United States, we would need to get ourselves a comprehensive federal privacy law where regardless of whether you are living in California, which has really our strongest state level of privacy protections in the country at this point, or whether you are living in a state that doesn't have that, you, no matter what your age is, can rest assured that there are certain rights that you have in your data that are not going to be violated regardless of where you're living. So I think it's it's hard to get to a concept of data altruism yet in the United States because altruism implies that it is yours to share. And while I am very much committed to the principle that all of us as individuals do have more rights in our data than our country's law currently recognizes, the fact remains that on a federal level, actually, those rights are not protected. So it's hard to think about how you find your way to being altruistic, to sharing, to giving something that the law does not yet fully recognize as yours. Second, I think that there is a trust issue in the United States with the idea that baby data can do big good. Certainly, we have many cases in Jackie, when a number of colleagues wrote a wonderful recent paper that was circulated in connection with this talk about all the ways, especially in crisis mode, that data sharing can facilitate absolutely life-saving forms of good. But unfortunately, as a research recent Pew Research study here in the United States found it was almost roughly 80 percent of adults surveyed have some level of concern about their data privacy in this country. And so before we can get comfortable thinking that we could share voluntarily and transparently for greater good, we have to not only have legal confidence that the data is somehow ours or somehow protected, but also we need demonstrated use cases where it is being used in our own good. And last but certainly not least, I think those two issues are particularly important when it comes to youth. And I am defining youth here broadly to include folks in that sort of 21 and under range. There are other ways that one could draw those lines, but I am going a little bit younger with that group to say that youth are born digital as our colleagues or as our John Colfery have been saying for quite a while. And so when you are talking about now generations, not just a generation, but generations that even before they are born in some instances and for some of them, you could argue even before conception, if data from a fertility tracking app or wearable device could be linked back to them, their full coming into existence actually has a digital data trail. And I think that for generations that are born and conceived, maybe even thought of, I don't know, folks who are technologists can jump in and tell me we have the technology that can read our minds that I don't know about yet. And by the way, if you have questions in the audience or out there virtually to jump in at any point, but I think that there needs to be an even greater emphasis plates on that trust building and proof of concept for data altruism to take hold in the United States, especially among you. And I'd be curious to hear a little bit more of your reflection, reflection job on when we look over and think about how the kind of trust may be built up even better right now in the EU and other European, but not EU countries. How do you think about that relationship between privacy protections and trust building such that countries can find their way to data altruism? I think it was a key element that we just discussed is trying to see how we can ingrain somehow trust in the entire ecosystem of data altruism. And one thing that we can note when we look at at this data governance act with respect to the specific chapter on data tourism and have some kind of helicopter view is that there is still basically a system which is anchored on consents as the key element which is supposed to justify the entire system. And you kindly alluded to that recent position paper that we have drafted in an interdisciplinary way on using or improving and maximizing the use of data in crisis situations. In that concept paper, we have somehow move away from consents in order to find other grounds which would make it possible to use data. So to me, the question is, do we want to keep that regulatory model which is based on consents, which is now what is at the core of the Digital Governance Act, which is, of course, the kind of the logical consequence of considering data rights as individual rights, at least with respect to personal data protection, or do we want to move away from that paradigm, also knowing that if we, and that's a general reflection which is also actually mentioned in the Data Governance Act, individual data that we collect from yourself or myself will of course have no impact that might come and might help if we collect all our data in this room and of course way beyond this room, perhaps also with our online colleagues that can have an impact. So my point is that we have to think perhaps in terms of paradigm shift from individual exercise to collective exercise. And that's, and if we want to do that transition somehow, of course, if we consider this is legitimate, we may also have to think about moving from individual consents to perhaps collective consents or no consents but other legitimate basis for processing data. And there are critical voices which have been criticising even quite heavily this Data Governance Act on the ground that precisely it's based on consent and I'll terminate on that. Also, because if you go through that document, you will see that there is pretty strong insistence not only on consents as the justification for processing and for data addressing, but also on that's a logical consequence for withdrawal of consents. Another question is what do you do if people withdraw the consents? That would mean potentially down the road that the use for the public interest might be threatened in the future if people do withdraw the consents. So we have a problem which is a general problem under data protection law but also personal data protection law but also for non-personal data. If it's from an IP perspective, I may just make a very short side note on that. It's as if you have a license agreement and then you terminate the license agreement. Of course, the licensee after the termination doesn't have anyone to use anymore and that would be the consequence here. Is it really what we want in terms of general interest, general public interest that everyone can basically individually withdraw the consents and also in very practical terms, I don't think that would be easy to implement because if we assume, and I'll terminate on that, if we assume that the value of data is basically the collectivization of data, the aggregation of data, what, how do you in practical terms, implement withdrawal knowing that the data will be basically commingled and mixed with all data? That means in practical terms, my perception that in any manner extremely difficult to enforce this withdrawal of consents. So question to me is, is it still the right model? And I've been, of course, very happy to hear you, but perhaps also the audience as to what might be the appropriate model to move away from this consent-based individually exercised rights to a collective exercise of rights which perhaps might be based on collective interest and say, this is legitimate for society. We need more data address. So we need some house to kind of find a legal basis to justify this data processing. I see a hand back there. Go ahead. I have a question. So this is very interesting. So I may need to pass my question first thing is, so our person has a very positive connotation to it, right? And sometimes individual interests and conflict with collective interests. Thank you. So there may be conflict between individual and collective interests and maybe it's not the best for the individual to sign up for that, right? So what are the downsides? And in the second aspect, I wanna talk about, but likely to ask about nudging or like consent. You talk about consent. There's a lot of research in my field which is information systems management about algorithmic nudging which refers to the use of psychological cues to make people behave in a way that may not always be good for them, right? So yes, consent is fine, but maybe they are nudged into something that is not good for them. And is that still then something that is altruistic or good? Thank you. Thanks very much for these points which are of course a key relevance. My perception is that even if we were to move away from individual consent and have some kind of another justification, what should remain is full information. And you see that also transpire in the regulation which is kind of a standards approach which certainly makes sense to make sure that to your point, there's really a full information as to what shall happen with the relevant data also to prevent that the type of general public activities that were supposed to be covered and that were supposed to be undertaken on the basis of the consent that was initially given that these general public interest activities shall not be converted subsequently into perhaps commercial activities. So there's really a need to make sure that you have a fully informed consent or even if we were to move away from individual consent, we would still have some kind of a full disclosure for full disclosure as to what happens. Now in terms of the nudging and not familiar necessarily with what you're doing in terms of research that we're interested by the way to also to learn more, it's certainly something that would also need to be taken to account in the sense that we should again, I guess that could be connected also to this full disclosure full information to be given to make sure there is no kind of misleading. And you see that also, and I'll terminate on that, you see that in the regulation, there are also some kind of protection mechanism in order to avoid that allegedly altruistic activities and organization shall kind of make like misleading statements in order to attract data and to collect them for commercial purposes. So there's this tension somehow to make sure that if it is truly altruistic, the entities which are engaged in this type of activities should truly make sure that there's no misleading of these data providers in any manner. Consent is a really tricky concept. And I see a hand so I'll keep this very brief. I just wanted to surface and I would love questions, reflections, pushback on what I'm about to say. I think consent is so tricky right now, especially in the United States. And here I'm talking specifically about consent when it comes to digital data that we should, and I use should from both an ethical and also legal basis, we should get consent from a data subject, from a person before using their data. Practically speaking in the United States, the vast, vast, vast majority of the time that we are theoretically giving our consent, it's really not consent in a practical sense. So think about all the times you are clicking, I agree, or swiping next or hitting submit. You probably aren't reading everything that you are theoretically consenting to. And I'm not faulting you for that. I will fully confess, if it is in my professional capacity or it involves my kids, I am reading it. If it is just in my personal capacity and just about me, and I'm trying to get my Uber app updated, I am definitely not reading it because I've got a million other things to do. And so the whole framework we have going on right now around consent in the United States, there's a lot of legal fiction to it in a way that I really struggle with in terms of thinking about how to build a fair and inclusive and just common sense, honest digital data governance system in this country when we keep talking about consent and it's ultimately pretty empty in terms of building upon it. There's a great recent law review article by a law professor named Mary Fan that talks about, and I believe it's the first of its kind in the United States to affirmatively talk about building a public or collective right to pool data in a data altruistic way in order to have shared public benefits. So thinking a little bit about some of the rights that might exist in terms of public access maybe a way here in the United States as well as across the pond to make our way through a little bit of I would say legal fiction in our country at least that it's possible to have actual consent. And I saw your hand. I don't want to cut it. Did I see it correctly? Please. Mine is a very basic question. In Wikipedia, we find biographical information of many scientists and artists. So I'm a plant biologist and I do provide data both of living and even the past. About three or four years ago, I heard that what we provide may not agree maybe in violation of the privacy of European Union. And so can you elaborate anything about it? I'm not sure I got the question right. Perhaps could you reference it? I think the question and please jump in if I misheard. I think the question has to do with, I think the question has to do with the ways in which GDPR over in the EU may be impacting the ability for a scientist to share information on Wikipedia or other platforms. Is that correct? Yeah, I mean, I would not be in a position to speak specifically with respect to Wikipedia, but as a matter of principle, I think, and I just had a doctor's written for PhD thesis on that topic and if I put it in general terms, the challenge is to make sure that GDPR doesn't prevent academic research and research activity in general. And you see that also coming back to the topic of today's session. You see that emerging to with respect to data address because research and scientific activities are one of the activities which are supposed to be covered by this general public interest that could justify data address activities. So there's a tension which still exists and on which I think we still should work collectively in order to make sure that GDPR doesn't act somehow as a barrier and as a hurdle for scientific activities in the broader sense. So that's certainly a very relevant concern on a very important issue. Yeah, thank you for your talk. And I agree with this idea that we need more collective measures for consent to data processing. And I'm interested in if you thought about concrete measures how to effectively democratize data processing. And if you also thought about specific use cases or something that you could share with us. Yeah, thanks very much. It's a very important point. And I think I was thinking also in terms of incentives in order to try to promote data algorithm. I think what we need is precisely case studies and I'm thinking perhaps also a comment or question to you Lea as to what should the next gens do? How can incentivize them to share the data? This not necessarily with this type of relatively, let's say regulated approach where we define what data alteration mobilization are supposed to do or what not supposed to do. We have to find ways to incentivize the sources of the data. And as mentioned, there are a few examples of the paper that we've discussed in preparation of the session. There was for instance this decode project which was conducted as I was mentioning earlier by the city of Barcelona. And I guess you can look it up online. That's an interesting example. And you'll see also that's one of the tricky question at your level is how do you distinguish between personal data on the one hand and non-personal data which is frequently quite tricky which is why I think it's good that this new data governance act in terms of data alteration adopts a transversal approach saying that this data alteration rules are supposed and can apply to any kind of data basically. I think now as to the first part of your point in terms of collectivization, if you look here again at another chapter of the data governance acts, what they are contemplating and what will have to be implemented in the near future is the creation of so-called data intermediaries. So data intermediaries as the name indicates are supposed to act as entities between data holders on the one hand and data user on the other hand. So it's one step in the direction of trying to move away from the individual exercise of data rights to a more collective exercise but here again and that's a different chapter of a different discussion. We have to see how that can be implemented. I think one point that will have to be taken into account in this discussion of fostering this data intermediation services is to make sure that we find the right balance in terms of risk of liability of these data intermediaries. So they will be basically getting a lot of data and then they will kind of sub-license them out using again an IP licensing terminology to third parties. We just have to make sure that they don't risk too much because they will not be at the source of the data. So they will help in the process but to me it might be kind of unfair to have a strict standard liability because otherwise ultimately you have no one providing data intermediary services. And the reason why I'm mentioning is that is that you may have the same type of concern or policy risk if you think about data alteration mobilization which may also in certain circumstances act as data intermediaries by which they would collect these data for interesting purposes and then sub-license them out. Here again we just have to make sure that the system finds an appropriate balance of rights and interests to make sure that the system works to move away from individual exercise to collective exercise if that answers your question. Do we have any questions? I'd like to see one in the back and then also if we have folks joining us from home we'd love to hear from you as well. Thanks so much. I wonder if you might just spend a brief moment expanding on the intermediary concept you just introduced specifically with respect to some of the work done by people here among others regarding data fiduciaries and borrowing specifically the obligations both legal and normative surrounding fiduciaries for example financial data, medical data, et cetera. It sounds like you're proposing something a little broader but I wonder if you might go into a little more detail please. I may return the question. I'd be interested to hear your thoughts on perhaps your definition of data fiduciaries with which I'm not as familiar. To be frank I'm looking at, I have taken a copy of the Data Governance Act because there's a full chapter on data intermediaries and which defines all the rights and obligation of these platforms. To me, if I may give also perhaps knowing or thinking that I know I'm not sure this is the case balance activities here at the Berkman-Klein Center in terms of intermediary liability for in the digital environment. My perception is that as I was perhaps mentioning earlier is that this new emerging concept of data intermediaries doesn't necessarily fit in what we've discussed so far in terms of risk of liability. It's not something which is somehow associated for instance with the intermediary liability with respect to copyright infringement for which as you know there's been a lot going on in all parts of the world including in Europe. Here we have to deal with something which is new which is somehow, and I'm well aware that I don't fully answer your question but I think this somehow one evidence among others of perhaps the fragmented approach that you can find in various, let's say activities of regulators and specifically also at the EU level. I don't think these are the same people basically who have drafted this data governance act by comparison to other well identified and well established issues that we may face with respect for instance to copyright infringement in the digital environment. But again, I'd be also very interested now later to have a sense of this data fiduciary intermediary that you mentioned earlier. Yeah, I've got this one and then I know that there's at least one in the Zoom. Hi, thanks a lot for this. I'm wondering whether you think that the digital governance acts chapter on data intermediaries might facilitate the emergence of data markets. And if so, what effect do you think that would have on data governance in general? Again, I'm not reading the crystal ball but this is the ambition I guess, precisely to foster on that as one of the many facets of European data strategy. But the question is to see also how we would reduce transaction costs to some extent to make sure that there are sufficient incentives from the source of the data to make this data available to the data intermediaries and in terms from the data intermediaries so that they can make the data available to interested end users. And to me, that also requires like for any market that the transaction costs can be reduced. So that means also in practical terms that there should be some kind of template agreement on the basis of which you can rely on the system which works and it is easy to implement to make sure you can get access to data or respectively you can grant access to your own data. And one final element in that respect on which I think it's important also to focus in this general framework of creating, as you say and this is a key element, creating data markets is to make sure that if there is a dispute these disputes are solved efficiently and to make sure that you have a system by which and I'm not to show in that respect that a strong state in for intervention is necessarily relevant. Perhaps we should think about alternative dispute position mechanism to make sure that there is like a speedy accessible also in terms of cost process by which you would manage potential disputes that may arise between the stakeholders. I may offer two very quick observations. I think I'm good for a moment and then I'd love to hear from the question coming in at home. These are maybe tied at last to wonderful reflections and questions together. The first is in my understanding and I will of course defer to our colleague across the pond if I'm missing something but I would read the data intermediary chapter in the new data governance act as well as the recitals ahead of it that support it as being less directed and requiring less of a duty toward a data subject than the data fiduciary model would. I would broadly understand data intermediaries as being required to engage in a somewhat enhanced level of responsibility with much more oversight than we currently have for the data broker model in the United States which is just completely the wildest but not nearly as much of the truth fiduciary would somebody who really has a directive to act in your best interest to be loyal to you and to not interject their own interests into their decision making. Last quick reflection on the effectively the data market model we have in the United States which has a number of different stakeholders but is powered disproportionately by either data brokers that are quite in some way they shadow market even though they are big business and I'd say they are a shadow market because they are very loosely regulated at best more probably it would be fair to say they're largely unregulated there has been some movement at state and federal levels to rein that in that data brokers in the United States engage in a robust data market business where they are aggregating, analyzing, resharing and repurposing data that very often all of us don't even realize they have in ways and with organizations that we don't know they are contracting with and unfortunately we do see that organizations in the United States that we would consider altruistic and are altruistic also do engage with data brokers one quick example that I will welcome our original participant there was a great study by the Center for Law and Information Policy clip at Fordham Law School back in I think roughly 2018 that looked specifically at how data brokers were getting information about youth they thought largely through school settings and they featured a piece of data in that report that the American Red Cross had gotten information about potential blood donors using data broker available lists or selects which are subsets of lists I am not critiquing the American Red Cross or their desire to find blood donors I am just flagging that already in the United States we do have these very unregulated very shadowy data markets that exist with data brokers so with that our virtual front great thank you so good transition to there's a couple questions in here but I'll start with this will youth have the legal agency to assert authority over their data management might that sovereignty extend to even govern their parents ability to control it? I love that question and whoever asked it I promise it wasn't a plant so we are in a unless it was I can't see who it was but I didn't consciously plant it so we are in a very exciting and also very open time with this new governance act in the EU because it has been passed it is not yet effective and there is much to be seen and much to be created about filling in the details and implementation I think that it is an open question just what relationship youth will have if they have not attained the relevant late age of legal majority for that purpose and especially if there were to be a some sort of conflict to use the term that was used by the question who would be the sovereign? Who would truck? Would it be the youth or would it be their parent or guardian? We are in an open space around that my guess unfortunately is that it's likely to default to parent or guardian laws typically do when it comes to youth and data and I really do question whether that is only as desirable but I would be curious to hear your take on this particular set of questions as we move from the passage to the effective date in 2023 of the Data Governance Act how do you see this and maybe some of the related questions unfolding and then I know we have folks waiting in our virtual queue? I think that's also quite intriguing area of policy attention and that would depend if I try to make a reasoning at least based on Swiss law and subject to more detail and indefinitely than the EU law my perception is that that may depend on local laws because it's basically an issue of civil law to what extent minors can consent or not if yes to what extent to something which is relevant to their own let's say intangible assets or which is where other court now was focusing on personal data so I don't think there is necessarily a EU wide solution at least there is as far as I can tell no international solution which is like unanimous but if we were to think about a way to kind of promote data altruism also for the future and also to empower minors to engage in these type of activities we may think about alleviating perhaps the conditions and to give them more power perhaps by standard by comparison to other classical issues for which as we're saying parents may have more power to decide but to me that would also of course imply that there is an even more detailed perhaps information if not consent to make sure that they get a very clear picture perhaps in simplified manner but of course truthful and adequate manner as to what shall happen but also perhaps more importantly to motivate them to engage in these type of activities and coming back to the question earlier as to the case studies to show impact to say look speaking to teenagers you may have the possibility to share data for mobility for in terms of energetic consumption but that should be based and perhaps there are technological solutions to that perhaps connected to an app which could show look by what you shared today you have contributed perhaps even the very minor points even not a percentage but still this is what's this is the output of what you're doing of your contributing to do so if we were in a position of ensuring that there is transparency sufficient level of information I would personally kind of speak for regulators I would personally be quite interested to see is there's a bit more freedom to make it possible for miners to engage in a non-commercial and to promote a non-commercial use of their data and specifically to engage into data altruism activities and I think would be interesting to see in this interesting period that we have my perhaps concern is that it may depend again on like quite fundamental principles of private law civil law family law potentially to see to what extent miners do have that level of freedom or not and that's of course open for discussions Well that leads into these are nice little ping-pong back and forth a question here where it notes that altruism feels more individual instead of doing something for the social good or in the name of the public interest getting to the incentives what are the limitations to the concept of altruism and is it possible to think about data sharing as part of a duty so that bigger problems can be solved of course the duty to share would not be without safeguards for example privacy Good things Thank you So there is this concept in the Data Governance Act about how altruistic purposes need to serve the objective or an objective of general interest and so there is a way in which already at least on its face the Data Governance Act is saying that altruism ultimately has to move toward this objective or an objective of general interest I think that does open up ways to think about and perhaps to design implementing regulations and structures that do point more in the direction of having some sort of obligation or civic duty or collective action that would move us toward this being less of an individual decision which then would add up as you were pointing out earlier Jacques to some real problems of individual consent more withdrawn to some sort of obligation I will say that I think in the United States at least that would require a lot of trust building and proof of concept because not always but there's a lot of sentiment I think justified that all of us in the United States have become at times and often unwittingly participants in a system where we are giving up large amounts of personal data all the time in exchange for free or lower cost digital services or devices so we sort of feel like we've already been entered into that model in this country and that we don't necessarily have a lot to show for it and again I do understand that the vision across the pond is specifically to make sure that if data is being given in this altruistic way that it is going to these non-commercial purposes of general interest but just flagging in the United States that figuring out how to design some sort of duty or obligation or perhaps you could also look at it as Professor Phan does which is a public right to have access to this information once it's generated we have a little bit of a ways to go So I think quick reaction on that I think as we discussed altruism may be perceived so far as kind of an individual behavior and decision but data altruism must be collective for the reason that we've discussed if just only single person doing that there's no way that will lead to any impactful outcome now if we just perhaps take a step back an even bigger picture this is not very far as far as I can see from other let's say sociable engagements of citizens in promoting, let's say scientific interests so like citizen science and these type of movements or perhaps crowdfunding or crowd contribution to the advancements of science to me this is also connected to what we're discussing here most specifically in terms of data altruism and I think there's obviously as we discussed a very important need to try to identify what shall be and the ways how you can leverage that and can promote that including more thinking when I was listening to what you just said also in term of education what if schools were to promote actively this type of data altruism activities of course with the limits that we've defined or what if other institution would try to do that again with the system which would preserve and protect transparency make sure there's full disclosure as to what is done but also which shows the outcome of the potential impact of the potential data training activities OK, we've got one more question so what about people whose data is linked with other folks data how do we deal with sort of the ways in which it becomes heterogeneous and how does somebody altruistically give rights to that or not I think, yeah to me one aspect of this tricky question relates to as I was mentioning earlier to the consequences of a withdrawal so given that the fate of the data that is supposed to be shared based on altruist data sharing is that it shall be combined with other data I think it will be very difficult to come back so ultimately even if theoretically there might be a right of withdrawal it's quite uncertain how that would work which is why again if we look at what has been contemplated and what will have to be implemented at your level it will be interesting to see how this will be drafted including in the standard constant form that is supposed to be drafted in the future I suspect there will be a very big gap as there often is between what is on paper and what is done in practice on paper under the Data Governance Act data is any digital representation of acts facts or information including a compilation of those types of facts and information so on paper sure I can revoke my consent but if the data that I have supplied in that digital representation includes acts and facts and information that also has data about folks who actually not only did not withdraw their consent but affirmatively wish to be part of this data altruism experience that is going to have to get sorted out in this year long space and beyond and my guess is in practice no matter what the on paper resolution is we are going to have a situation where either too much data is being withdrawn because it is intermingled and someone revokes consent and you sort of cut with a shovel rather than a scalpel or we are going to wind up under removing and the person whose data is requested to be withdrawn it won't get withdrawn because it will say well actually it's part of somebody else's compilation of digital information and for folks who want further reading Alicia Solo-Niederman who used to be at HLS and is now at Iowa is doing wonderful legal scholarship here in the United States about exactly these types of group life realities and the ways in which our current approach to data governance and privacy law does not fit the realities of group data sets and how all of our lives at this point are very much networked and that the ability to give a revoked consent individually does have implications for the folks around you and with that are we at time or okay thank you all so so much this has been delightful shock always a pleasure we hope you will come back and see us again soon and for everybody here at BKC to our wonderful events team to our wonderful attendees and our wonderful folks on the other side of the screen it is always a delight to engage on these tricky and exciting topics with you thank you so much Merci Merci beaucoup


So before you can send your questions and we start the conversation, we have a question for you, the audience, which you can answer on menti.com, typing the code that will appear on the screen. So the question is, which area do you think artificial intelligence will transform the most? Employment, health, mobility, new areas, public policy, or other areas. Yes, directly on the box. Oh, okay. And so while we went, we wait for the result, which would be your answer to this question. So it looks like health is starting to get an early lead. I guess the people can see this on the screen at home, no? Yes. So, up to nine now. What, what do you think? Sorry, I was thinking. Yeah, no, I was just wondering about that question because imagine that when electricity became available for many without all the people at that time, you know, what do you think electricity will transform the most would have been very hard to predict In fact, even in hindsight right now, if you ask me what electricity transform the most, I'm not even able to answer that. And I think AI is comparable to electricity in the kind of transformation it's going to, it's going to allow. So yeah, I'm not going to make any back here. I have a question by by five and Montego, he says, over the past few years we have been observing a lot of changes in the global scene disrupting supply chains. How can I contribute to mitigate these issues and help us optimize supply chain management so that's true I remember like a few years ago, and I did a project for steel case. For those of you who might not know steel case are the number one manufacturer of furniture office furniture in the United States. Okay. So they're based in Grand Rapids, Michigan, and they manufacture like the office furniture they will see all over the US actually most of it come from them. And they have just in time manufacturing. No, so if you guys are not familiar with the idea of just in time manufacturers that a steel case doesn't start manufacturing anything until after they sell something. Okay, so you order a chair and after you order a chair they start manufacturing the chair. So, this is people that, of course, have been in the area of Michigan for a long time, their life has been supplied chains you know Michigan has been a supply chain intensive economy for a long time is the is the car economy and so forth.  If the order doesn't leave all in the same shipment, they lose money. If the order is able to leave all in the same shipment. That's how they make a profit. No, so it's all about that logistics you know it's not about the materials is not about any of those things.  Now, even in their case that they are a very advanced registered company. At that moment they have big information problems about being able to foresee, you know, constraints in their supply chain that would emerge when they would have unusually large orders of an item that was not very commonly no order because the problem is that they're able to know their supply chain, but they would not know a like constraints that might happen to or three links, you know, upstream from them. Okay, so they might order certain pieces from China they usually arrive in time, but that's because they're all in 1020 at a time, if they put an order of 500, all of a sudden, they're not going to be able to send them, not because they cannot but because that person also has other barriers that then become constrained to provide some of the so in that word you could think you know that given enough data on the history of the supply chains you might start training those models that be able to provide alerts when that purchase is going to happen that triggers you know that there's a purchase that maybe we're not going to be able to fulfill that's going to cause a delay of the factory, you know, and that information can also be then sent to the people that eventually you know their lives are going to become hell. Two or three weeks down the line when they have to deliver the order and they cannot do it. So I think that's one place where I think can support like supply chain business is that it's a big intensive business. You know SAP, the video software company in Germany, I think maybe in Europe, you know, is, you know, on the supply chain business, but I think that other places and just to give you like a brief idea like we are working in my group with them. Next in the United States the National Institute of Standards and Technology, in part because despite the fact that supply chains are so important we have very limited data about them at a global scale everybody knows the wrong thing. We're using machine learning, you know, on international trade data to try to infer supply chains so that we can provide these maps that we can use to strategize and be resilient you know we can strategize when a natural disaster happens when a shipping route gets interrupted. So what are the other places that can provide what we need and that we could pivot towards, well, you know, to make those strategic decisions you need to have those maps, and I think with AI, and with the data that we have we can start building those maps. Thank you. Okay, so we have another question. So the other question is, what would be the approximate time years for artificial intelligence based public utilities to reach economies of scale, where developing countries could also make use of them for optimal standard of living for the masses at large. One question is, when will Utopia happen? But I don't think Utopia is a bad idea, not because it might come true, but because you want to know where you want to go. Yeah, and in some sense, you know, like what Utopia means changes over time and people have different ideas of what they would like their world to be like. And I do think that there's an imagination which maybe technology is going to be able to solve some of, you know, our problems like problems that involve like poverty, lack of housing, inadequate sanitation, you know, and one of the places where I am kind of hopeful, you know, and also doesn't get discussed that much, you know, is the use of artificial intelligence and robotics in construction, you know, you don't see too much here. I think probably China is going to be one of the ones that is going to get there first, but with, you know, prefabricated parts and with large construction machinery, you don't get to a point in the future in which you can have crews that are almost autonomous, you know, performing construction and those could be extremely useful in places that have housing shortages and there's big parts of the world with big stocks of housing are inadequate, you know, or that built with light materials, you know, or that built in places, you know, that were not pre urbanized and therefore the sanitation conditions are really bad and you cannot build sewers. Once the house is there, you know, it's easier to build the sewers the street, you know, all of those are utilities before. So for those large big infrastructure projects that maybe in partially the United States and Europe, we don't think we need because this is an infrastructure here, but in our parts of the world we do this type of technologies might make a big difference, you know, and I do think that a word in which construction machinery can have a big component that is automated my a lot some possibilities of planning and design that at the moment are not available. Okay, thank you. So, do we have other questions at the moment. So, so I have a question to see or just to follow on this topic so we saw that in the survey, just one person said that one that artificial intelligence is going to transform public policy and governance but the discussion that we have up to now we can see that actually, this is going to have a big impact on governance and economic policies. So I do see a few places, you know, that are very different where artificial intelligence can affect, you know, and the public sector on the one hand on the executive side. I do see an opportunity for artificial intelligence to have an impact and it's already having some of that impact and we've done projects on that space. What does it mean by on the executive side. So on the executive side basically, you know, you have budgets that you have to execute, you know, in agreement, you know, to a like like public policy but you have a lot of this question on how you allocate those budgets. So for instance, recently we did a project in a country of Latin America, in which we grab data about, you know, health and about hospital infrastructure to try to identify gaps in public investment in hospitals, so that they could better allocate, you know, their public investment. So this is a simple machine learning type of approach or algorithm, but it's the idea that when you're dealing at a country scale and you're dealing with so many people and infrastructure that is so large, and that no work that is able to know, you need this type of implementation tools to basically identify those those gaps that you might not otherwise, you know, be aware of, and to be able to do those budget allocations. Similarly, when it comes to innovation, you know, and economic development and regional development approach, Europe now requires all regions to provide a smart specialization strategy that they have to produce in the three years, and they have to produce that report, but doesn't tell them how to do it. So what is happening now is that across Europe, many people are starting to use the tools of relatedness and economic complexity to create that smart specialization strategy because that's a way to basically grab the regional specialization of, you know, that that economy, and use it to anticipate what's the probability that you would be likely to succeed at certain activity, what is the potential value of that activity. And that's again, I recommend the system like the one that Dana was telling us about, you know, in the case of Netflix, but applied to regional economic diversification and development. But again, you know, these are not democratic processes these are executive decisions that in the context that an executive has, you know, basically to distribute some resource, you know, in a way that is much more granular than the one that is told how to, you know, distribute. Now, there is another space, you know, which we can talk about later everyone but would be on the use of artificial intelligence now on democratic institutions. And that's very different than the one for the executive I think the executive side, it might happen even without people knowing about it. The other one, you know, I think it's much more controversial, but also much more interesting. Okay, thank you very much. So, Jean-François, let me ask a question to you. Oh, we have a question, do we have a question? Okay, so, okay, I have a question for you then. Okay, so you recently worked on artificial intelligence corrupting our morals. How would that work? Okay, first, in this case, you have to start from the baseline, which is how do humans corrupt the morals of other humans, which means that that we have different frameworks to think about that you could have people who are role models.  So the thing is, though we have to trans transpose that to the words of machines. So imagine that you see a machine doing something that is evil. I guess, many, many people were very outraged by the episode known as Twitter type, which is the day where Twitter bot was exposed to was deployed on Twitter to interact with people and started repeating racist sexist things that people were saying online. And many people were outraged and thought that if this bot is saying these things, other people are going to see that and be influenced by that. So that would be the role model issues. But then of course you also have an AI might give you advice that is unethical for some reason, and you would then you know just follow it without realizing. In our paper, we basically said that these two things are unlikely to happen for behavioral reasons. But the last one which is delegating something you know to be evil, or an ethical to machine might be a real threat. That is that people might feel that it's okay to actually delegate the dirty work to a machine and feel still feel good about themselves have plausible deniability about what it tells what it says about them as person because that's a very, that's a very strong motivation for people to act ethically. That is that they don't like to think of themselves as bad persons. So if they have a way to have plausible deniability. about their character by delegating the dirt work to a machine, then that's actually apparently a serious risk. So in some sense, if I understand it well, it's not my area of work. So maybe in some sense, these contradicts what Cesar says in terms of humans switching machines rights, because if there is an accident and this action was produced for machine. So people is catching more than if it's by a person. So that contradicts the thing that people choose a machine to do a thing or not. So, in that case, you have to imagine that the outcome is probably good. It's, it's obtained obtained by analytical means but it's good let's say that you delegate investment to an algorithm. And the algorithm, you know, you suspect is using privilege information that is taking from somewhere, and the outcome is actually positive you're making more money. You know you don't have the bad judgments from a bad outcome generated by machine because the outcome is good, but the means are an ethical and you would not do it yourself, because you would know this is not supposed to happen. But you know if you just suspect that the machine is doing it but you have no evidence then you know fine. I think that, I mean, in your opinion, machines should have rights, maybe, and the thing that what because one of the topics in ethics in artificial intelligence is whether the robots or machines should have rights or not so if for example there is a self driving car and crash. What happened with this machine so do you think that maybe the existence of rights in machines should change for example their results you obtain in terms of ethics also one or no. I, I'm not really seeing that for in this example, like the car crashes. Why would we want the machine to have right. I mean in that case, the problem is the responsibility gap I think that you're explaining. You know who's responsible what's going on. And it seems a very dangerous idea to say that the machine is responsible, because then the bucket stops, you know, the responsibility just stops there. And the car probably doesn't have a bank account to compensate the victims. The car cannot be jailed or punished. So, of course we want to say the responsibility should move on higher in the chain to the people who designed the car, the people will load the car in the market or someone some human at some point. And I think many people are saying that this idea of machines having right as a dangerous effect of making the responsibility bucket stop at the machine. I mean, if I can add a little bit so, and I agree that machines, you know, are very far, you know, and probably might never have rights as humans have rights but the rights over a lot of things, you know, and I think it's important to look at that nuance so I think for example, a machine anytime soon is going to have a right to life. But I do believe that for example, when it comes to copyrights. There might be ideas in which you might want to have a machine that produces something that something gets used by someone else.  Yeah, sure. So, okay, so finally, Dana, also, I wanted to ask you, so you haven't worked a lot on algorithmic game theory and pricing. Could you provide us some example of where algorithmic pricing is used? Yeah, so again, it's pricing, it's another example where we can use artificial intelligence to learn about people. So if we think about pricing, we think about a company who tried to fix an optimal price for their products. So, of course, if I'm the company and I want to sell this water and I know that you, your willingness to pay for this water is 10 euros, I will say, okay, this water is 10 euros, because I know that it is the value you have for the water. The thing is that, first, we don't know the valuation of people for products, because even if I ask you first, you don't have an incentive to tell me they're right. So, and on the other thing is that even if I know all these values, I can do discrimination. So there is one very studied topic that is discrimination pricing. And it's basically it's unfair and it's illegal to do discrimination just because I know that you can pay and you want this water and you are able to pay more. So there are a lot of studies and a lot of things to do about that. So we basically we can model this problem as an optimization problem is a problem in mechanism design where basically you want to maximize the revenue of the company that you take into account some equilibrium constraints that measure that the consumers are also maximizing their utility basically. So if you know that today the water is 10 euros, but tomorrow will be one at one euro, probably you will wait and you will buy tomorrow. So there are a lot of constraints are a lot of things that we have to take into account to to determine the optimal pricing policy and artificial intelligence helps to predict or to to learn this willingness to pay of people. There is one parameter one value that it's really difficult in economics to to learn. So having historical data and being able to learn about this historical data allows to put people in different clusters saying okay people in this cluster, maybe, and is able to pay this amount of people in this other cluster, another pricing. So based on that is that a pricing policy is made. And for example in terms of discrimination, because companies cannot do discrimination. So what they do is to do it without saying that they are doing discrimination, for example, airlines companies when you have different prices for the same seat, basically in some sense, that is discrimination saying okay. This is the same seat but now I'm saying that this is a business class. So you have to pay three times the value and maybe you would pay yesterday for the same seat. So, yeah, there are many problems in pricing and artificial intelligence can help in these problems, basically learning about how people think, how people evaluate different products. Okay. And so consumers behave strategically towards an algorithm, a pricing algorithm. The consumers. Of course, if, if I am a consumer and I say okay I will use all my knowledge about artificial intelligence or the algorithms to try to do the same strategy as them, I can learn about their strategy. And then I come in. This is the case where basically consumers are completely rational. This is the definition of being rational in economics, basically, I can learn and I can, yeah, I can guess, which is happening in future and based on that I will do the best for me. So, I think, yes, it takes time, but as, for example, with tickets for flights, we can learn about data because we know that price are dynamics and we don't know if tomorrow the price will be the same or not. But we know more or less that in some dates the price are higher or if you want to buy a ticket and you try to buy it one day before and would be really high price. So basically, if you use these platforms to compare pricing prices and an airline, and you have like a good algorithm and a lot of data and good data, of course, you can also develop an algorithm as maximizing your, your, your utility. Yeah. Okay, thank you very much. So, I think we have a question from a moment. So, he says, I think you have answered the questions in zoom. No, okay, sorry, not definitely the question. I cannot. So, the question is over there. Okay, so we have all seen how Africa is transforming itself through digitalization. A concrete example is a mobile banking, which has allowed the creation of new opportunities and the monetization of applications. How do you think Africa can take advantage of artificial intelligence and boost this digital transformation. Anyone can take the question. Someone has to buy the bullet, no, so I'll start, you know, and please join me, you know. So I agree that Africa did a great job at developing mobile banking, which is something that a lot of other places have tried to achieve. In the US, I met all of these entrepreneurs know that have been developing mobile banking and say like in five years, you know, like we're going to basically displace all banks and it didn't happen. It never never caught on and even the forms of mobile payments that that exceeds like the Apple pay, they're not as popular as like the, the good old plastic credit card that that gets used a lot in the United States and here in Europe as well. And so that was a big change now when it comes to artificial intelligence. Is this an opportunity for for Africa doesn't have the same type of conditions, you know, for leapfrogging that mobile banking had my intuition and here I'm going out on Olympus that they might not be the same, you know, in the case of mobile banking. There was a population that was a bank that was growing in terms of income, and there was a cheap and accessible technology like the mobile phone that could be used to provide banking service to that population. You know, now when it comes to the development of artificial intelligence, you know, it's not something that you are able to do so much in kind of like that distributed context in which, you know, you are satisfying a consumer need with a low cost, you know, in the case of mobile banking and a lot of the artificial intelligence that we see right now is being produced in very no centralized big efforts, you know, when you look at GPT three like this big language models, you know, it's really hard to compete if you're a person that is working on on on natural language generation. You know, you are basically in a university, unable to compete with GPT three and Bert and all of these big models because you don't have the engineering team, you don't have the computing but those models are very exportable. No meaning that through an API, you can interact with them, therefore the transportation cost is very low so you train them once, and many people can use them and that generates a very different economic geography than the one of mobile banking. And of course there might be opportunities to maybe develop applications and exploit that very fluid economic geography in the case of Africa, but I think that the, the geography at least of creating this organization like creating a mobile banking you know, in which the advantage is having locally and bank clients and creating an AI company in which the advantage is not so much on that demand side but more on having a real advantage on the supply side in an economic geography of low transportation cost and high value are very different. Thank you. Okay, so, okay. I don't know if I can read it. Can you all milky. Yes. So, Paul asked us to talk about the black box effect, you know, basically he's saying that if we judge humans based on intentions and machines based on output is that because of, you know, the black box effect is because machines cannot explain themselves. So, I'm sure Jeff from Sahas a lot to say there. I'm going to just mention one paper that I like a lot of paper by Berkeley divorce, which I'm sure you're familiar with, which is about algorithm aversion. And basically in that paper what they, what they do is they give no people a certain amount of money that they can decide to invest with a human financial advisor or with a machine financial advisor. In the experiment, they get to take the money back home so there's kind of like an incentive to try to maximize your bets, you know, to, to basically do wise investments. Now, what they find is that people prefer the human financial advisor, but then they have a condition in which the human financial advisor is paired not just with a machine financial advisor but they're also showing the picks that this machine financial advisor made, you know, and then they're sort of opening the black box they're not just saying this financial advisor is giving you 7% return.  So what, what they find is that people avoid the machine even more after they see the machine or after they see the picks. No. So as people would be kind of like over interpreting the mistakes of the machine and they don't feel so identified. No. So when you open the black box, at least that paper of course opening the black box in terms of explainability can mean a lot of different things you know that's a very narrow example. But it's still in that example, you see the opposite of what you expect based on that intuition.  Maybe you sold at 400. Exactly. So that's, that's what I wanted to add. Yeah, thanks for introducing that paper. So I can go straight to the floor. So yeah, I think we have to look at the black box in the outcome, first say that, okay when when we say that that, for example, people are less forgiving machines mistake than human mistake.  We know that various variants in human performance, you know, we don't expect people to deliver every day the same well like machines. So, so when someone makes a mistake, we know that yes many things could have happened and we're not quick to judge that the person is incompetent. So, the problem is that when a machine makes a mistake, we don't have the same kind of perception that there's a lot of variants, and a lot of reasons why a machine would make mistakes so it's tempting to just think well, if the machine is making a mistake that's because it's badly coded. Whereas we don't say that oh this human made a mistake once their brain is badly coded. And I'm not going to trust them. If we give some slack to humans we give less like to machines, but then there's also the problem that indeed the black spot problem is that the thought process of machines are opaque to us. Again, it's always nice to go back to the baseline and to say our human thought process. So transparent to us because I've been a psychologist for 20 years, and for the most part humans are still black boxes to me. I don't want to over and we know to do over emphasize the understanding that we have about how humans decide. But yes, at least we have at least the illusion that we understand what other people think and how they decide and, but we don't have that for machines. And that was very striking to me when for example, there was this very highly publicized car crash into Arizona where Uber vehicle with a safety driver crashed and killed a pedestrian. And in the days after the crash, you know, a lot of information came up, like, okay, the car, perhaps add issues with identifying the object while the human on the road. But then the safety driver was distracted and was watching the voice on her iPhone during the ride. And the pedestrian was crossing the highway at night in the dark. And outside of a crossroad. And so you would see that, okay, it's very complicated situation but what you saw that is that everyone, everyone jumped at the human responsibility, either safety driver pedestrian for the first few days. And you can understand that I have no idea how a car thinks. I have a pretty good idea that yes, not watching the road that you're the safety driver is bad that crossing the road at night is not so smart, but the machine, I don't know. So in that case, we think that okay giving more information about how the machine perceived the road and so on, which changed people's mind, and it did to a certain extent because there were explanations about the mistakes that the machine did. These explanations, they're always very imperfect very crude. Sometimes I compare the explaining machine thought process with visualizing a complex data set. The figure, the graphic you make from a complex data set is never the exact truth. It's just the way you have to frame and emphasize some aspect of the data set servers, like a persuasive component to it. Sometimes you sacrifice a bit of accuracy, or a bit of granularity to make your message more compelling. All right, this is what we are condemned to do. When we want to explain complex algorithm, we will never get rid of the persuasive component. You know, we will never be able to give a neutrally framed explanation of what was going, what is going to do what's going to happen. But I don't see how we're going to solve this all our efforts toward explainability are always going to hit that wall that if people realize that there's always a persuasive components to the way you explain the machine, they will never trust completely. What you're saying. Is the one on the top. Okay, that so from a cyber security point of view, which we kind of attack can be expected to artificial intelligence based system, how disruptive could it be, and how could it be prevented. I'm just a humble psychologist. That's definitely also very far from my area of expertise, but I met some people that you know have been working on this along the path and of course we'll know about this adversarial attacks you know we know these attacks in which sometimes you change a little bit of a stop sign and the machine you say that's a duck or whatever something different. The point is that you know you can fool the machine in a way that you will never be able to fool a human, because the way that the machine is interpreting the input, you know, because of that obscurity. Sometimes it's not very robust, you know, so there's a lot of these adversarial attacks and I don't think that's a bad thing I think that's a great thing. I think that's the evolutionary pressure that you want, you know, on the development of those algorithms on the, you know, training data you're going to use and whatnot. I think a lot of people have been doing great advances at developing AI that is more robust by combining these statistical learning models that sometimes can be very good at the scalability and speed and performance and all of that. But, you know, all of a sudden there's some corner case in which they make really stupid mistakes with AI based systems that are based models symbolic logic, and that in some sense you cannot fool in the way that you might be able to fool a system that is trained and you don't know that much what's going on inside the network. So, so, you know, there are attacks that you can do by providing input that can fool the machine, but there's also a lot of effort to create these robust algorithms, you know, and some of them involve this symbolic logic, some of them also involve mimicking some sort of feedback structures, you know, that are able to denoise, you know, the input as it moves, you know, throughout the layers of a deep learning system. So, I think this is a very interesting, you know, question because we are kind of at that space in which the technology was developed, the mistakes were understood, and now we're starting to figure out how to create a technology that is robust to those mistakes. Thank you. So, so it's almost 6 p.m. Thank you all for coming. Before we round up the discussion, we would like to present our latest IST magazine with a special focus on artificial intelligence and IST 10th anniversary. Okay, so we have now come to an end of today's event. Thank you, Jean-François, Cesar and Dana for coming and for sharing your expertise with us and for sharing for answering so many questions. And thank you very much to the audience for being with us and for participating by your questions. So, if you're interested in our research, please visit our website. The link should appear in the chat right now and follow us on our social networks, Facebook, Instagram, LinkedIn and Twitter to stay updated with the upcoming events. Thank you all for coming and have a great evening.


Thank you for coming today. I realize it's very rough to break away from the office on short notice, but the reason I've asked you to come is an important one, kind of an arm-twisting session. For more than a decade, the modernization of America's land-based strategic missile force has been analyzed, debated, and pondered by the Congress, four administrations, countless congressional committees and subcommittees, and nearly every columnist who's ever put pen to paper. During all this time, not one new United States missile has been deployed. In fact, the opposite has occurred. We've begun retiring our Titan ICBMs because of their old age. Don't think what I'm thinking. ICBM, of course, stands for Intercontinental Ballistic Missiles. By comparison, last year alone, the Soviet Union deployed more than 1,200 ICBM warheads, which is more than is projected for our entire MX peacekeeper program. So we've had a price or paid a price for our past unilateral restraint and indecision. And the growing imbalance in strategic forces has weakened the credibility of our nuclear deterrent. Our self-imposed restraint has left the Soviet Union with far less incentive to negotiate arms reductions. The debate, analysis, and posturing are just about over. Within the next two weeks, the Congress will decide the issue. They'll determine whether or not our deterrence, the key to peace in the nuclear age, will be strengthened. They'll also decide whether arms reduction negotiations now underway will have a chance to bring about a safer and more secure future. When the Congress votes on the peacekeeper program and later on the small missile funds, it'll not be just another vote. Leaders in Western Europe, in the Kremlin, leaders everywhere will be watching because they know the decision will have a profound impact for generations to come. It's hardly possible to exaggerate the stakes that are involved. The full Senate is scheduled to debate later this week on MX and the House next week. The specific legislative proposal is to approve flight testing of the peacekeeper missile and the work necessary for basing it in the existing Minuteman silos. Implicit in the vote is the OK for production of the missiles, and this is the essential first step for deployment of 100 peacekeeper missiles beginning in 1986 and for the development of a new small single warhead, ICBM, which would be mobile. Congressional approval will be a clear signal of national resolve. The critical message so necessary of where to assure deterrence and real arms reductions. When I endorsed the recommendations of the Snowcroft Commission, I did so in large measure because of its thoughtful integration of three indispensable elements, modernization, deterrence, and arms control. There's a direct relationship between modernization programs like the peacekeeper and the twin objectives of deterrence and arms control. Tear the elements apart and you have less security. Keep them together in the future of peace and freedom are strengthened. Now, this isn't a partisan issue. The Snowcroft Commission demonstrated that it's possible to undertake a complex emotional issue of extraordinary consequence and achieve bipartisan consensus. Leading Republicans and Democrats across the country and senior officials from past administrations joined our call to build and deploy the peacekeeper and to build the small single warhead missile, but not so we can fight a war. They want to build those missiles so America can secure deep arms reductions and preserve the peace. So do I and so do we all. Now the question is whether the Congress can also reach a consensus, a consensus that will unite us in our common search for ways to strengthen our national security, reduce the risk of war, and ultimately reduce the level of nuclear weapons. Security and well-being of our nation desperately require a lasting national bipartisan consensus. We must sustain it from one administration to the next on arms control initiatives, defense policy, and fundamental issues on foreign policy. A vote for the peacekeeper program can lay the foundation for this consensus. During the past several weeks, I've met with many members of the Congress. I've provided written replies to the specific concerns expressed by individual senators and congressmen. And now we're approaching the critical moment. In my discussions with the Congress, I've asked all Democrats and Republicans to act responsibly and to act together to endorse the Scowcroft Commission's recommendations. Last week, two key congressional committees voted to support these recommendations. And now I'm asking you for your active support. I want to again thank you for taking time from your busy schedules. Your presence proves you understand the stakes involved. But understanding isn't good enough to ensure that the final decision is a reflection of a national consensus. You must express your views and make your voices heard. I hope that you'll do just that and do it soon. The legislative clock is about to run out on this particular issue. So again, I thank you for being here.  Well then, why did I say it over and over again in the campaign? Why did I appoint two commissions that are in Geneva, one negotiating on the strategic missiles and the other on the intermediate range missiles in Europe? I do want arms control. I can't believe that this world can go on beyond our generation and on down to succeeding generations with this kind of weapon on both sides poised at each other without someday some fool or some maniac or some accident triggering the kind of war that is the end of the line for all of us. And I just think of what a sigh of relief would go up from everyone in this earth if someday and this is what I have my hope way in the back of my head is that if we start down the road to reduction, maybe one day in doing that, somebody will say, why not all the way? Let's get rid of all these things. And that's why any of those fellows up on the hill lean on them. Do anything you can to persuade them that we must have this. I know it sounds silly to build a missile in order to get rid of missiles, but we're very dangerously close to not having the deterrent that we need to keep the other fellow from using his or at least using them for blackmail. So we have to modernize to the point that there is a deterrent that he knows that the damage to himself would be too great to risk pushing the button. And then with that taken care of, we start down to an equal and verifiable limit on both sides. And I'm going to quit talking because I know there are others that going to be speaking to you and I've got to get back to the Oval Office. But thank you all again very much for being here.


What's going on guys welcome back to the channel critical little here So let me talk about the Halloween franchise in this video here again today Mostly just the Halloween franchise and I'll teeter off into the Strangers because we got an exciting update regarding Madeline Pesh's role in the upcoming stranger trilogy now We'll preface this by saying if you do not want any spoilers for the stranger's trilogy and Madeline Pesh Then click away once you hear me start talking about Madeline Pesh Firstly though I do want to thank those of you who took the time to reach out to me after this somewhat exciting news And I'll get to why it's somewhat exciting But yes, I do want to thank those of you who are all very sensible individuals who genuinely enjoy getting to know me because Something about me I guess just makes you interested in knowing more about me beyond just what I upload for content purposes and for Entertainment and discussions with a lot of you guys Especially drum-dums who I want to personally name because drum-dums has seen me be correct several times This of course not being one of them and that's fine Thank you also to phantom empire and everyone else who has taken the time that you did not have to to reach out to me And trust me all is good on this end I've stated in a lot of my videos that I do not mind being wrong I was wrong about Sydney Press God and Neve Campbell returning for scream 6 I did not take that stuff down because I want it to stay up so that people can see that I really do not mind being wrong I can confidently accept that my source was not correct in this instance But I will not do is ignore the amount of times that they've been right with Maxine being a whodunit Which has been confirmed scream 7 and Chris Landon, which was confirmed Linda Blair nexus believer confirmed Radio silence producing scream 7 confirmed and so much more than that person has been correct with Correct about with me. So I'm not gonna cut them off However jumping into the actual official Halloween news Deadline report today that after a after a heated bidding war Miramax has scored the television rights to the hugely popular Halloween franchise Which are controlled by Trinkets international films run by Malik Akkad Miramax has signed a wide-ranging deal with Trinkets to develop and co-produce a Halloween TV series The new Halloween series is envisioned to potentially launch a cinematic universe Spanning film and television Miramax's head of global TV Mark Hellwig will be overseeing the franchise Creatively in close collaboration with Akkad guys keeping my Miramax already has hand has a hand in the rights to the films So a paramount owning Miramax. I believe since 2000 I believe Paramount seems like the likely distribution choice and 824 just completely fumbled. Unfortunately as many of you have actually Made comments that you're disappointed that it didn't go to 824 and a cinematic universe doesn't necessarily Excite me all that much but a lot of great Michael Myers season of the witch and other Content could be on our way or we could see the same frustration folks seem to have towards the MCU and DC as of now Not everyone. I still would love to see That Loomis show that I've been thinking about that gradually builds to Michael Myers breaking out and going home a show that spends a few seasons on Loomis and Michael before continuing on the big screen if you want to do that and Then allowing that same show that starts off being about Loomis and Myers to explore stories Like Charlie Bowells or even another Corey Cunningham type could be the perfect route to take or again This could all just be overwhelming depending on how this is executed I saw a lot of you guys feeling a little let down by the official news because you're like really a cinematic universe And I'm assuming most of you might be fans of the MCU and DC and you might be feeling a little bit Overwhelmed with all that content that they're putting out So a lot of you also just seem to be completely tired of Michael Myers keep in mind guys a Lot of you like me probably grew up on this stuff So we're kind of an autopilot while the franchise is constantly being rediscovered by new people While you have folks like me and many of you who listen to me who grew up on this stuff And that's why you naturally would be tired of it A lot of people were just introduced to Michael Myers courtesy of what Blumhouse put out in 2018 and what they continued to put out up until 2022 so we're kind of again just an autopilot with this franchise It's a no-brainer though that they work this fast to get Michael back out there because the 2018 film overperformed And the trilogy was a success despite personal gripes that you may have with some of the later entries It was a success But yes guys that is the official update regarding Halloween and again I really do appreciate those of you who are sensible Rational Understanding have taken note of how many times I have been correct and you reached out to me Being respectful being courteous being kind and not or reminded me to just take this L with grace Again, no problems on this end. It was a loss. That's fine. Not a problem at all However, I will say that I still am a little bit Split on the news because a cinematic universe could be good It could be bad could be too much and a lot of people might be turned off by it But again guys, that's probably just gonna be the sentiment from folks like me who grew up with Michael Myers and have seen these movies countless times and You rightfully think that it's time for this IP to take a break. It's time for Michael to take a break It's time for them to just create new Entertainment, but however, there's an argument to be made There's a reason why they aren't when you show up like this when they put out sequel after sequel after sequel Timeline after timeline after timeline, but we don't show up for some of the newer content You know, they take note of that and that's why they feel confident and constantly Regurgitating things like what they're doing with Michael Myers, even though it's gonna be a fresh approach It's also gonna be teetering on a lot of things you've already seen so I get why a lot of you are disappointed by this But I'll remain open-minded. I'll see what ends up coming out and we'll just go from there. So Talking about the strangers here Rene Harlan and I think as of now There's a panel going on at New York Comic-Con where there might be a not might be there is a first look of the strangers being Disclosed to those in attendance Rene Harlan those sat down with blood disgusting lately and said this about Madeline Pesh's role in the stranger Trilogy, let's say it's no secret that the main character is Madeline Pesh Who is the star of the first movie and she will continue to Continue to the second and the third movie and it's her journey that we are watching from the moment the first movie ends We get to see what happens to her five minutes later. So she's gonna be a part of chapter 2 She's gonna be a part of chapter 3 So he's kind of already let it be known that she most likely is going to survive Chapter 1 which I guess is fine. Maybe she doesn't survive and somehow she just still appears But I'm gonna assume she definitely seems to be surviving I don't think we're getting a lot of flashbacks in part 2 and part 3 I think she's gonna ultimately just be our final girl who is alive survives part 1 and continues on into part 2 and part 3 That might be exciting news for those of you who are looking forward to understanding what Madeline Pesh's role would be in the upcoming stranger Trilogy because I know many of you like me are excited to see how she does in this role And again I've made a mention of the fact that when I was watching Riverdale the person I was paying to paying attention to the most Was Madeline because I thought she was doing the best work out of a lot of the actors involved with that show Even though I gave up on it during season two. Let me know what you guys think about this down in the comment section below What do you think about the official news for Halloween? What do you think about Renny Harlan's comments on Madeline Pesh's role in the upcoming stranger trilogy? I'll do another update on the strangers if I have time later I have to prepare for this for nearly four-hour Martin Scorsese film Although it looks good. I am a little bit already just kind of taking a sigh of Really because it's it's long but I shouldn't be shocked knowing that filmmaker Let me know down below if you haven't already course make sure you subscribe turn on post notification in this video in the description I have links on my social media accounts. I am on Facebook square in Instagram You can message me there of course limit is any movies news or views you'll have me to cover in the future And with all that in mind guys, I will see you in the next video