[Auto-generated transcript. Edits may have been applied for clarity.] Uh, this lecture today is part of the MSC in Translational Health Sciences that we are running here
at the University of Oxford on specific themes of technological innovation and digital health.
Uh, so Nicole is an associate professor in business and society at the National College of Ireland.
Uh, we're really happy she's going to talk to us about power,
politics and the fight for justice in the age of a and really hot topic that's been discussing, uh, some of these issues earlier in the week.
Uh, Nicole is really widely established. She works with researchers across Europe as well.
So you bring that perspective to Nicole and people to hearing about your work.
So over to you. We have a battle now. The next going to take about an hour.
And then we have five minutes for questions and discussion.
Perfect. Well, thank you so much for having me. Um, I'm delighted to be there because I think this is a really important topic to talk about.
And, uh, I, I lecture myself.
So I do tell this stuff to my own students to, uh, but also in other capacities,
whether it's publishing or policy papers, I try to bring, uh, the same message across in different forms.
Um, so my goal today is really to tell a story, um, and the story is, um, about, um,
digital health because digital health is so much more just, um, technologies or tools or devices,
uh, because it's just easy to pick off, uh, a theme, so to speak, and think that's digital health,
but it is really a story about power and politics and the fight for justice.
And because I want to tell a story, I have divided it into different parts.
So the first one is like, how did we get here? Because obviously, you know, um, everything has a trajectory.
And the trajectory is important in many ways because, um, unless major interventions happen,
like, especially when it comes to power and politics, things tend to go into similar direction.
And that's called often in the literature part dependency.
So um, and then obviously I have three different parts here to it.
And uh, your team had asked me to present on three of my papers.
So the first one is called choreographing for Value in Digital Health.
Uh, the second one is a potent problem indeed, where I talk a little bit more about AI because this is so relevant at the moment.
And then the last one is healthcare at a crossroads. And then, of course, wouldn't be a good story without a contemplative pause.
And then a question, where do we go from here? Um, so I suppose all good stories start with, uh, once upon a time, uh, tagline, of course.
And I'll do that too. Okay, so as I said, every story has it gone.
Once upon a time moment. So, um, this scenario happened in Ireland and Ireland.
That's important here for that kind of context because, um, Ireland is obviously lauded as the tech capital or the Silicon Valley of Europe.
Um, so once upon a time in a research centre, uh, in Ireland, they, uh, that was in around 2015.
Here you can see of, of a press release, um, there was, uh, an industry and academia collaboration, um, where basically people from industry,
the state healthcare providers and academics came together to try to make sense of digital health and try to,
you know, bring it into the market and also to up, uh, public health and public value,
really, uh, for your convenience, have obviously highlighted a few key words here.
Um, it was talking about a proactive model connecting stakeholders, acute care patient at the centre, empowering patients, um,
having good interventions in the care pathway here and then scale, um, personalised health care,
you know, patient experience and reducing costs, which is all very important.
And of course, we know, um, that Europe and many other countries are on a big crash course in terms of ageing populations,
um, health care constraints. So, of course, you know, given that kind of team health care or public health was, uh, divisive.
So that's what, uh, I work in this research centre.
Of course. That's why I kind of use this as a as a theme.
So the vibe here was that we are coming together to bring, uh, digital technologies into public health.
And of course, uh, the the promises are always very, um, enticing.
Here, now, sorry, this is not working again for me to go to the next slide.
Oh, here. Lovely. So the promises, of course, of digital health are very enticing.
Um, and they still are to this day. And I even to this day, I do believe, truly, that digital health has enormous,
enormous capabilities to make public health better when it comes to likes of disease intelligence wanted.
Clinical decision making, patient centred care.
And indeed we see many, many benefits of public health and digital technologies already in public health right now, and even patient empowerment,
having a doctor in your pocket and as a mom, as a matter of fact,
like many of us have health apps and they use AI even know to kind of hack their health.
Um, of course there's big promises of operational efficiencies, better quality of care at speed and at scale,
cost savings, etc. and of course that people get better and faster if they work in health care too.
So, um, moving on here, you can see here, um, with the best intentions in the research centre, we started researching and advising and executing.
Um, and you can see a picture of me here, and I be very happy. Uh, I that's because, like, we we had problems along the way, too.
And, uh, this is a picture of my, uh, lovely co author, Susie Geiger there that you will find on many of my papers here on our paper,
combined papers, and of course, many other researchers. It's never a team.
It's it's always a team effort. So I worked at the time in a particular unit.
So this gives me probably a little bit of a tainted view as well because there was different units at the research centre.
But the questions we were asked all the time is what is the best business model,
you know, if if by the companies, uh, and what's a good market entry strategy?
How do I sell to the national health care system or, you know,
in Ireland and also and so in Europe and in fact, what how do I sell my testing anywhere?
Because our experiences with some of the technology companies were that they did develop their
products in isolation of care pathways or revenue models or even what's going on in public health.
Um, that kind of gave us, in some ways problems, because we went into the research centre with the promise of improving public health.
So we were asking questions, what what about connecting stakeholders?
What about patients and care pathways? What about improving health care?
And remember public health, of course. Um, and which was very interesting because like, um,
fast forward a little bit of time and the research centre field and we also written about this afterwards in 2022,
um, in a paper called Leaning in or Falling Over What it takes to make a market.
Um, so this was actually quite complicated because, like,
there was a big mismatch between what they wanted and what we wanted to give them or what we wanted as academics.
And we ended up working and in a kind of state of liminality where we didn't know what what was happening and if you were coming and going.
But it was very chaotic at the time to, um, in saying our experience as a research centre wasn't unique, really,
because all the companies were trying to get into health care at that at that stage,
big and small, uh, and there were struggling and there was many reasons for that.
It could have been that they as I said, they didn't understand, uh, power structures in health care.
They didn't understand national health care systems, pathways, revenue models or revenue pathways.
Uh, but they wanted to sell their thing there, take things, so to speak, of a solution.
Uh, it cetera without understanding necessarily, how to do a good value proposition that address public health.
And as I said, the research centre ended up failing. So, um, moving on here.
So from 2014 onwards, we saw some quite concerning shifts.
Um, we tracked them over to time to. So it was called e-health, mHealth, connected health or digital health at the time.
That's a term that we seem to have settled now in, in the literature.
But it was truly a wave, a hype and a voyage. And why I'm saying that is we did.
You can see here a paper on the right hand side. It was called this hype.
Uh, creative irreversibility. And you, you need a lot of promises and investments to build a market.
And at that time in the mid 20 tens, these investments often came from the state who supported that,
but also from the tech companies, of course, too, because they wanted to find a market for their technologies,
but because it was so difficult to get into the public health market,
many of them defaulted into the direct to consumer market, which at that time was growing quite heavily, too.
You might remember two times of, you know, your first fitness tracker or your first health app,
etc. and of course, the the health care system also lauded patient empowerment a lot at the time,
saying, well, it's not just the doctor that needs to tell you what to do,
but you also need to take care of your own health care and your own responsibility.
So at the same time, we also saw an increasing data application and digitalisation of health care,
which is not a problem in itself, but it came alongside an increase in growth of surveillance.
And data capitalism. Shoshana Zuboff has written a fabulous book about this, um, which is well worth reading for anybody who's interested.
Uh, but the idea is that basically data like that, the data is stripped of people and to pervasive surveillance,
you know, you like you have lots of data trails in your daily life behind.
And we know that. And but this money, this data is converted into assets.
And by assets I mean like, you know, things you can sell on the market and have real current, uh, earnings from that.
And of course, when you think about now the advertising market like it's Google and Meta and many other advertisers,
they basically absorb your data and then sell it on the market for, you know, for it's called the market for future behaviours.
To any companies that want to know what makes you tick. But they basically tell all, take all your data and sell it.
Um, and of course, and then the advertisers use it for, you know, targeting stuff back at you.
Um, so we track that in multiple industries, for example, in the consumer genomics industry where you,
you know, the 23 to me and ancestry etc., that they have a multi-sided platform,
you pay for the service, but you also kind of get your data in the background if you agree to terms and conditions or at times even to research.
And the consumer doesn't know what research is, so to speak, if they think they're helping medical science, um, at the same.
And also we looked at that in the context of mental health apps.
Uh, we saw also very worrying, um, developments in, in the increase of power of big tech and colleagues like, uh,
Tom or Sharon and colleagues, they have talked, they have written about this quite extensively, and I call this a sphere transgression.
So when big tech companies started to get into health care, into public services,
they're kind of getting into spaces where tech companies probably should not have as much input and power.
Um, and what also became very clear that there was a very, very clear move away from public health promises and promises.
Um, and that is, of course, a worrying development.
So, um, at that stage, of course, along came Covid and that was in around 20, 2020, as you most of you will remember.
And then, of course, there were so many questions that came along with covet.
And of course, as academics, we take these questions and we we use them as an opportunity to do more research.
So our questions were at the time, you know, when Covid hit.
Well, what what was a public health crisis of this scale due to the adoption and scale of digital health?
And many of you might remember the, uh, the Covid tracking systems or booking system.
So everything all of a sudden got almost digitalised overnight because we had no other choice because of the contact, low contact kind of environment.
Um, and of course, at the same time, you know, because the state was or the many of the European states were a bit out of their depth.
They also asked, uh, tech companies to come on board to help.
Um, so we saw public, uh, public private partnerships flourishing.
But we asked ourselves, will this mark a return to public health?
Because we saw this big crisis and we asked ourselves, well, will this be a good a bad thing for public health at last, as their power shifts?
And then obviously, as we know from the contact tracing within Europe, on the back end of that, the European health data space was developed.
Um, or or not, of course, because these are always questions that are remain to be seen.
And that's what I meant by my point in the slide that it is a journey too.
So we ended up taking that into the deeper as we do, of course.
So digging deeper here meant, oh, sorry, let me go back.
Um, the, the in previous papers we had pointed to finger quite well at big tech, you know,
and that these that sphere of transgressions and they were having more and more powers.
But we also asked ourselves at that point, well, you know, what role does the EU or state policies really,
uh, have in choreographing these public private entanglements?
And they are actually entanglements, uh, we call them actually techno tangles,
which are push and pause or dance, as we call them, a dance between the EU and Big Tech.
Because it is a back and forth, it's never a clearer, you know, just one contract.
It is a back and forth because they in some ways they need each other.
Like in many economies, public value and market value is intrinsically entangled.
And this is one way of seeing, um, digital health to us as an entanglement.
And the other question that we had, of course, is how does can this dance orientated towards public health?
Because this is something that we wanted to. Keep in mind from day one, in telling the story to that public, how it is here, what matters.
So what's the problem here? Well, you know, there was many issues at play.
For example, um, the entrepreneurial state, um, many of the we know that from America anyway.
But many of the European countries also they play entrepreneurial state.
And Ireland is particularly guilty in that respect with our foreign divestment, uh, foreign direct investment strategies.
And it's also very clever in many ways to of course. Um, but by playing Entrepreneurial State, um, you are encouraging these techno tangos,
but they always come with consequences to benefits, promises and perils.
Yeah. Of course. Um, and Mariana mazzucato has written quite a lot about the entrepreneurial state and quite critically too.
Uh, so we have also a big data driven health economy by now that we cannot talk about healthcare anymore without talking about, uh, data.
Um, so of course, uh, and there was also state driven data sales already in the UK happening at this time.
And of course, there is a quite unstoppable blending between public health and new digital, uh, concepts and tools that, you know, as I said,
you can't talk anymore about healthcare but are talking about digital health,
and that will be coming to that point in time where you can't talk about it anymore without talking about AI.
So and the looming question here is, well, what public value is actually created in and through those health tangles?
And that is a difficult question to ask.
So what we did is we went through 30 years of documentation from the EU and did a discourse analysis pretty much to
see about that kind of notion of public value and how it is directed and shaped and shifted and how this can happen.
And the question was also, of course, what does the European health data space achieve here in terms of the public health and public values now?
But of course, what is public value anyway?
Um, it's the question how public sector institutions build a further their democratically established schools.
And again, when it comes to healthcare,
we always have to remember that healthcare is very special because health care is a public good and a human right as defined by the W.H.O.
That means the state has a social contract to its citizens to provide health care.
And of course, how does it improve the still lives of citizens.
And it's also a way of measuring progress towards achievement, of widely accepted societal goals.
And of course, the state always has to be proactive and value creating in,
in, in creating these kind of, um, improvements and to further social goals.
And our question was, well, how does the state do that over the course of 30 years and all their policies to do with digital health,
how are their mission oriented policies now? The grim realities are in many ways, that's when it comes to sorry, what's that question?
Okay. Uh, the grim realities are in many technologies.
Even if you hold your own mobile phone here in many technologies.
Even, as I said to your smartphone,
the fundamental technological tools and infrastructures that went into that were actually developed by public investments, uh,
by the state investing in these technologies, however, you know,
it is the apples and the Samsungs of this world who really monetise these technologies, and it's informed by the same thing.
There's many public investments that have gone into fundamental R&D.
Um, but they have been turned into assets by, by pharmaceutical companies, for example,
and Covid alone, nine new Covid billionaires where basically, you know, um, born.
But many of the as I said to you, the vaccine technologies or the vaccine research was actually done by public investments and universities.
So at the same time, we have also a lot of infrastructural monopolies,
even think about your own kind of, uh, in healthcare, I think about the power of big tech,
all the emails to the devices, to infrastructures, the cloud space, all these infrastructures,
uh, often, uh, you know, owned and held by a handful of big tech companies.
And at the same time, we have a what's called a lot of data lockouts.
So they are taking charge of the data, uh, in the absence of knowing who actually owns the data.
And, but they're also excluding the public from this data and from what's, what is supposed to be an information commons.
And what I mean by that is that data in general could be a common good.
It could be owned by everyone because you own your own data too.
Um, but yet you have no idea where it goes half the time and what happens with it.
So we lose, we lose sight of it. Um, and of course, there is pervasive and increasing surveillance.
Um, and also what's called a data enclave.
And Cambridge has written about this,
about data enclaves to these are all protected spaces that big tech built to keep this data safe and for themselves,
and that other people can have access to them. And of course, during Covid, we had a large new wave of Covid.
Techno tangles and new norms were established here that are very difficult to overturn.
Now, the European health data space is interesting here because yes,
there is definitely much more medical research and innovation because of that, because now we are either at a position where data is shared.
That was before. Nor I can also say enclaves, you know, within different countries, but now it's more shared, which is which is a good thing.
Um, so there is much more opportunity for public health research and innovation too.
But at the same time, we have also a big European data lake now.
And when you think back now of the story I've told you so far about sphere transgressions and and technology companies accessing data,
you know, quite freely, then you can see that this is a problem.
And of course, many of when I look back at the documentation, many of this is cloaked in snazzy business language,
like old data for innovation and opportunities to enter new markets.
And there is nothing new here because looking at, uh, the, the documents over 30 years ago, quite clearly that this language is very pervasive,
much more than the language of public health or public value, of course, that it's very, very complicated.
Questions, too, about who directs these policy conversations and how is this done?
So colleagues here, um, and I'm show you a really quickly, I'm going to pop out of this for a second.
Colleagues here.
Um, but this, this great, uh, Dutch repository here, um, which I have written quite eloquently here by presenting big tax law lobby playbook.
And they have done it for different countries.
So these are all case studies here about the narratives and, you know, echo chambers and stuff like that.
So it's very, very good. These are all in the presentation. So you can click into it again at your convenience to to look at it more.
I'll share a presentation afterwards for to share with the audience of course.
Um, but this is a great repository which shows you how they do their lobbying.
And the dangers, of course, are committed to.
So, um, and of course, there's loads of interesting models here too, and of course that the pharma, sorry,
the big tech playbook and the lobbying influences has influenced quite heavily also the European health data space.
Now the interesting models that um, that came because of that was the the primary and secondary use of the data that flows within Europe.
Now. Now the primary use is everything medical really any.
To do with health and medical stuff, and you can't really opt out of this.
This data flows either way. But there's also a secondary use of data.
And this is what what is a lot more difficult to understand.
It is the stuff for data for innovation and markets and marketing that is a lot harder to grasp,
uh, for an ordinary person, you know, for the ordinary Joes.
So that clicks agreed to all cookies that they see, right.
Um, because people are not as informed us as, you know, as we should.
They should be. And then this also this questions about opting in or opting out of the secondary use of data.
So the initial question was should people be automatically opted in and then they have to opt out,
or should they be, you know, um, just, you know, should you opt out the another opportunity or option?
So what they have done is now so basically should people have to specifically opt in or specifically opt out?
And what they have decided now is that everybody is kind of opted in, but it has to be an opt out option.
But they left it to every country now Ireland, you know, the Netherlands, Germany, France to to implement that for themselves.
So how that looks like now is up to every country.
So there will be fragmentation, but many people will not understand that the ordinary Joe open not understand.
If they are sitting in the doctors and they are asked, would you like to participate in research with your data?
Many people are very altruistic and go like, oh well, I can do something good here.
So yes, they don't understand that necessarily. What that means that they should be opting out or what what any of the implications of that are.
And the question is also when it comes to secondary data, you know,
the premise is it can be used by by, you know, invite the by the market, by tech companies, by,
you know, anybody who, who has an interest in health care,
as long as it has a benefit to individuals and society, as long as it develops, basically delivers public value.
But the question is, how is that actually policed and accounted for?
Who who checks up on this and what does that actually mean. And that's these are the the complicated question.
Many of the European health data space now.
Moving on here. So what? Solutions are possible. So is it possible to redirect value flows back to the public?
They cannot be public private partnership or technical.
Technical tangles should not be based on unbalanced or parasitic relationships.
They should be.
There could be profit sharing clauses, conditionalities in public contracts or digital tax that could be redirected back to public health.
But it needs political will also by the state to put this into contracts.
And at the moment, you know, as you can see from this kind of act or chapter,
that the state has a lot of responsibility also to enable the companies to do what they do.
There could be also a public value would be beyond financial returns or to do an exercise of public value mapping.
My colleagues here have a data solidarity in the University of Vienna,
have a data solidarity mapping tool where you can click into it and see how much public value does.
What does the data even generate?
Some of them don't even generate any public data at public public value, so it's not probably even worth, you know, worrying about this too much.
But some data really does and that needs to be looked at.
You could also have a common space approach to how the data to have to make sure that these data enclaves or that these,
these protected spaces, these monopolies are disbanded.
So you could have, for example, data sharing pools or data courts of public data trusts.
But the point is they shouldn't be all owned and managed and and held together by the big tech companies.
So of course then along came AI or generative AI.
And I'm sure you all remember this when the gen AI models started scaling.
And of course, now we are in a position where they have crept into every tool, into every tool bar, into every application.
AI is somewhere there. And of course, with that, there's even more questions of, you know, it is a truly transformative technology.
So will it change digital health and how will it change due to health?
And of course, how it will this transformation mean a return to public value at last?
And finally, and of course, is there any issues emerging here?
And then of course we we dug deeper and I put this little robot here.
Of course he is. Of course, robots are not the same as AI. We know that.
But you might want to remember him or her or this this bot here.
Okay, so a potent problem indeed. So this is the second act or the second chapter I want to talk about.
And of course, you've seen it even now in the media, AI is the promised land.
Of course we wonder why we had this heard this one before. We have heard this exactly ten years ago with Digital Health,
where we had all these promises which are needed to basically and investments to build a market and to build or to let these technologies flow into,
you know, the applications or real life. And the research questions here, where, um, how does generative AI, um, impact health care?
What data does this bring to bring along? And how how can these be addressed really?
And I'll explain data justice in a minute.
Of course, it is a transformative technology, and it brings along so many complex and layered ethical hurdles that affect health, wealth and power.
Um, so data justice in this most simplest form is a fairness by which people are made
visible or presented and treated as a result of their production of digital data.
And we all produce digital data.
You cannot be any more existing in the world without being online in some shape or form, even older generations now.
But you think about by 2030 all public services are online basically.
So it doesn't matter. You have to participate in this ecosystem.
What is meant by visibility of by Taylor is this access to representation to information
privacy by by being represent that it means how do you engage with the technology.
Is the other benefits shared and is there autonomy in technology choices?
And even what I said a minute ago, like if everything is online, it is probably not even so many, uh,
choices anymore because we are already catapulted in an in an element of choice lessness because everything is online.
And then of course, um, the last bit is how are you being treated?
Is that non-discrimination? Can you challenge bias and can you prevent discrimination?
Now, Taylor extended their framework in 2025 on data justice to include preserving and strengthening, uh,
public infrastructure structures and courts because they realise that this is so much bigger than just the individual.
Um. A small ecosystem, but it is truly, uh.
How technology flows into public goods and infrastructures.
And also they also extend it to inclusiveness, to contestability and accountability and to global responsibility.
And we will come to that, um, as we go along further in our story here.
Now, um, so what is the problem here?
Well, truly, it is a pick and mix. Um, there is a problem.
And we've seen that this before.
As a team of unchecked power, Big Tech has been establishing power and making money from data as asset for over 20 years.
So they have a very strong to track to be here.
And of course, they have a lot of political influence, as you saw earlier with the with the big tech labour and lobbying.
There's also this, uh, big problem of equity that these processes of and practices of data extraction
are so mundane and pervasive and hidden that we don't even understand these.
And I'm sure if I ask you in the room, do you read terms and conditions?
People will shake their head. Do you accept cookies, people?
But not because it is about convenience, about clicking things away?
Um, and of course we don't even know how many how much data we shared, whether it's like this topic type, the stuff, uh,
you know, even our, you know, the stuff we say, our voice, our movements, GPS tracking, and we even have now neuro tech.
That's where it reads your brainwaves.
And it's other things too, like, you know, uh, there's also sensors and, you know, think about stuff like smart cities,
many things are doing without permission because they're just in bins or in roads, sensors, etc.
And when you think about the more ambient technology becomes, the more invisible it becomes,
because it is so seamless and frictionless and integrated in our lives that we don't see it anymore.
And this also includes your AI technologies, new AI agents.
Um, of course, there's a problem of access. You know, that maybe we have access to all these technologies, but not everybody doors.
Some people don't have phones. They don't have internet, they don't have literacy.
They don't have to live in low and middle income countries. It's totally different.
And there is this inequality and institutional and structural discrimination.
And of course, resources as we know are not distributed fairly in the world.
And the most marginalised are by default most more disadvantaged.
Uh, and they will be more disadvantaged even further by AI.
So there's also this problem of identity.
And you might want to remember my bot here. Um, so every time I would have said to you, if you picture a robot or I as a person,
you would have given me probably that description of the male white robot, right?
Because this is, you know, there are so many biases naturally inbuilt in the AI system,
whether it's like gender biases, there might be racial biases, political language biases.
Because many of these technologies come from the West.
Um, there's a fundamental lack of representation that is harmful categorisations of self from communities.
Um, there is an the ratio of identity, and it is not inclusive by design.
And often it is starting literally from the technology design all the way down to the implementation.
Um, and of course there is a problem with participation too, that, you know,
there's many dehumanising and oppressive features, like even when you think about other AI technologies, for example,
like, um, you know, in hiring or in H or many bots nowadays, scan your CV,
there's many you don't even get to be selected because the AI has already rejected your application from the get go.
There's also lots of choice lessness and dark patterns, as I said, for there's not always a choice there that algorithmic decisions are being made.
Even if you don't participate actively in AI because they are running around in the background,
you know, because there's so much data around you and of you at any moment in time,
because it is coming from different data sources, but also over time, because we are online for such a long amount of time now in our lives,
from being born or from even before your being born, even, you know, your mother had, you know,
if you a mother takes now a pregnancy test or has a period tracking up,
then the system knows you're pregnant before your parents do or before your doctor knows.
So again, it is. It's all such a long amount of time that the companies have been able to to build themselves up.
Um, um, you know, a vision of you, um, again, and there's a lot of choice listeners too, because, like you don't you cannot always opt out of things.
And there's a lot of dark patterns, too, and many papers have been written about that.
And you probably know that two that you've heard recently about the dark patterns in social media and the addictive features.
We are now already at the point where we have AI, empires, and tech companies owned these AI empires.
So again, the AI is just an extension of the powers that they already have.
It supplements that ecosystem. So it's very, very difficult as an individual or it's just me or you to challenge the status quo of power,
because as I said, it is so pervasive and mundane that we often don't see it.
And because we feel as a very small person in a very big ecosystem that we don't even have any powers over.
So fair participation isn't always there. And then, of course, the knowledge too.
I mean, the AI systems were implemented with them move fast and break things kind of approach.
Um, even if there are times have killed people and I'm sure you have heard that in teenagers in America,
but also in Europe, have used these systems to vent their mental health issues.
And we're kind of almost nurture towards suicide, which did happen at the end.
So, you know, it is quite serious.
And of course, there's black boxes and algorithms as trade secrets, secrets.
And big tech is unwilling to share knowledge and to foster this intercultural learning
because they want to protect their business and revenue models and their trade secrets,
of course. Now there's also grim realities about data justice here.
We know that Pandora's box cannot be closed.
I cannot be invented at this point. Right?
Um, there's also an AI arms race.
So, for example, the US has 40 models, India has 18.
Um, I think 15 or 17 models. Uh, China has 25 or 27 and U.S. four models, Foundation models.
So it is a two AI arms race at the moment.
But because of that arms race, they have chosen to gloss over the risks and harms and issues that are kind of clearly emerging.
Um, and there's also a next level of surveillance and data capitalism.
So before when you think about it, but before when you search something, you put a search term in or you asked your search engine something.
But now we have a lot more interaction with AI, which means that, you know, we we have we have a back and forward conversation.
So it's not just there's a lot more data coming from you.
And also now when you think about it's a genetic AI or uh, AI agents, they have not just access to one application on your computer, but to many.
Um, so again, there is a more high level surveillance opportunity there to look at absolutely
everything that you have on your desktop or on your computer or on entire hospitals,
computers, let's say. And yet there's no such thing as a fair value in return.
There's also, of course, a lack of transparency and accountability.
But yet you have these AI empires now, which huge amounts of money.
And there's also a lack and representational justice.
Like there is such a growing evidence of harms, misuse databases, data security and privacy issues.
Um, so that quest of obtaining fairness and dignity and autonomy is getting harder to do.
And even stuff like, you see, as I said, all the AI tools have crept into your tool box.
So whether or not you like them, they're just there now.
And Google has announced yesterday you probably saw today their AI that that their normal Google search will be all a lot more AI infused.
So you can't even do a normal Google search anymore. Of course, there's huge, um, intellectual property issues.
So think about when I hoovered in the learning and training data sets over all the books, all the paintings,
all the artists stuff, all the poems, and hoovered all this up without any any regard to intellectual property issues.
So the people like artists and writers and also newspapers and etc., academics, books, you know, it was completely cast aside.
There was no fairness, autonomy there.
And of course, there is this pervasive presence of entrepreneur capitalism tool, but a lack of Covid with a lack of global regulation.
So there's of course more problems here. There is flaws in restorative justice.
But these these are the processes and mechanisms that you need for reconciliation and repair.
Well, I mean, we have loads of high level ethical concepts and guidance here.
But to be honest, the big tech companies, they it's meaningless and toothless and isolated and useless if it isn't regulated properly.
Um, so ethical guidance is great, but it is not really binding.
Of course you can have also individual. Like, you know, you can, for example, report about output abuses or wrong doings of to the I directly.
You've probably seen these that you can answer back saying that's not correct, or you can give thumbs up and thumbs down.
Um, or you can launch a lawsuit. But this is complicated in its own way.
To whom exactly? To the tech company, to the developers, to the I.
Who do you launch a lawsuit against? And what's an AI crime anyway?
So because AI is fairly new, these are all these questions that people just don't have the answers for.
And that's why we had put the robot open jail, so to speak. There's this AI incident database that I just want to show you to quickly.
So there is this, um, I incident database here, this global one where you can report,
uh, I incidents on an ongoing basis to, to map them and tracked them.
Uh, there's also, um, for example, organisations and researchers.
So I've talked only last week to the Amsterdam Institute for Global Health and Development and the lovely Gloria,
uh, umbrella and her, her team and a supervisor.
So they have, for example, developed here an AI maturity, AI governments and maturity ranking website,
where they looked at a world map here, where they looked at the maturity levels.
Here was frameworks and what jurisdictions and what levels they're at, you know,
and you can see here many countries do have some regulation, but many of them also don't or it's incomplete.
They have also developed here um, uh, a matrix here, a global health AI governance matrix here,
where they looked at different tiers and how binding they are.
And when you go, it's quite interesting here because when you go a bit further down here,
you can see here there's hardly any binding international treaties or supernatural things.
There's locally binding laws, but not really anything on a global level that is binding or supernatural.
Then this sort of research is really, really important because this gives us an an idea of the state of play where we are at at the moment.
So there is jurisdictional regulation, but nothing supernatural or nothing global.
And that's a problem because what big tech or tech companies do is they look for pockets of opportunities,
of course, uh, because that is basically their business model and their market.
Um, and there's also more obviously grim realities here that way, because.
You know what needs to be changed in order for fairness and and human flourishing to happen in theory.
And look again at all the promises of AI that we see.
There is clear benefits, including the potential to change people's dignity and well-being,
and also to make to enhance public life and public health, and to bring everybody on an even keel when it comes to education.
But, you know, there is so many harms at the same time and so many injustices that at the moment it is an ethical minefield.
So that flourishing is not not facilitated by the big tech companies.
And regulation, as we could see, is still inconsistent or lagging behind.
So what solutions are there? Of course, transformative technologies do need transformative solutions.
So for example, um, peace, emancipation, eliminating the costs of injustice.
Um, you know, what we need is what I showed you the website earlier.
We need something legally binding and global agreements.
If we can do intellectual property regulations like the WTO, the World Trade Organisation has it.
Why can't we have legally binding regulations when it comes to AI and protecting people from injustices like that?
And also, we have to remember that modern conflicts are a push and pull between, uh, status and power.
So I think the state can learn, for example, from what happens today when it comes to stuff like,
you know, the social media errors that they have made. The state can also learn, for example, where to apply political will better.
And citizens also need to learn from conflict, for example by engaging more in social movements.
And because we are often so busy on our own phone, we forget that there is, you know, we should bind together and be more solidarity.
Stick with others. Uh, in terms of human rights approaches.
This panel principles, there should be glass boxes, not black boxes.
We should enforce active participation. We should allow big tech in in lobbies for regulation.
And for example, when you remember and I hope you do remember, but eventually, over the years, uh,
big tobacco like the the companies that used to do the cigarettes,
they got kicked off the the political decision making table, um, because of the harms caused.
And why can't Big Tech be kicked off the lobby when it comes to regulation making?
And also states need to question their, uh,
entrepreneurial state approach and that innovation and commerce friendly stands at all costs and I mean at all costs.
I think it's important to facilitate innovation, commerce, but not at all costs.
And, of course, you know, when it comes to empowering agents and people,
it's like looking at the the lived experience, advocacy work, citizens, juries or other AI reporting.
It needs to be set up at which to challenge power differentials here.
And. The state need all states ont.
EU needs to listen much more to what people have to say and not just the, you know, the tech companies now.
And of course then more AI. And we know that because in recent years like it has scaled significantly.
And of course there might be also what about real data justice issues.
And as we know, there's more regulations coming like the AI act has kicked in.
Now the Digital Services Act,
which is about looking at control and choice for consumers and platforms and decrease illegal harmful content and digital markets Act is about,
you know, identifying these exactly these gatekeeper platforms, the big tech platforms and holding them responsible.
And there's also such thing as the Digital Fairness Act coming, or in the making or in the discussions at the moment, uh, which looks at,
you know, the marketing side of art, dark patterns, influencer marketing, addictive design and the unfair personalisation practices.
Um, you know, anything to exploit consumer vulnerabilities for marketing purposes.
And the big question is, are changes coming? So of course, again, we talk deeper here as we do.
And we looked at health care at the crossroads here. And yes, of course the promises are still going very strong.
But we look this time more into that. What I mean, what I mentioned before are these entanglements, uh,
between health care markets and regulation and who and what drives these entanglements,
and how can we also, again, more or more towards justice rather than data capitalism?
So the theoretical lens that we used here, and I don't want to go too much into the sociology side of it,
but that that these are basically socio material assemblages,
which means there are an entangled mess of networks that are human, non-human, material, immaterial,
and physical and textual things, and they all interact with each other on an ongoing basis.
So it's people, materials, actors, body spaces, but it's all about control.
And who directs these conversations and how and what's what becomes contested as a consequence.
So the problem here is that we know that China is so entangled in health care and markets and regulation,
and that the big tech companies are transgressing spheres, and the technology often acts as an overlay or underlay here.
What I mean by that is that technology is absolutely everywhere. But it's not just technology, it is power plays, and it is markets,
and it's also politics and political struggles are all imprinted into these technologies.
So these are also on the move and these are creating ripple effects.
Um, it's we call this an ontology of movement.
And there's two opposing concerns here. So one of them is data justice.
What I mentioned before and what came from Taylor,
which were the digital technologies and algorithms and data and health and medicine are already putting the most marginalised in society,
um, further disadvantage. But there's also this big issue of data capitalism, which is the modus operandi of the tech companies, where data,
including your personal health data, is transformed into assets and monetised with lock outs and all these inequalities.
Now, we've had all these before, but AI is adding so much new layers of actors and interests and powers and revenue here, huge amounts.
So the thing is, and I said that from the very start, digital technology is not just technology.
It AI is in flux, it is moving, and it can be directed and exploited by actors with powers and resources.
And when you look back again at the previous two acts that I presented as part of the story,
you can see already the key actors here and the powers and resources, um, it is persistently provisional.
It's invisible, unstable, and it's being constantly configured, can be configured, which is a good and bad thing because it's so hard to pin down.
What, because it's also provisional means it can also be reconfigured towards the good.
So these ripple effects, they affect, you know, health care delivery regulation,
tech markets, they destabilise other actors and move them in the process.
So even by talking, uh, to health care providers, they said, well,
we've lost basically control over essential infrastructures and we don't even know anymore.
We're losing all this knowledge because when it comes to essential updates of technologies or training and implementation,
we don't have this knowledge anymore. They do, the tech companies do.
And of course, you know, citizens often complain saying, I can't even participate in that because,
you know, I don't understand it anymore and I can opt out and I can say no.
So again, over the last 20 years, when you think about that push and pull of.
To two different directions. This movement,
it has been moved and often also with the state's help towards surveillance and data capitalism and data enclaves and data ecosystems and monopolies.
And the EU has a responsibility here to, uh, to to let that happen.
And of course, there has been attempts over the years to pull towards the data towards the side.
And I showed you some, some resources here, like the as I said, the mapping out to incidence databases or public policy papers.
You know, there's loads of efforts also from people like me and my colleagues, this academic activism.
Now the grim realities is that it is AI is the big tech companies are still encroaching public health even further.
And because they make so many promises,
it's easy to get swept away by these promises and to allow them to get more foothold in, in, in, in public health.
And of course, AI is an unstable actor. There is more mounting evidence of biases, hallucinations and a lack of value.
And, you know, I'm sure you've all heard of the dead internet of of the AI collapse because the the more AI generated content there is,
the more issues they are with the accuracy of their outputs.
Um, and of course there is issues with consent and privacy.
And of course, people feel very much undermined in terms of visibility and autonomy, and there's still a big lack of oversight and accountability.
People are often very choice too. And I know all this legislation in place, like or in the making, like the Digital Fairness Act,
that where the EU was at least aware of it, other geographies are not.
There is often a lack of options not turn ative steers,
TS and CS cookies consent opting out digital literacy issues where we are feeding varied choices as consumers.
Um, and of course, as I said,
it is a huge data capitalism boost because there so much more data in and through the AI and AI cements Big Tech's powers even more.
There has been a lovely book written by Ivan on ABC by Bruno Mucha, who do talks about world builders,
and his argument is that, uh, the world is built anymore by states.
Uh, it is built by big tech companies who have more power and more money than individual states.
And the regulation, as yet is yet not there to rebalance these power symmetries significantly.
So what are solutions here? Of course, we need to empower civic society more and have more literacy skills building and core creation,
less surveillance, less of a like I can't even say less automation because we are definitely getting more automated,
but at least to facilitate that understanding and have also clear and options to opt out,
we also need to move towards dynamic, more dynamic regulation and more precaution of regulation.
Sometimes that works well.
For example, we have a new digital omnibus, which is good, and that I will talk about this a little bit more in a minute, but.
You've probably saw the recent notify fire ups that.
You know that came out which was very harmful obviously.
But in the the new omnibus they have made that immediately, um, uh, illegal to do this, uh, notify our UPS or these new eyes, which is really good.
So that dynamic element is good, but we still always go much slower than what what the tech companies can release.
And also, I think we need to decide what is off the menu when it comes to AI and health care.
For example, what about AI generated viruses?
What about palliative care? What about assisted suicide?
What about full automation of care? Where do we set?
Where do we have precautionary regulation? Where we say we do not allow dogs the same way?
We don't allow, for example, um, you know, human embryo cloning or other things, right?
Or, you know, um, some experimentation on humans, etc.
So there has to be really clear guidelines about what's off the menu.
We also need to and I've said that before and all the other solutions we need to push for public health, uh, public value accountability.
I should not disproportionately provide a big tech.
Um, so a contemplative pause at this point.
Where are we now? Um, so I, I put a picture up there.
I was almost upset that it wasn't good news first and then bad news because I like talking about bad news.
But I'm actually glad it's this way, because I think I want to focus on the good news, too.
So we know. And the bad news site. My favourite, of course.
Um, there is a continuous and aggressive push by Big Tech,
and we see that even now the investments that are made, there's also still very worrying sphere transgressions.
And I'm sure you've all heard about that recent case of Palantir getting more access to own anonymized NHS patient data.
This is definitely not what you want, especially since Palantir's very clear about marketing data.
We are also now in a point where we have a lot of shadow health care systems.
And what I mean by that is like, we have a lot of, uh, we don't just have a public healthcare system anymore.
We have people hacking their mental health on ChatGPT or character I or Google Gemini.
We have a shadow health care system that's completely unregulated. We have a lot of job losses and environmental costs of AI.
And I want to talk about health care here in a broader terms as well, because health care is not just what happens in the doctors.
If you lose your job. Your job is your health. It's got to be effective. Think about the environmental cost of AI.
This is all going to affect our health and wealth in the long term too.
We have AGI scare us, and I hope you've all heard of notebook, which was, uh, this social media platform for AI agents.
And the first one thing why it became famous is because so many people are.
Well, uh, but but why don't we overwrite and kill humans?
So this is, of course, very worrying.
We have also the digital omnibus proposal and what that is, it's it's a move by the EU to, I don't want to say essentially deregulate,
but it is pretty much about supporting businesses, reducing compliance and costs and supporting innovation.
Again, all the stuff we've heard before and also kicking the timelines now to August 2028.
And of course we have stuff like Trump politics, which is all very bad news, uh, because it doesn't have full global regulation.
But now here's the good news. The critical voices are getting louder and you can see more and more.
And I hope you all are getting the critical voices, too. And it's not just me sitting in some kind of filter bubble,
which is all very possible to with my searches and stuff that I do, but the critical voices I feel are getting louder.
There's also good regulation in the EU kicking in, but don't forget EU is the gold standard here.
Um, not every place in the world is the same.
We have also moved towards theorising the digital terms of health, and I'm also writing a paper at the moment with colleagues, uh,
a global network of colleagues for The Lancet, where they're looking at the digital as a structural condition that shapes chiefs health.
And we are looking at all the things that I've discussed here, the business models, the political side of the political norms,
the infrastructures, the personal practices of us, important determinants of health.
There's also big litigations. Now, you saw the landmark ruling about Meta and Google, about the social media, um, you know, the harms it has done.
And these landmark rulings are really important now because whereas public health
or technology somehow should not be based on litigation based approaches,
but it is at least a start to show that big tech companies are doing the harms.
There's also local solutions. You might not have seen it, but Palantir got kicked off, um, in the UK also for, uh, you know, tech.
Analysis for refugee accommodation, and they source their own, um, open source software system for that.
They keep Palantir of that because they are aware of these sphere transgressions.
There's also national solutions. Now, we saw Australia doing the social media ban and given the harm,
and starts to take the eye of many of the digital technologies doing for certain cohorts in particular, like younger people.
It's really important to also extend this maybe to AI.
And as you could see here, from what I showed you from the different in my different from the different acts in the story,
there is coordinated efforts there for global regulation and digital governance of health.
Now, finally, a last slide. Concluding remarks.
So what do we need? I tried to, in the story, try to the main message home that we need public value in digital health,
not just buzzwords like innovation and markets and all this.
We need public value and public health.
But what we have is dependencies on big taken power asymmetries and we that are very difficult to dislodge at this point in time.
But they are not ever irreversible. And it is also adding new complications and layers.
Here. What needs to happen is coordinated and systematic, practical and dynamic contestation of power, profits and politics and health care.
We all need to fight and regulate for fairness, justice, equity and democracy.
And of course, I can't leave without giving an inspirational quote here.
So Shoshana Zuboff has said, if the digital future is to be our home, then be it is or step or we that who need to make it.
So we will need to decide who decides. And this is the fight for our for our future and future.
Um, even though we are often faced with choice lessness and with a big daunting task, we have to remember that.
All angles, whether it's student scorn, you know, get an education and going into health,
into the marketplace with a different insight or citizens organising themselves for,
you know, advocacy and citizen movements or the state regulating more dynamically and more precautionary or tech companies taking on ethical guidance.
It is it is all possible, but it is a fight.
We have to remember that. So I want to I want to leave it here at that at the talk and open the floor up for questions.
Of course.