Shaun O 'Noe, thank you so much for joining me on the road to accountable AI.
Thank you for having me.
So what is responsible AI or AI ethics?
Pick the term that makes the most sense to you.
What does that mean to you?
So AI ethics, or for us, it's also how we call it, digital ethics under data ethics
and AI ethics for me is the responsible handling of algorithms and AI.
So how can we responsibly use and deploy this technology in the world so that key
ethical principles are safeguarded so that we create trust within our solutions?
That may be trust from partners, trust from...
even our own employees plus from customers, that we offer solutions that
they trust us to buy solutions or work with us.
That's the whole idea behind this.
And to do this, actually, even besides regulation or an absence of regulation or
developer regulation, we want to do the right thing.
And how do we do this?
By behaving ethically in our AI solutions.
That's the not so short answer to responsible AI.
Before you had a digital ethics program, as I understand it, you had a bioethics
advisory panel.
How did that earlier work inform what you're doing on digital ethics and AI?
So yeah, actually, so with the company, the bioethics work started already in
2011, which is some time ago.
And this arose from, let's say, bioethics challenges for us.
We have a strong health care pillar.
One of the pillars is health care.
And it actually has quite some sort of like obviously, we're going to try the
programs, but also our fertility research, one of the leaders in female fertility.
Actually, this brought quite some questions around sort of like research on
embryos, embryonic stem cells with it.
And how do we deal about this?
And sort of at that point, 2011, there was quite a big debate around this.
And so we decided to get external help in the sort of like the Merck Bioethics
Advisory Panel was then called.
And they really helped us to come up with principles and structures and how to
address ethical questions.
And then when five years ago, we first ventured really into digital health with
the collaboration that we have with Palantir called Syntropy.
We really looked into sort of like, okay,
How do we do this ethically?
Because as I mentioned, the trust element is really critical for us.
And then we looked at sort of like, OK, what do we have we learned from the
bioethics?
How do we operationalize it?
How we put this forward?
And that's sort of like how we borrowed from the bioethical principles and
frameworks and everything that we had done, and also these advisory panel work
into digital ethics and then also AI ethics.
And that's something that I guess why pharmaceutical companies have a little bit
of a...
Head start here because we have been dealing with these ethical questions all
along and we are a highly regulated sector at the same way.
So that's not, we're not the first, not the first time we encountered this.
So we can borrow some ideas we've had before, other than maybe let's say big
tech who have not been in this situation before that there's like also like a very
critical public having a look at you.
So like, how do you behave doing this rightly and really being careful that you
do the right thing in your innovations.
What's different about AI ethics from bioethics?
Why not take the existing advisory board that you had and just extend it into this
area?
Yeah, so we realized we needed very specific expertise.
Actually, we went with our first digital ethics questions to this existing advisory
panel and said, OK, can you advise us how to do this?
And they said, yes.
We can give you a few hints, but this is out of our realm.
This is not something we can help you with.
We can give you some basic principles.
But first of all, you actually, for yourself, you need to set yourself, let's
say, ethical standards or ethical principles that then you can derive your
tools from.
Because we always come from the
bio and medical side, and we have no experience at all in the digital sea.
I mean, we have some that have sort of like big data, medical, big data
experience, but the skill set is very different.
And that's also why our digital ethics advisory panel is, other than the
bioethics one, is consisted also out of industry experts.
So they're all external, but they're, let's say, a mixture of academic and
industry experts, while the bioethics purely academic, because...
there's hardly enough, let's say, academic insight right now into all the AI.
And the innovation is happening within the company.
So it's very difficult to get the right expertise there.
So we write more on this.
And then, yes, I said also, for example, our legal experts, they really come now
from the FDA regulatory medical devices, AI side, to really help us with this.
And also, the factor of fairness became much more important in this regard.
So we have an anthropologist, actually, from Johns Hopkins.
pockets, Khadija Ferryman joining, because he has a special eye on neglected
populations and how do we really take care that there's not a bias in our systems and
how can we take care that these people are rightfully addressed also.
So that's some of the points.
And then also the principles are not the same.
I mean, they're similar, but then there's different emphasis.
And also one thing is that...
Bioethicists are not too keen on going to principle frameworks and these kind of
this and the digital ethics are much more open to this because they see at a large
scale, how can we do this?
Because within bioethics, it's usually a few larger topics that we discuss.
And within digital ethics, it's a huge amount of topics that we go into on a
regular basis.
I mean, we have more than a thousand analytics use cases, for example, in one
unit alone.
And how can we screen this at a high throughput?
And this will not work without.
assessments and frameworks and these likes, and will not just by like be a one
-on -one discussion with an SSS.
But I will go into this in more detail probably later on.
absolutely.
No, it's really interesting.
How do you find people who have the right kind of expertise?
It probably was easier when you were doing it, getting started a few years ago, than
now where every company now feels like they need to bring in people that have AI
ethics expertise.
But how do you find people that have the right expertise, but also are a good
enough fit for your organization?
Yeah, so I mean, sort of like we had the, we had started a little bit because we
went through obviously our existing panel where we have to, okay, do you know
somebody and something's happening?
But then five years ago, this was a little bit more exotic.
Nowadays, it's also more difficult because everybody is all of a sudden an AI ethics
expert.
So you're really difficult to find this.
And so when we try to go through this, despite going through our...
let's say, our esteemed experts that we already have.
We go through our business that often has already the context to sort of like the
right people in our digital unit, EMV Digital, that's located in Cambridge,
Massachusetts.
They have really good contacts already, let's say, to the academic sphere due to
this innovation part.
And so we got somebody along as John Alamka, who works for Mayo Clinic, but was
like a CIO on Harvard for many years, to give us insights.
And then from there, we went bit by bit forward looking.
Also,
you
do we need?
So we scan a little bit the academics here, looking into, OK, what kind of
academics do we need?
Is there already something?
And what kind of expertise do they have?
Have they published in this field?
This is obviously very important that we go by publications and see, OK, speakers,
what's the background?
And then usually we invite them for a few test sessions and see, does that work for
panel meetings?
And through them, we then get exposed to other experts again, also to more industry
experts.
So it's the.
a bit of growing exercise.
And I think we started with three experts and then one change, then we were only
two.
And then by now, I think we're seven experts.
And what's important to see that we also have a linker between these two panels.
So there's one bioethicist, Jeremy Sugarman, he's also from Johns Hopkins.
He's a bioethic professor.
He has done some work for the Obama administration.
So he's very well renowned in the US and he's, let's say, a generalist in bioethics
and he's also sitting on the digital ethics panel.
So there's a link between them and they can also learn from each other.
Also share best practices.
You talked about the distinction between bioethics and digital ethics, although you
mentioned there are some connections.
Is AI ethics just a subset of digital ethics?
for us, it's a subset of digital ethics.
Because when we started to develop, let's say, ways to ethically handle data,
because our first business ideas, this started then with, as mentioned, product
centropy.
This was about health data and sharing health data and how can we analyze data in
larger data banks.
And we decided, OK, we need something for ethical handling of data.
This was 2018.
Then AI ethics had popped up.
but it was not really present.
But we said from the beginning, OK, we want to do something that encompasses AI
ethics and data ethics at the same time, because for AI, you will always need good
sets of data and sets that are handled the data the right way for all the training,
especially now for generative AI.
And so we decided to do this under the term digital ethics as one.
Many might just be also responsible AI, maybe for some the more, especially in the
IT field, the more.
understandable term for them, but for us, this is sort of like digital ethics
encompasses this all.
So that we really also make sure for data, this is something as a German company or
German headquartered company, this is also something where there's a high, at least
within the country, there's a high view on how do we handle data or within the EU in
general, the GDPR, you have heard about this probably.
So that's why also we said, okay, we also need a strong footprint on data ethics.
And so we encompassed this as two together.
Other than the heightened sensitivity and the regulation, as you mentioned, what's
different about thinking through these issues in a healthcare and life sciences
company?
mean, it's obviously been a heavily regulated environment already, which makes
it more difficult a little bit to operate.
And then it's also the most sensitive area you can imagine.
So the trust element is even more critical for us than for other providers, I'd say.
I mean, maybe it goes similarly for banks and the sensitivity or insurance.
But then health is usually the most personal information people have.
And we see this even in the US, that there's a higher, let's say, alertness
towards health data than maybe other data being used.
And so this was really for us the strong push.
OK, we need to get this forward.
And also, we're discussing with other companies in the health field.
And actually, the most reach out from other companies I get is from actually in
the health care field, because they see they have to be extra alert and extra
early prepared.
this because this is something that might help them otherwise in the long run.
And also if you look at the UAI Act applications within healthcare, we'll most
likely get in the high risk group and always will need more detailed analysis
and oversight.
Cutting the other way though, even within your corporate group, there are many
different businesses, different units, different products, a range of different
kinds of issues.
So how can you, as a large company, you develop one set of guidelines or one
process around digital ethics that spans all of them?
so, I we have, I mean, I lead a small team of experts.
I mean, that's we're the parts directly to the board of directors and we have, we are
sort of like owning the topic over all our businesses.
So the healthcare, the life science and our electronics business and also are what
we call the enabling functions.
So all our, let's say,
finance and HR for everything.
So, and we actually take care of questions for all of them, but we, and we have one.
of digital ethics for the company that defines our principles.
And it's not do's and don'ts, it's specifically principles.
A set of in total 20 grouped under five major principles.
And from these, we can develop guidelines and do's and don'ts for certain
applications, certain areas, certain fields.
So that was very important for us that we do something for the entire group.
But then obviously, these sectors have very different needs.
I mean, for our life science sector, for example, for the analytics unit, I already
mentioned it before, we have one that's called the
center of excellence.
We have a platform here on Foundry.
It's a technology platform.
And here people can develop analytics use cases from ideation to proof of value to
industrialization.
And they, for example, asked us, can you quickly build us a filter so we can
identify those cases that have ethical challenges?
And within life science, which is sort of like our business that sells, let's say,
any kind of lab materials, but also tools like genome editing tools or
mRNAs and lipids, so also offer services, their data might not be as sensitive as
they have.
So we don't think that there's so many use cases that are critical.
So we just wanted to drive with a filter to find out the views that are critical.
And so we based on our critical ethics, we develop ethical markers that we then put
into questions that the use case owners can actually have to answer as part of the
process in their different stages.
And we can see, OK, is that ethical risk?
yes or no, and then we can, if there's an ethical risk or the score is high, then we
go and analyze it.
So this is, for example, one way.
But then, for example, in HR, which is right now one of my closest customers, I'd
say, with all the Gen .AI solutions that come along, or AI solutions in general,
whether we insource them or if we develop them ourselves.
For these, instead of like, these are all.
Yeah, these are not so many, but then they all have very individual needs.
So we're currently developing a different framework, a little bit based on what
we've done before, but much more concrete for HR, which comes with certain
questions.
And then we also developed something similar, again, for healthcare, really
tailored to them.
So that's really, yeah, the code is a pretty good for all, but then it's like,
yeah, how do we market our different customers?
So that's really running a small consultancy here rather than, yeah.
doing a one -size -fits -all solution, or doing a do and don't for everything in one
document.
So for those who are really keen on this, they can read about this.
There's actually a publication in AI Society about this, how we put this.
They have published this with a partner from the University of Fittenherdeck in
Germany, Sarah Becker.
She has been on our advisory panel also before as one of the leading experts.
And she's also
we really went and looked into being a European company.
We looked into, okay, what kind of principles and frameworks have been
published in the EU in the last five years back then.
So this was between 2015 and 2020 when we started this.
And what has been published towards data ethics and the AI ethics.
And I think we had like, and may they be from governments, may they be from
corporations, may they be from...
NGOs or think tanks, and we found a total of 62 frameworks.
And this was back then, I guess, now with several hundred.
And we tried to identify principles and all of them to have a most holistic view.
And often they were called differently, but we really tried to look into all
details and to see, OK, is there sort of like one word behind this that we can
frame with us?
Because some of them are really marketing speech or semantic differences, but we
really could narrow them down to the different details.
And then we...
After that, we actually grouped them into the five major principles that you see now
when you access the code on our homepage.
You can see five different principles.
And actually four of them are identical with the bioethical overall principles
that exist.
And one of them actually is new, is transparency, which is because this is
actually more or less the key principles when we go to digital ethics.
This was a larger endeavor that took us more over a year.
What we also did after is actually we checked whether, okay, how does this work
within the US?
principles that have been formulated there, also within Asia and China, for
example, especially.
And we see that it actually matches pretty well because it really just defines ethic
principles in a philosophical manner.
So it's really, if you would read this as normal employee, it's rather complex and
we have to easing it down for people to explain, but it really helps us to
identify challenges.
And we saw that, for example, we're working well in the US, but also in China.
And maybe if we look at the Asian...
geographies, it might more be that the traditional principle of harmony coming
along, that the autonomy is not as important and there's harmony coming up a
little bit more.
Societal harmony is a higher value, but beyond that, we have something that is
actually quite checked globally also.
Why is transparency so distinctively important for digital ethics?
So we think about this for us, the key element or why we do this is really to
create trust in our solutions that we offer trustful solutions.
And I think with this high opacity that AI has or the whole big data topic has behind
it, people, it's really extremely difficult to grasp what is happening and
everybody knows they're putting the data somewhere on the net and something's
happening with it, but nobody knows what's really happening.
And you always imagine these kinds of things that are happening with your data
or there might be...
used in a certain way.
So this is for us that we say, okay, we want to be transparent, we really want to
share sort of like what we do and how we do it and be as open as possible about our
sort of like maybe AI solutions and what's happening within them so that people can
actually trust in these solutions and lose the fear and skepticism of these
solutions.
I mean, and this goes not only externally, but also internally with our employees
here at Merck KGA Darmstadt, Germany, because this is...
really something that, I mean, obviously we have the whole variety of society
working for us.
And also here there's quite a level of mistrust, especially when we look at blue
color workers, sort of like, okay, the CI may cause my job, but also our white color
workers are threatened by CHPT and think, okay, what does that mean and what kind of
solutions are we using?
And so this is something where we think, okay, we really want to educate here.
This is where this transparency even plays a very important,
role in -house.
So we really educate and be transparent with our own employees so that they
understand this.
One of the reasons that I was interested in speaking with you is that you have
published a couple of these papers, which we'll put in the show notes, talking about
your approach.
And I was particularly interested in one that talked about what you call principle
at risk analysis for operationalizing AI ethics.
Can you talk a little bit about what that means?
Yeah, so this comes a little bit.
So why did we actually put this code to digital ethics?
I mean, why do we need this?
And I couldn't we just do with the bioethicists that advised us because we do
not have like a code of bioethics for the company.
And this comes to the fact that there is not yet a fixed sort of like set of
principles for digital ethics out there.
And bioethics are very well established since 30 years or so, but for us, it's not
the case.
And so we realized, okay, we cannot just simply form the digital ethics panel, but
they need something to orient themselves on.
And these principles that we formulated in the code, they are as guidance for the
experts to take decisions, but also, obviously, as an overall guidance for the
company.
But also, and this is why it's really designed in this way, that we can build
analytical tools from it.
And this is the para, as we call it.
This tool, actually, we developed directly from the code, which is a way to analyze
the certain questions and see whether in a certain business model,
things are at risk or principles at risk.
And with this outcome of this analysis, we could say, OK, if we have a certain
question, for example, are we responsible how things are done in a certain way and a
certain technology?
And then we can, an expert, not everybody in the company, but our expert, my team
can do this analysis.
And then we find out, OK, five principles of our total 20 are at risk.
And then this for us is the discussion basis then with the panel.
I mean, that might not be a.
complete identification of all ethical risks, but it's really a good head start
because often at the beginning of discussion around an ethical, a certain
ideology, I think challenges, it's all over the place and you need to structure
it well.
And so this really helps us to go sort of like in the risks on certain principles
and how can we address them bit by bit.
And so this is also like a bit of a loop.
So a question comes to us from the business.
We sort of, if it's a complex question, we run this through this analysis and the
report then goes to the panel.
And then they can prepare themselves.
And then we sit down with a panel and discuss all of these risks and how can we
mitigate these risks, what has to be put in place.
And then this goes back to the business as a recommendation.
And then we see that a loop of reporting several times to see, OK, is there
actually a solution that's working?
So this is sort of like, how can we actually operationalize this?
And this is, let's say, for larger complex questions, how can we do this in a better
way?
I mean, if we would run the power on any kind of
of most questions, then I would need like 20 people in my office.
But for those couple of discussions, it's a focusing mechanism, it sounds like, for
the discussion that's an input to the advisory board.
And also really shows you the risk that might arise and something that you might
not just not have on your radar that might happen.
And then the output, as I understand it, of the advisory board, as you said, is a
recommendation.
What happens if the business says, we don't agree, or the business benefit of
this is too great, we're going forward anyway?
So this is always the problem.
There are two different approaches to how do you digital ethics.
I mean, there's the way of ethics -based auditing, which is more through
compliance.
So there has been a paper of Yako Mukanda on this with AstraZeneca.
I think this has been done.
They showed this value of audit every project.
And in our company, we run it a bit different.
This is also an experience with bioethics.
We are awareness -based.
So we do trainings, and people know that they exist, that they can address us.
And so we are sort of like, people are not forced to ask us, but sort of like they
come to us for questions.
And throughout the years, or with the bioethics, we have built a very good
reputation.
So people come to us because they need the help and they see this is actually helpful
for their business.
They could ignore us and they could also ignore the advice, but it's well
documented within the company and sort of ignore the advice and later on, then run
into problems.
Then this actually, I mean, obviously we'll heard.
very much the decision -takers, let's say, reputation with the company or with the
company.
So that's sort of like indirect pressure.
And it also goes to the way that we say, OK, for us, we're a family -owned
business, or let's say we're on the stock market, but 70 % is off -stock market and
family hands.
There's a higher sense of, let's say, generational thinking.
So we want to have a sustainable business in 15, 30 years for the next generation of
the family.
And so this whole of responsible business has been longer.
thing, and it has also been, it's on the responsibility is on everybody's hand.
So it's not just with the data scientists or AI developer that's on the all end, but
it's also with the middle manager, the high manager and the board of directors.
So it's really the entire chain.
And this is sort of like a certain culture we have established with the company.
And this helps that actually people come to us and they take these recommendations
seriously.
I mean, they ask us for certain questions.
And I must also say some recommendations, obviously, we are not able to implement
because just they may be not realistic.
But so we come back and we ask, OK, can we have a more pragmatic solution?
But do you think about this?
We approach it in a certain way.
But sort of like how you think it, it's not working because probably they do not
have that insights in our processes and business.
And then we really go iterative way to find a solution that's best fitting.
And I also have to say that our advisors are rather pragmatic.
I mean, so we're not going to.
We don't have ivory tower from an analyst that comes a certain way, but it's really
OK.
This is a pragmatic solution.
And obviously, we cannot aim for total perfection.
But let's say risk mitigation on a larger level, we do go a pragmatic way.
What has changed over the time that you've been doing this digital ethics and AI
ethics work?
Obviously there's now a lot more adoption of AI, there's generative AI.
How has that influenced the nature of the work you do or things that you've learned?
So actually, five years ago, I took over the team of digital ethics or bioethics,
actually.
I built this up before it was supposed to side job of the chief medical officer and
I said, okay, we need somebody skilled for this.
And I saw this sort of like coming up.
It was just me as a lone player back then and sort of pushed this little bit forward
with the company, but it was seen as a rather, let's say, esoteric topic or
something like data governance or data anthology that nobody wants to do.
It's something of a side business.
And I've really seen the, let's say, in the last year, I mean, the people that
come to me and ask me questions has been risen exponentially within the company.
I mean, this is Chagy B .T.
finally showed people things are happening and actually I need to address these
things and I want to have certain solutions and how can I have them?
How can I have them responsibly?
This obviously has also been a sort of like under the attention of the board.
And we now have a...
since a few years also a chief data and AI officer who's really taking care of this.
And he's actually the chair of the panel.
So he's a very close partner in crime with me.
He's really pushing this forward.
And we see a big momentum.
We have a big data culture team within the company now that's really pushing this
forward.
We do a lot of awareness.
And I mean, I can see that there's not only me pushing and standing in the
corner, but there's also a big pull effect from different businesses.
So I don't need to stand on everybody's door anymore and say, OK, I'm
I'm very sure you have some questions for me, but people are coming running to me
and ask questions.
And it's also externally.
I was just yesterday sitting on a panel also with somebody from Google Cloud
Service, actually, from Munich.
And she was also saying that all of a sudden since a year, this whole data
governance, data ethics thing has moved out of the shadow.
And she always says to people, this is unsexy in the beginning, and people need
to get their basics right.
And it's sort of like,
eating your veggies every day.
But this is something you have to do in order to be able to innovate fast.
And this is also what our chief AI, data AI officer who worked with automotive
sector before always says, I mean, he always comes to me and always say, so why
do we have brakes in the car?
So the brakes we have, so we can go safe, but fast.
So it's like, okay, this what we have enables us to innovate really rapidly.
But sort of like in a...
safe matter so we actually can hit the break if needed.
One of the issues with generative AI is these foundation models, which get
developed by big tech companies, OpenAI, Google, and so forth.
And then an organization like yours might be using that model and bringing in some
of your own data and building queries and specialized applications, but you're not
the ones who are controlling the original training of the model.
Have you had to confront that issue, or how would you think about that in terms of
your ethics work?
For example, so we actually use an insourced model within the company that we
have our own, we have our own GPT, my GPT that we have in the progression where we
can actually, let's say it's cut off so we can put in our own data or let's say
confidential data from the company.
And, but obviously there are questions about, okay, how do we do this?
And obviously the IP questions and the larger frame sort of like how it was been
trained, it's becoming much more prominent now.
So how do we, how do we deal with this?
But.
But one question that also came up in this whole thing that in our speak up line,
actually somebody asked us, so you see, I'm a compliance speaker.
We got the questions that, okay, there were those reports that the OpenAI had
workers in Kenya, actually, that sort of like were exposed to violent materials or
pornography materials and didn't get the right counseling.
And so they did this for like $60 a day and they were really suffering a lot.
And how can we...
what kind of thing is this?
And so we really looked into, okay, is this a technology we can use?
Has this been developed under human rights violations?
And obviously we did a survey, and we see it's not the case.
And also these kinds of programs have been halted.
And so OpenEye is no longer pursuing this, so that's why we can use this technology.
So this is some of the things that are in our radar.
And I think you can read about this, it was a time article, I think, some time ago
on this.
And it's something that we take seriously and survey.
But obviously, then we try to also when we in -source solutions, especially now
within HR is a big topic right now.
And we actually have developed also ways to assess the vendor of a product.
So in the final, sort of like when we have like two or three finalists in our vendor
selection processes, actually we come now.
for AI solutions with the certain kind of questions that we need to check also, not
only sort of like how does the company behave, what kind of principles they have,
but also how they address issues, how do they have like bodies, the digital ethics,
so they have sort of like a crisis model for this or certain things, just do they
take this topic as seriously as we do.
So that's also a big thing because most AI solutions, I believe we will insert, I
mean, we will use the few skilled people on this we have, maybe like 200 or so.
to develop solutions for research, clinical research, prediction tools, and
these kind of things.
We're not going to use our in -house experts to develop HR tools.
Obviously, these will be mostly in -source and maybe adapt.
And here we need to find a good way of sort of like, OK, how can we ensure that
this aligns with our principles and general vision for AI?
You mentioned earlier on the European AI Act, European Union AI Act, as a company
that was doing this digital and AI ethics work for a long time before that
regulation was adopted.
How do you anticipate that influencing and impacting your work?
So this was also a case where our work was suddenly highlighted and was seen as very
valuable.
A few weeks ago, the consultancy highlighted the survey of sort of like a
UAI readiness within the company.
And we saw that we have very strong foundation, thanks to all the work that my
team has been doing in the last five years.
So that's a good head start.
I mean, it's also good for us to see, OK, we're doing something this.
Because the whole thing was for.
as I said, how do we behave rightly in the absence of regulation?
So we had the sort of like future regulation probably upcoming already a
little bit in mind when we design certain things.
And so the influence on us is not going to be that strong.
I think it's a little bit more about building sort of like one of the things
that we need for now is for to identify high risk cases and really document them
in the right database.
And so I have a look at this.
So this is going to be a little bit of more.
building a broader AI governance that we do not have in that strict way right away,
because right now it's more decentralized.
So to really enable innovation.
So we will move to a little bit more centralized AI governance that also gives
like a consolidated mandatory review, I guess, for high risk cases.
So it was no longer voluntary, as in my case, but then there were probably like a
legal ethics and whatever other body review of certain projects and areas that
we identify really in response to this.
regulation.
So it may slow down a bit in things.
And let's say it's not a perfect regulation.
But I mean, I obviously have two hearts, one of them in the company, I want to be a
bit more free, but I'm also a citizen.
And sort of like I as a citizen, I really much appreciate that the European Union is
moving forward with this.
It's not an ideal framework, but I think it's still better than having no framework
at all.
And you will see how this plays out.
But I think it's also going to be a little bit of a role similar to the GDPR, which
we have pushed forward.
which is lived very differently in European countries.
So in Estonia, it's lived very differently and much more openly than in Germany.
So I assume it's similar going to be with the AI.
But I think it's a good thing moving forward with this.
And I think also other world regions will look at us pushing this forward and how
have we done this and what are some of the key elements.
And obviously, everybody wants access to this market.
So let's see how this plays out.
And also how the documentation requirements in the end really are.
For us as a larger company, obviously it's easier than maybe for like a hundred
people player.
Shona, thank you for a really enlightening and instructive conversation.
Thank you.
I mean, for all those who see this, I mean, don't hesitate to reach out to me
through LinkedIn.
Learn more about how our company is doing this.
And we're willing to share.
We actually do a lot of cross -company initiatives also to share best practices,
pushing things forward, educating, yeah, really policymakers, also exchange with
academics that do case studies on us.
So feel free to reach out to me or my team members and discuss.
Wonderful.