Hey, to everyone joining from around the world, I see we have people from Japan, from California, from Switzerland,
lots of time zones here, Seattle and even from West Berkshire in the U.K. and, of course, from Oxford.
Good day to all of you all. I hope you keeping well and safe in these difficult times.
No doubt you've heard the good news from Rocksmith that we have a vaccine that's going to be affordable and globally distributed.
So this is a significant day for us in Oxford and made even more significant by the fact that we have a
presentation from Gene UNEF and Gina is going to give a talk that she was originally scheduled to give in March.
And then circumstances intervened and then we had covered 19.
But she's here and we Apsley delighted that she could join us.
I'm in Golden. I was the founding director of the Oxford Martin School.
And since 2006, I have been directing the programme on technological and economic change at the school.
Gina is the professor of technology and society at the Oxford Internet Institute, was actually with founding groups and the Oxford Martin School.
She's also a professor in the Department of Sociology at Oxford.
She works on the future of work and data rich environments.
She's published three books and over a dozen research articles in many of the magazines and journals that we'll be reading.
She's a pioneer in creating the area of human centred data science and leading a new project on the
organisational challenges that companies face using artificial intelligence for decision making.
She's had fellowships at the British Academy, the Institute for Advanced Study at Princeton and elsewhere.
And she's an adviser to many companies, organisations and others on this agenda.
She's also an advisor on Álex, the Women's Forum for Economy and Society.
Gina. Delighted to have you. I'm here to talk about data work. This is the new project that my team and I have been working on.
And it's going to I'm going to talk about three distinct projects.
Now, the stakes at hand are that we are looking at a transformation of the basic everyday infrastructure of how we do organising.
I like this word infrastructure because it reminds us from science and technology studies that once technology is going to put into place,
they're often invisible or everyday.
We don't so much think about our roads and the structure when we're trying to get it from point A to B in the west.
In the north. The Northern Hemisphere, we don't think necessarily, and fortunately,
we don't have to think about things like our water infrastructure and we think about our connectivity infrastructure because it's helped us allow.
Allowed us to do. Allowed us to do things that we haven't we wouldn't be able to do otherwise during this pandemic.
That said,
infrastructure helps us think about how small technical decisions being made today end up having enormous everyday implications for all of us.
And that's what I'd like to talk about. There's three myths of everyday A.I. that we need to address.
The first is that A.I. is not. It's not a province anymore of large technology companies.
That is that we are seeing the integration of new modes of organising new kinds of technologies into everyday decisions,
from legal decisions to marketing decisions to civics decisions, decisions about who gets credit or who gets put in jail.
And in this way, what the conversation so far about ethics has targeted those who make A.I.,
those who those who the large technology firms who are driving the innovation.
And so I would say that the context for us to think about in terms of data work is not that we're targeting large tech firms,
but rather the places where many people on this call would work.
Smaller firms, firms outside of technology firms, organisations of all sorts that are starting to integrate, purchase,
acquire new kinds of data, processing, new kinds of technologies that make decisions and automate processes.
The second kind of key challenge for us and thinking about data work is that we've talked about artificial intelligence as as a suite of technologies,
as a smart tech, but fuelling and driving the expansion of artificial intelligence into more parts of our everyday lives.
It's really this question of A.I. as a data so that, you know, if the first is A.I. is everywhere, it's in smaller firms.
We need to we need to change our conversation.
The second kind of key point that we need to think about is that we need to think of what is happening, not as just a technological phenomenon,
although the processing power and cloud computing are allowing and enabling and driving new kinds of new ways of generating data.
It is about a data question, right. And how data is being used in the Web.
And that's going to be one of the key areas to look at tonight. The third is the idea that AI systems are automatic.
Now, this seems almost like an oxymoron, but bear with me for a moment.
When we talk about integrating artificial intelligence systems into everyday life, we might think of them as driving a cent of automation.
And that's certainly the push behind many of these systems. But what is fuelling much of the expansion is a whole host of human work.
And it's really in the intersection of these three points I of the every day and many different kinds of organisations.
I as a as a data innovation and not just a tech innovation.
And the notion that A.I. systems involve enormous amounts of human effort.
That's really where we need to be thinking in terms of data work. And that's where our talk is tonight.
So with my team. Maggie McGrath and Diana Prakash earlier this spring.
Earlier this year, we were rereleased report called A at Work.
And what we did was actually fairly simple. We surveyed global newspapers for accounts of how artificial intelligence was making the news.
And first thing we did was set aside basically everything that looked like a company press release.
Right. So those cases where someone was advertising how well their product worked,
or we know from existing research that much of the journalism that is on artificial
intelligence is coming from the sources of technology companies that make it.
So we set those aside and instead we focussed on how news stories were covering, where I was working and organisations.
And we came up with three common challenges that we saw in the about a thousand articles that met our criteria.
And this over the course of the year. And those challenges were challenges of transparency, integration and reliance.
That is what we began to see.
If we if we take and and collate the stories of where A.I. is not quite living up yet to its potential as a tech, as a transformative technology.
We see the stories, share these news stories, share three key challenges now in transparency.
We see this lack of transparency between companies and customers about what I can do, how long it takes,
how much work is involved in making it, and sort of closing the loop as as as they say.
So companies on the one hand tout eyes this automating solution rather than being open about the amount
of money or time or effort needed to produce and sustain systems that actually work in practise.
And this led to a whole host of problems that that that arose in our reports,
because we could see that that the people, the clients behind these systems felt deceived,
they felt deceived by privacy, promises, etc. And companies felt that there was a whole host of unglamorous ways on the on the one hand,
they had signed up for know, sparkling new systems. On the other hand, they were getting what looked like lots of back and data cleaning.
Next, there was a huge gap in integration and we caught we use this to think about how what was the gap between
the conditions under which I was trained in real life environments and how it's used in practise.
And we we saw that in many systems, the systems that that that made the news that they somehow were were trained with one set of data.
But unfortunately, the real life situations in which it was rolled out or integrated into were messier and less organised.
And so it often meant that there were enormous amount of human labour used to train and manage the systems even when they were put in place.
And that meant that companies often struggled to scale artificial intelligence
system so they could work across a broad array of scenarios and problems.
And that takes away or presents a problem for some of the notions of scale.
And there was a notion of reliance, and we like to think of this as like companies were mine either too much on their eye
system or the idea that an ISIS was in place and investing a lot of agency autonomy
and authority into the systems rather than training their workforce staff or bringing
them alongside and making those those decisions practical in the workplace.
We also see that there's a did these challenges of both over and underlined.
So let me take each one of these in turn. First is with transparency.
Now, my colleague Mary Grey at Microsoft Research and her colleague said Hearths, I'm sorry, have just released a wonderful book,
goes to work on the platform work of labouring labelling so that when we see a systems,
we know that there's a lot of contract gig or on demand platform labour that goes in to building these systems.
And often often that kind of transparency is is a problem that we don't see who's involved and the work that's involved in making these systems work.
However, we in our report kind of pushed on a different idea of transparency,
the notion that companies often had different kinds of labour in different parts
of the organisation that were really important in making their systems happen.
And so and so in this sense, the transparency was both. Is the system being transparent with with where work is happening?
Who is doing the work also geographically? Is the work being done within the company or outsourced to a third?
And again, several of the cases found where even internally within organisations,
they would think that a process was happening inside their organisation when it was really happening outside.
The next is this kind of thinking about integration. Right.
And example that how systems actually integrate into existing workplaces really presented a lot of problems.
So we saw several cases where there was problems scaling data across multiple hospitals where data would be collected in one hospital,
a system and a system would be trained. But then bringing that to be able to make sense in another hospital or scaling
it even across different departments of an organisation became a problem.
So we see these challenges of integration really as when data moves from one context to another.
And companies, they can't quite fit the way I they eye into their existing organisation and an existing strategy.
And then third was this notion of reliance. Right. So when was the dependence on I just.
Right. Neither sort of a Goldilocks moment.
And there were several challenges where companies were were touting that their system was was doing one thing, but really.
Well, it was a it was a shield. And what was being done was people in behind the system.
So in countries like China and India, there were crowd sourced to labour actually providing the work.
So it was weak data and privacy laws and cheap labour,
this kind of intersection that really gave different places on the globe a competitive advantage in this kind of work.
And and so, therefore, the the the reliance isn't so much on the technical system, but rather on a new form of outsourcing.
So what does this mean for data work?
Well, we define data work as the work that needs to happen in terms of helping the entire interpretation and contextualisation of data.
In practise, that's first. Second data work is really the work that is involved in helping translate data for fairness and inclusion.
So that, too, needs some kind of context around a system.
And third, that we define data work is the communication that needs to happen with a whole host of stakeholders,
often including conversations about privacy and ethics.
And so this comes from a paper that we published earlier this spring.
Who does the work of data and ACM interactions? And that's with my colleagues.
Mueller, Boesen, Pine, Nielsen and myself. And and in this, we we we we ask three main questions.
Right. Who if we're going to understand this hidden,
invisible lead labour of data work that is happening within organisations preparing organisations for A.I. systems.
We need to ask first who is ensuring that the data are meaningful?
Who's doing the work that helps integrate data solutions into practise within the organisation?
So who's taking the responses? So the sorting and making sure that systems are organised around it.
Second, who's really doing the work of organising an infrastructure thing for making it even possible?
And so much of what we've seen in terms of what Grey and sorry,
called ghost work in terms of some of the platform labour really is that a lot of the organising an infrastructure in work is hidden labour.
But even within organisations, we see that there's work around organising an infrastructure that needs to happen.
And then finally, who is doing this work that is attending to questions of ethics, privacy and people's concerns about their data?
Again, difficult to automate that particular part of work.
And so we took these three questions and we looked at two sets of hospitals,
a set of work in hospitals around creating Redding billing data in the United States for analysis.
So as many people on the call will know, in the United States with privatised healthcare system,
each procedure within a healthcare setting has a particular billing code, and those billing codes can be aggregated to provide real sense.
How long did someone stay in hospital? What what kinds of treatments end up with?
What kinds of outcomes? We'll see, we looked both at the United States and in Denmark, where a site where a set of workers,
medical secretaries do the work of transcribing and making certain kinds of data meaningful within the health care setting.
And so by doing those two and this is where we're really missing out on the slides, we come up with a data,
will we come up with a way that we can model where intelligibility and transparency?
Ongoing optimisation of resources and the work of context, information and metadata occur within the organisation.
And so we think about these three really primary types of work, both as new kinds of work that's necessary to make A.I. systems even function,
but also as recognising work that we might not have seen as crucial in the data driven revolution.
So, for example, are building codes, experts and hospitals in Denmark and the United States were both really key and
making sure that certain kinds of data were ready for data scientists to process.
They were really important for making sure that the right codes got attached to the right procedures.
And that was a kind of infrastructure work, a sort of care of data work that opens up new possibilities for analysis.
And third, they were the ones who interfaced with people who had concerns about whether or
not the codes about them were right or had concerns or questions about privacy.
So before we open up for discussion, and I promise, since we're doing this without slide's, I'm going to I'm going to bring us to a close very soon.
I will just say that the third project that my teams worked on in this scheme and really where I see both opportunity and
a great opportunity for people to get involved is around the discussion of what is and is an artificial intelligence.
So helping people make sense of the systems that we see and unpacking some of the myths about automated work and the future of work,
I think is one of the things that we need to be working on. So with my team early in this in 2020, we released the A to Z of A I or the A to Z of a I.
And if you Google that, you will find it. We partnered with Google to create an educational product that really helps people with a
buy a simple bite sized explainers to help people understand what a is and how it works.
And that and that project has now been rolled out in fifty nine different countries, in 13 languages.
And it's part of an effort to kind of tease out some of the misperceptions that we have.
So I have a series of practise and a series of policy research recommendations.
Before we wrap up.
And if this were slide enabled, if this weren't 20, 20 and the slide and they talk, we would leave these up as we have the conversation.
But I just want to say that for a practise agenda, we really need to be thinking about organising for data saturated societies.
These questions around artificial intelligence. And who does the work and making AI systems even possible.
That raises questions of digital data and the public good,
and it helps us think about projects that seek to understand how we can harness some kinds of data.
Commercial data, perhaps for responsible reuse.
It helps us think about how we want to intervene and think through those questions of who is advocating for the data of data subjects.
Second, it reminds us that people's understanding of their privacy and their own data is
not a task that we can just leave and assume that people will do it on their own,
that there actually is quite a bit of organising and stakeholder engagement work that needs to be done both on the part of companies doing the work,
but also for those of us who advocate for responsible use of data. We really need to be working in this intersection of helping to people to to to
learn to interrogate the values and implications of data driven businesses.
And then finally, third, I mean, this is part of this is we really need to empower citizens and upscale societies as part of this.
So so the work of making AI systems is simply too important to be left to simply large technology
companies who have an interest in as as as vendors of selling systems on to small companies.
We really need to support this responsible utilisation of data, not just by thinking about who's designing systems,
but helping people who are going to be using them on an ongoing, ongoing method.
And that, of course, brings up the policy questions of how we can enable policymakers to up skill as well.
And finally, in terms of in terms of research agenda, I think this applied everyday infrastructure is one way that we can begin to think about
and move A.I. and ethics questions out of the realm of large scale technology makers,
but really start to think about how we map, track and measure these changes and all of our lives.
What's happening in the organisational settings where we work and how can we see that we're moving from
these these questions of a more and more data gathered about us and managed about us in different ways?
Can we identify ways that social, cultural, organisational and causal factor factors really shape who can intervene and hold accountable A.I. systems?
So where can we do the social science that helps us ensure that systems are deployed and integrated in responsible ways?
And then can we begin to think about the changing social norms and conventions that are happening around AI systems and organisations?
When do we cede power to automated systems and when do we remember that behind the interface of
many of these systems is a whole host of human labour that also needs to be held accountable.
And so with that, I indulge. Thank you all for indulging me with my technical issues this evening and invite and come in and join our conversation.
Thanks very much, Gina, for that admirably clear presentation.
I love your organisation. Of Everything in 3D, which is incredibly helpful, is where every training is so that people remember threes.
It helps when you have no slides to follow. However, it is impressive that you remembered all the 3D as well.
Admirably clear and and really extremely urgently needed, because without knowing it,
we are all walking into this maze and sometimes catastrophically so,
not least in the UK government's use of Excel spreadsheets it and understand for track and trace.
I have a number of questions, but I am conscious of the time and we did start a little bit late.
So let me let me just impose a few. And having been in some of these data factories in Kenya, the Samasource and others,
and and also admire the work that your colleague Mark Graham stop and thinking about the rights of people,
which is very allied to the work you're doing. No doubt this is maybe just beginning.
We've got your book that argues that I am at the bottom, but some of what you were speaking about didn't sound too much like a guide.
To me, it sounds more like filling in Excel spreadsheets. Is is that a slippery slope or, you know, without reading a book?
Can you give us a quick definition of of a. But I do think all the participants to get the book obviously answered to look at the deeper explanation.
Thank you. The. So is it is it is.
It just excels. There's a there's a joke that says when it's a as sales or consultant,
a as a spreadsheet, when it's someone who's a data scientist, it's machine learning.
That's absolutely true here. When we look at how companies are talking about what it is that they're doing.
They they they they they put they put a gloss that allows more computation, that infers much more computational power than they're actually doing.
So with the Women's Forum, you mentioned the beginning in the introduction.
I've been doing this autumn a series of focus groups with chief data scientists in in companies in Europe and the US.
And these are incredible company leaders. Right.
These are Fortune 500 companies, banks, large manufacturing firms, large consumers, consumer services firms.
Named the sector we've had we've had an interview with someone working in their data science team.
And we ask the primary question, what are you doing about responsible A.I. ethics?
What are you doing for responsibility in your data systems? And each each data scientist knows they have a huge responsibility to do.
And none of them can articulate yet what it is they should be doing on a practical level.
So there's an enormous amount of Catch-Up that our corporate leaders are doing in
terms of figuring out how to put into practise something that actually works.
So, sure, some of the some of the challenges that we're looking at are challenges of any kinds of large scale centralisation,
large scale control over globalised systems and supply chains.
But the challenges here are that we risk building infrastructures that once put into place as technical data.
Infrastructures become difficult to untangle, difficult to hold accountable and difficult to intervene in.
And so that's why we are suggesting that there's an urgency at this moment for really kind of.
Guy getting getting getting the conversation more involved in holding these systems accountable.
Yes. And I think you you very clearly articulated the urgency of this and the need.
I have many questions, but let's go to some of the questions, the questions that have been posed by participants.
You are able to vote for these questions, so to vote.
If you if you're keen on a particular question and I see we have eight.
Let me take the first, which is from Ali Steadman, who I happen to know.
Hi, Ali. Good to see that you are participating in this. What does the equity of opportunity in tech look like?
Where do we aim and how do we know when we've reached it? That is a fabulous question, because on the one hand,
we want to see the expansion of the types of people involved in designing and building A.I.S systems for the world.
Right now, we see a concentration of that effort in the global north.
We see it in the US and in Europe. We see that only 18 percent of people working in Azi are women.
So we have we have enormous challenges of racial, ethnic, global gender diversity in building the system.
On top of that, we need to start building capacity. And I would suggest that there's a couple of challenges.
One is that the the equity of opportunity is necessary for building better tech, but it's not sufficient.
It's not the only thing that's going to get us there.
The second would be that we really need to increase the capacity in the global south in order to ensure that systems are properly localised.
So if we are just relying on systems that are built in one place,
trained on data from one place and then integrated into into the systems around the world, that's that's a recipe for disaster.
Yes, integrating bias and a massive concerns, not least Shakir Mohammed.
Deep minds been writing very admirably about the colonialism of data and data algorithms for votes for doxa rulers.
Question. Can you comment on technological determinism? Is I.
Trends such as those discussed. Yeah.
Susceptible to the fallacy of technological determinism. That's a fantastic question.
We certainly hear that determinism in how industry leaders talk about and support and and push the inevitability of A.I.
So if we look at this kind of data work question, we where we see people in these large and these large hospital settings,
the people who are involved in the day to day operations of getting data systems ready for data analysis.
There's a whole host of new kinds of jobs that are being involved.
It's not inevitable that that it automates work or that it displaces all kinds of work.
But instead it's creating these new moments with with with different kinds of opportunities for people to be involved.
And so and so when I think about the technological determinism,
I think the technology has its own drive and it will naturally kind of go in one way or another.
Whereas whereas what we can what we can see from how from from from older industries like healthcare and construction
to that I know very well in this case that the pathway that new technologies take is not predetermined at all.
So so I think we have I think we as educators have work cut out for us where we are.
We make sure that how the technology industry talks about the inevitability of their genius is held to account.
Absolutely. So there's also people on YouTube participating and we've been since.
A couple of questions that have come across from that.
And the first one which seems to disappear is I was wondering, this is doesn't say who it's from.
Andre Grisman. I was wondering how Prof.
Mathaf thinks data saturation in a rich healthcare will impact medical education in the next five years.
Brilliant question from a brilliant colleague in the United States.
So we absolutely have to use these notions of data work to help people understand the context of that that results they're looking at.
If we continue to think about the outputs of A.I.S systems as decontextualised outputs, we end up in a dangerous place, especially in medical systems.
If we if we understand where the data come from, what kind of context they have and and who is advocating and who is responsible for ensuring that
there is the organisational stitching together between the outputs and the the the the organisation,
the practises within the organisation. We would have much more highly contextualised and much more relevant results.
It's life and death consequences and health care. So where we are right now, today, in this moment where lot we are, we have a lot to be grateful for.
Large scale computing power and the integration of global supply chains is going to help
us end this global pandemic sooner than than than any pandemics ever ended before.
Right. We we we should be cheering large scale computing power and the collaboration possible from teams in this particular way.
But we're not going to get to those great societally beneficial outcomes unless we realise that these data systems are highly dependent,
highly contextualised and sometimes highly fragile. Many of the cases that we looked at in our work report come from healthcare,
where data from one context was simply tried to be brought into another context of data gathered in one particular
hospital reflected choices that that that were very specific to that hospital and not applicable to to other hospitals.
So how does this influence medical education?
We have to train people in health care who are going to be using these systems on how to use them responsibly, how to be critical.
Tumours of the data they're using. They are our front line defence for bad.
And when a guy goes awry. In fact, that was from someone else.
But Andre Guzman said they'd like to extend this this discussion from medical schools to business schools,
etc., what should be the core of an education generally related to a Ayane data?
Carl Broten, Bergström and Jevin West at the University of Washington have a wonderful new book out called Calling B.S.,
where they developed a training course around critical human centred data science.
And they basically are training people to say, wait a second, I am calling out and I won't use the profanity.
I'm calling out the as wrong as patently wrong.
Some of the data, some of the seemingly data driven evidence here,
I think it's incredibly important in this particular moment when we see attacks and challenges on science and evidence,
we need to stand up,
on the one hand for science and evidence and help ensure that we continue to build public trust in good science and good evidence.
But on the other hand, building systems,
building fragile A.I. systems that are not robust or they don't deliver as promised isn't helping us get there.
It's if we want to make sure that we're not simply selling new 21st century versions of snake oil.
And so I think it does happen where I think one of the solutions,
just like we would train medical professionals and knowing how to push back on and query A.I.S systems,
they don't need to know enough to design them, but they need to know how to how to operate them.
It's gonna be the same in business schools. We're not necessarily going to have that.
The MBA is in the CEO's necessarily as people designing systems,
but they're going to need to know enough to ask their team and to hold them account for how the systems get integrated into their existing practises.
And that's the education I think, that we really need to be doing right now. Absolutely.
Including in Oxford. There's a reporter asks with three votes.
What specific regulatory mechanisms would you like to see you put in place, put in place to address these issues?
We've already started to see questions around people's individual data. Right.
How do we how do we advocate for ourselves, you know, with the European GDP are, for example, general data protection.
How do we how do we get individuals to advocate for this?
We some of my esteemed colleagues have called for a kind of algorithm regulation.
Right. That we that we that we regulate particular kinds of data systems and data structures.
I don't necessarily think that's the path because we have in place enough outcomes regulation.
So, for example, in the education system,
we want to make sure that data systems are not failing our most disadvantaged students and that they're treating people equitably.
We have we have ways of monitoring that.
When we met with company leaders this autumn with the Women's Forum asking data scientists what they're doing about responsible tect,
we found, surprisingly, that some of the most advanced conversations around responsible tech were happening in banking.
Why would that be? Why would it be in banking and not in technology, for example, on banking?
They already are under such a highly regulated, regulated system around how they make their decisions and choices that they
had to be really sure when they integrated new systems into their analysis,
that they could explain them, that they could explain them to the customers, that they could explain them to them themselves,
and that they could be assured that these systems were not causing new forms of discrimination.
It wasn't because that there was a special banking regulation in place, but it's because we have certain regulations around our financial data.
And so and so I think that that's the the the way it's a framework, as it were,
for how policymakers need to be thinking about regulation in this space.
We need to be thinking about what existing frameworks in each realm of our our our lives as citizens.
Do we need to be adjusting to think through how these systems are going to integrate into that?
Because that's the infrastructure. So we're building. Right.
There are three votes for this question from Gwen in your research, have you spoken to employees interacting with data driven system?
If so, what are their main concerns and challenges in their work?
And how do these differ by race, gender or age?
In the data work article? In the article, who does the work of data that we published over the summer?
We spoke overwhelmingly to women and in the United States it was women of colour who were
having who had very stable bureaucratic jobs and large hospitals doing the state of work.
They are the champions of the Azi era, large scale data.
And those hot data analysis in those hospitals did not get done without these women who worked in literally the back offices.
The challenges with our medical secretaries in in in Denmark was that the hospitals where the we interviewed,
they were actually looking at automating their work because some of the some of the work that they had done up transcription was being automated.
And so their concerns and challenges really were, how can I can how can I keep doing this work that I conceive of as care,
as caring for our patients data, as caring for others,
as making sure that the record stands properly while not getting recognised for doing that work.
So the challenges really were how how can how can we support this invisible labour?
It's not being called a I work. It's not being called data science.
And yet neither new systems nor data science can happen without it.
So I think the main concern and challenge is making sure that we're supporting good organise.
We're supporting the work that needs to happen within organisations to make sure this happens.
Is a cold. What's the organisation that Mark Bram's involved in as well?
That's right. So Mark Gramme, my calling Mark Ram is working on Fair Work and Fair is is looking at the platform economy,
labour rights, so, so, so much of this work. Mark, the work of Mark Graham, the work of Mary Grey.
Much of this is looking at platform based labour. We're not seeing so much platform based labour in banks and hospitals.
And yet and yet that the work that's being done in those organisations is absolutely about the invisible work that's fuelling.
Yeah. So another question from all of this has two votes, which is one another vote, which is what I'm going to give him a second question.
How do you regard human level performance as a metric to beat in machine learning applications,
especially in manufacturing, checking for defects and so on?
And he says Andrew Migg and I wrote recently about its limitations.
Right. So, listen, we know that large scale computing power is going to help humans solve really big problems.
And there are really big problems we cannot solve without large scale human computing power.
I think if we frame artificial intelligence as the core, Larry, or the competitor to human intelligence,
we we we get to these questions, will, which is which is better.
We don't actually ask the question which is better, my calculator or my set of hand calculations.
OK, sure. My calculator can answer some things faster.
I can I can do some other things easier. That's going to continue to be the case with artificial intelligence system.
So if we think about if we think about using the spray human level performance,
we again are pushing A.I. systems to act and think like people and not act and think like bits of technology that we use or.
Bits of infrastructure that are going to be part of much bigger organisations and data ecologies and flows of data that that already exist.
So so to personify in some ways is to hand over a powerful sensemaking tool that takes the power away from how we can intervene in these systems.
Thank you, ask this question, but it's not a question of Sujata.
Can you comment on deep sake's or the more concerning the general ábhar issues you've discussed so far?
Listen. You know, I'm concerned about deep fakes almost as much as everybody else.
We have at the moment big challenges to add to the notions of information.
Is our information secure or do we how do we know what we know?
And this is a this is a question about it. It's a pistol ecology.
This is a question about how we know what is real. That is very much a function of where we are in the early 21st century.
But that said, I am more concerned about ensuring that we have a healthy, robust new system, healthy, robust democracies,
healthy, robust organisations than I am about a particular technology of having lifelike human pictures.
Right. So so we know, for example, that the amount of misinformation circulating about elections on Facebook varies by country.
And we know that's a function, not a Facebook, not of the people in one country being smarter or easier to do,
but a function of the regulatory environment and the social environment in which those elections happen.
And so and so on the one hand, we can worry about deep fakes.
On the other hand, we really need to be thinking about how do we shoulder the responsibility and bolster
our social institutions and organisations to make sure that we're supporting a society.
That's a great point to end on, and I'm afraid we've come back to our which is raced by.
Sorry to all the participants for the technical glitches at the beginning.
That's part of this transition to a digital world.
It's great to see that so many of you have joined this, some of you extremely late at night or early in the morning.
And thanks so much to Gina for enlightening us on what is an immensely important topic in a way that I found exceptionally clear.
There's a lot talked about in this area, and I found your presentation to really cut through a lot of it.
You can follow Gina on at Gina soon.
You can follow me on an underscore golden Geraldine.
I n do look at the Oxford Martin school events page.
The next event, which is really a must for anyone that's interested in the Internet.
We wouldn't be here without this man. Interconnect is Sir Tim Berners Lee in conversation with some.
Who was another founder, together with some of much of the computing private, Sir Tim,
who is credited really as the person behind the World Wide Web, will be at the Oxford Martin School giving a talk at five o'clock on Thursday.
So do register for that. If you haven't already and look forward to your engagement in the future.
Thanks for all of your participation and thanks particularly to Gina for her great presentation.
Stay safe and good luck to you.