[Auto-generated transcript. Edits may have been applied for clarity.] Welcome to our students in the room and only two guests in the room, but also to those joining online.
This is the guest lecture for healthcare evaluation and research impact.
And Tom, who's going to introduce himself fully, I know making part of a team from Rand Europe, which is a kind of policy facing research consultancy.
Yeah. Um, who actually collaborate with us because we in the university are good at certain things like ivory tower academic stuff.
But actually the interfacing with policy is something that Rand really majors on.
And so so we like collaborating with them. And I can't remember how we ended up inviting you.
I think I just put a throwaway line in an email, throwaway line about evaluation and AI.
And I said, well, that's it. I think it was actually we on a team's call and you put it in the chat?
Wait a minute. I'm looking for someone to give a talk on it anyway. That's exactly what it was.
So welcome. Thank you very much for coming all the way from Cambridge. You've been, said Morton.
Do introduce yourself. You've got 90 minutes and you're going to be interactive.
Apparently, when you ask a question, it will be edited out of the video.
It will be edited out of the video for data protection reasons.
So you may find Tom repeating your question. Uh, and that's kind of for the video capture.
So. So I mean, this is a funny guy who's just repeated my question back to me, that's why.
Anyway. Welcome, Tom. Great. Thank you. Thank you so much and lovely to be here.
Um, and this has been great. Kind of preparing my thoughts for talking to you, because it's always a great way to consolidate your own thinking.
And that thinking in relation to AI is going to be rapidly evolving.
so it's great. It's a kind of stock taking moment for me as well. Um, but the that all of that is changing and in flux and and evolving.
Uh, so whatever we finish up agreeing in 90 minutes,
the world will continue to change and we'll need to be adaptive and learning as we as we go forward.
Um, so we're going to make this quite interactive. There'll be lots of opportunities to, to pause and to chip in.
Um, and then we aim to finish by, by by three, 3:00.
Um, right. So AI and the ethics of evaluation.
So it's not the evaluation of AI, right. This is going to be about how we use, um, uh, AI in, in evaluation and in particular how we do that.
Um, and how we, we do that in the world of evaluation around Europe.
We already mentioned is a not for profit policy research institute.
Um, We we work in the in the public interest and we aim to publish everything we do, just like an academic institution would do.
But we have the kind of internal structures of the management sciences, um, and,
and we focus particularly on policy related issues, covering the whole range of public policy.
But my work is particularly in the area of health care. Um, but I've also got an ongoing interest in international development.
Um, I work with Save the Children for a while way before that.
It was also at random. Before that, I was at the National Audit Office as their research fellow, working on kind of value for money evaluations.
Um, in the long distant past, I had a few academic appointments along the way.
Um, so I am if I would, I would claim to be an evaluation expert.
I absolutely would not claim to be an AI expert. So, um, you've probably got more expertise in AI than than I do,
but we can bring that our insights together and learn together in the coming 90, 90 minutes.
Um, as well as, um, around Europe.
I'm the, the, uh, recently the president of the European Evaluation Society.
I'm still an advisor to their board. Um, I ran out.
I can't engage with the UK Evaluation Society as well, but they do great work.
They produce good guidance for evaluators. If you go on their website.
Um, on on the use of AI for evaluation, um, and the European Evaluation Society, we've got our conference in Lille.
You're very welcome to come in October. Um, etc., to pay for it, but you're very welcome.
Um, and, uh, an AI will certainly be a kind of ongoing topic, and I think I'm giving a session on that while we're there.
What I'm about to, to outline comes from a chapter that was recently finished, which will be published early next year with Routledge.
Um, and it's from algorithms to evidence using generative AI in evaluation practice.
And our piece, our chapter is on the ethics and practice of evaluation.
Um, and I should acknowledge that I'm doing that with Ananda Millard, um, who is is a European colleague of mine.
Um, so a lot of this stuff draws on on that.
Any questions? We're kind of okay on on what we're what we're about to do and who who I am.
I suppose the other thing is that I, um, spend as much time as I can writing poetry and music.
So you can find more about that on Tomlin. musicOMH should you, should you wish to do so?
Um, I mean more slightly more seriously that maintaining I recommend in your careers that you maintain a creative
element to your life where you're you're doing things that are kind of metaphorical and interesting and creative.
And I believe it sparks a lot of useful thinking in the harder work that you do in from the evaluation and and in report writing and so forth.
Getting, getting some sense of that. So let us get underway now.
Are you not to be looking up to music as I as I speak? So what is the what is the issue that we're going to be exploring?
Um, evaluators are exploring are embracing AI, but actually we're just saying earlier it's quite recent really the last 2 or 3 years.
Um, and a lot of people were either just pretending it was going to go away or were,
um, uh, we're saying, well, it's the it's the, it's the it's the spawn of Satan.
It's going to ruin everything. Um, and that whole that attitude, I think has shifted a lot, even if it doesn't,
because it's evaluation works more much more than academia, which is competitive.
And of course, backbiting and mutually supportive evaluation is very competitive or is competitive interdependence?
We work together a lot. We collaborate a lot, but we're competing for the same business.
So Rand Europe is not for profit, but it's also not for loss.
And we've got 200 star researchers. And that's a lot of mouths to feed.
And we've got to win a lot of work. So if we don't engage with AI then our competitors will.
They will be more efficient and more effective than we can be. And we will.
We'll just not win work.
So, uh, until very recently, if we were going to do a reasonably good literature review as part of the first station evaluation,
we'd probably cost that up at ten, ten grand or something, or 20 grand of that order.
And now the first draft will be done by AI, and we will then check all of that by hand.
There are a few loose nations, and there used to be. There are a few. I mean, it's all a good bit better than it used to be.
We'll check it all by hand and redo it. Um, but but we'll probably that that budget for that bit will probably be five grand.
And if we try to charge 20 grand, then somebody else is going to go.
So it is the thing about evaluation,
if those of you that finish up working in that area is that even if you're working within an organisation and you're there evaluator right,
you will still be, uh, the question will be asked, why should we do it in-house if you're far more expensive than getting someone else to do it?
So that's the reality we're going to we'll definitely be moving into AI, and that makes this discussion even more important,
because we've got to think about how we're going to do that and what the challenges are,
not focusing on the specific techniques, although they will crop up from time to time.
It's about the broader question about the relationship between reflective human inquiry and AI led reasoning.
What's how do we what's the hybridity that works within ethical guidelines And within and produces
results that we can defend in terms of their rigour and the quality of those data that come out of that.
That's going to be where we hope to get to. Um, but what's peculiar about evaluation,
it what's particular about evaluation is that it requires you to come to a judgement about the value or worth of something, right?
Um, academic research is very often an inquiry based.
It's just like, let's find out more about let's what is going on here.
Let's let's understand the world more deeply than we did.
Very important. Evaluation uses the same methods,
but those methods are oriented towards arriving at a judgement about the value or worth or and or whether it can be improved.
And you know, so those those are the, the things that you do as an evaluator.
You have to come to that. And typically there would be kind of more practical recommendations, um,
obviously some academic research does that as well, but, um, not all would do that.
So what's often what's often called the Industrial revolution.
So you've got the classic 19th century industrial revolution. You've got forward production lines.
Next you've got the information explosion. Um, in the 60s and 70s, and now you've got kind of AI led as the fourth Industrial revolution.
Um, and that that is marked by the fusion of digital intelligence and human systems.
And I want to emphasise it's about the fusion of that rather than just the, the replacement of human judgement by AI.
Um, but it is reshaping how knowledge is produced and how we apply it.
Um, uh, of course, AI is not a single thing.
It's a family of computational approaches.
Um, and we will touch on, but we're not going to, um, over focus on, artificial superintelligence, which some argue is going to come.
Some argue it won't happen, but that's where, um, AI agents spend a lot of their time learning.
From other AI agents. And those AI agents then share the fruits of that thinking, that knowledge,
and you are quickly moving into something which is beyond human comprehension at all as to how we got there.
Right? So that's the kind of superintelligence.
Whereas a lot of the things that we're looking at will be more specific AI agents, which we set to work on a particular task.
So, for example, I did the evaluation, I did the review of UNICEF's, uh, evaluation policy a couple of years ago.
Um, they've got, as you can imagine, 30 years worth of evaluations, um, which just sit on shelves and absolutely never, ever get used.
They've digitised all of that, and it's now entirely searchable using AI.
And you can just ask, for example,
what is the best way to support girls education in a humanitarian crisis or in the first six months of a humanitarian crisis?
And it will just give you everything that is available on that.
And that is such a step forward in terms of the utilisation of vast amounts of knowledge that previously really were just just sitting, sitting there.
Um, um, and yeah, so it's about, uh, judgement.
Let's, uh, moving on. Uh, very, very briefly,
I mentioned the kind of the people talk about the four industrial revolutions in terms
of another way of thinking about it is to think about the four steps of digitisation,
uh, in the 1990s, um, and digital data collection and performance systems and data application with large um,
geospatial analytics and big data, cloud based infrastructures, all of that.
Um, and then, uh, algorithmic transformations, the integration of AI in machine learning.
And then now where we're at is, I believe, kind of hybrid intelligence.
It's about this kind of interaction between human judgement and human, uh, leadership and, uh, AI leadership.
Um, and so what we need to do is to try and think through how we, how we balance that, um, in knowledge generation.
So coming back to focusing on, on, um, evaluation and AI, um,
probably the best way to think about it is that in any evaluation, we just go through a series of steps.
Um, and the rule of AI is slightly different in each, each of those steps.
Um, if you can imagine going that's kind of chronological, isn't it?
Uh, from designing through to, to delivering.
Um, but it's the little the bits between these, the step back opportunities for reviewing what you've done, planning the next stage,
making sure that it is reflecting your interests and the needs of your clients, but also your ethical requirements as an evaluator.
Um, and on the right hand side, then we we commission, we design work, um, but also we commission evaluations.
So AI is already you can read it in the in the invitations to tender.
AI is already being used in the commissioning process, for better and for worse.
Actually, sometimes it is very difficult to work out what's going on.
But, um, but there again, it was always difficult to work out, um, rather than in addition to the tender from some sources.
Um, so designing and commissioning, um, then the kind of inception meeting where you're really digging down,
how are we going to manage the we've got 250,000. We've got 18 months to deliver.
We've got these questions to answer. We've got these databases that are available.
We've got this resource that we can collect new data. How are we going to do that?
That that actually, um, has always been something that the experienced old hands like myself would just kind of go, that feels like 25 grand.
I think that will probably cost.
And you kind of have a get a feel for it will probably be much more accurate and specific about how we go about doing that now.
Um, and I think what they'll do is they'll learn.
We'll, we will look at previous examples and train the AI on what we said we would do previously, and then what actually happened.
Um, and hopefully that will not reproduce the errors of what we, what we did for what was the one there was um, recently guys in Sir Thomas's,
I think there were it was on their recruitment processes and the questions that they were asking and how they went about recruiting.
And the AI agent was instructed to replicate the existing processes.
Right. And then they implemented that.
And they were shocked to discover that it was significantly discriminating against women, for example, amongst other things.
And then so then we went back into it and what they realised was they were already discriminating against women.
It just became visible and apparent when you when you put that into the AI agent.
So that's yeah, that's kind of interesting examples. The um, so the that kind of inception meeting getting you know, you've won the contract.
So you've, you've got the NIH grant, you've then got to say, right, specifically, what are we going to do about this?
Um, and then you're a data collection.
Uh, of course, there are all sorts of opportunities for, for searching social media for, uh, for doing, um, AI based interviews.
Uh, and so the AI based interviews don't just take the interviewee through a preset set of questions.
They respond to your response. Um, and, um, uh, there's a bunch in Bristol that I've developed all the software for that,
um, for example, on quite intimate topics of sexual health or whatever.
Um, the most the majority of people respondents said that they preferred being interviewed by an AI agent, which I hadn't really thought about.
I thought, well, it's probably cheaper and whatever,
but actually people are saying that in many circumstances they quite like being interviewed by an AI agent,
even though they know perfectly well it's an AI agent. But the so the the agent will then respond to your reply and change the next question.
But of course it will also, um, then do all the analysis as you're going through.
So one of the particular uses of that would be um causal mapping.
Um, so don't. This is not it doesn't prove that this is the real causal mapping in the world.
This proves what people think are the causal maps, the causal causality.
So you ask people across a whole. Population group what what what would happen if.
Why do what. What causes that to happen if that had happened, what must have been the antecedent for that?
And then use AI to build up the user a questionnaire.
And then that will give you a map of the causal maps that people have in their heads.
And of course, that's not necessarily true,
because people have in their heads all sorts of misconceptions about causality, but it's a fantastic starting point.
Um, I mean, just for example, on misconceptions, we did, um, the evaluation of the integrated care pilots,
where ten years ago now, um, 16 integrated care pilots, really quite interesting.
And many of them. Over half of them had as an outcome that you would reduce unplanned admissions to hospital.
So a pretty obvious target to have.
Um, and um, uh, or every single GP that we interviewed agreed that what they were doing was reducing unplanned admissions.
We looked at the data and actually there was no evidence that if anything was increasing our population.
Um, and, and we didn't have enough time to dig back in, and we were shocked by that.
But probably what was happening is that, um, better integrated care meant that, um, Mr. Smith on a Friday afternoon is visited by his health visitor.
Um, and he's a bit, you know, a bit unwell, a bit iffy bit, and normally wouldn't get picked up.
And normally I'll come back. I'll come back, uh, Tony on Tuesday and we'll have a and we'll see how you're doing with this improved system.
Mr. Smith was whipped into hospital. Spent the whole weekend in hospital.
Then by Monday or Tuesday, it was probably better anyway.
Um, so there's nothing appears in in improving morbidity rates or mortality rates, but you've got an increase in unplanned admissions.
So it's better care. Almost certainly better care.
Um, and desirable. But in terms of the outcomes intended by the integrated care, it wasn't achieving that.
So. So it's a little footnote to the point that asking people about causal maps and what happens
if I do this is only a starting point to then try to understand what really happens.
And you triangulate that with other data, but really, really useful.
And then, of course, data analysis. Um, and one of the first things in data analysis that we would normally do would be cleaning the data sets,
which how many of you have been involved in cleaning data so desperately?
Yeah. Um, I mean, the big uh,
the really big advantage of at some point early in your career of cleaning data is you really understand how the data is constructed.
You get a real feel for what works. If you just ask A.I. to clean the data, it will remove immediately.
Remove all the outliers. But some of those outliers are the very things that you want to find out more about.
Of course, we'll talk about prompts later. You can prompt it to to pay more attention.
You can do other things, but the tendency would be to say and very confidently to say, we've cleaned the data and this is what it looks like.
Um, but you as a researcher have no idea how that was put together.
So I would always, you know, encourage people working with data early on, at least in their career,
to spend a bit of time actually cleaning the data sets and, and seeing how their, how they constructed and how they might be used.
Um, and not just ask AI to do it because then you've got no understanding of that.
Um. Uh, another kind of quite useful thing.
Very often, uh, we benchmark or compare.
Compare against standards would be an obvious thing. You can ask AI.
Say these are the standard procedures for this illness.
For this surgical intervention, whatever it might be.
Um, you can you can establish what those are.
And then you can use AI to check how people are talking about it, what they're doing, and what practices are actually being used.
Um, and that, again, will be a starting point for doing something else,
which you're going back to the surgeons and saying, why are you not doing the standard procedure?
And you need to try and understand more because some of them will be doing it because they simply inept and they should be doing it,
but they don't know what they should be doing.
Others are doing other things because they're innovating and trying out new approaches, and they're really very good at what they do.
Um, we did some work with on quality improvement with surgeons.
This is quite a long time ago. Um, and so you would say to surgeons.
Um, these are the guidelines. Um, why you why don't you follow them?
As a significant group, we're not following them.
And they would almost everyone said I, um, I know that this produces better results for the average surgeon, but, uh, Tom, I'm not an average surgeon.
I'm actually a lot better than that. The techniques that I've evolved over time are my skill that I bring to it.
I mean, that I get better results, not following the techniques. But what came out of that was every every surgeon is above average.
It would seem. Uh, so, um, yeah, comparing against uh, against guidelines and standards can be quite, quite, quite effective.
Um, but also um, benchmarking against other countries, for example.
So OECD, a lot of their learning that they try and do is to just say to governments without any, any other, without a lot of additional comment.
You're doing it this way. You're getting these results in Sweden or Denmark's always better.
Um, they're doing it that way. And they get and they get other results.
But actually, to be honest with it's quite difficult to translate a lot of that into your own thing.
And then, um, then there's this whole thing about kind of reporting and sensemaking.
One interesting thing is that expert judgement, human expert judgement, um, is swayed, is influenced by the quality of the writing.
So very grammatical, very beautifully constructed arguments.
Um, humans respond to that quite positively. AI is going there.
Well, whatever, uh, is less less impressed by that.
On the other hand, if you ask AI to help you write your report, it will arrive generally.
And again, it depends a bit on prompts, but I left it's own devices.
It will be much more overconfident about the findings and it will give you a much smoother Gulliver interpretation of your results.
The nuances will be lost. The the outliers will get lost in all of that.
So. So it kind of cuts both ways.
Which leads you to the conclusion that I will come on to this, really probably a great way is to do both expert solicitation.
So do your Delphi's or whatever you're familiar with.
And so Delphi is a way where the evidence is unclear.
What is the best treatment for lower back pain?
You just ask the ten talk surgeons or clinicians in the country who know back pain, and you ask that questions,
and then you get them to arrive at a consensus, usually with 2 or 3 rounds of questions.
And then then that's your source of consensus seeking advice amongst the experts.
Do that and do the AI and then ask yourself, why are we getting different results here?
What's going on? So you you don't have to privilege one over the other.
You can kind of engage. And so let's, let's let's look at do the expert solicitation, do the do the AI led analysis and then bring that together.
Um, the other bit, to be honest, we haven't really we've only just got started on this is on project management.
And um, so just, just how do you make sure that the project is running smoothly, putting all the dates in advance.
You've got your when, when when interim reports have to be done.
Da da da da da da da da. And it just prompts you to make sure that you're, you're ah, target with, uh, with all of that.
Um, and that will make quite a change, um, in terms of bidding for work.
For example, you always are asked to produce your track record.
What have you done as an organisation? God, it's a boring bit and you always put it to the end and you haven't got enough
time and you miss out with the most important report that somebody else did.
And, and it's a, it's a real pain.
Um, now we're just going to, we're putting all our reports into, into an AI agent, and it will just ask it to give us our track record every time.
And that little one page, it's like just a little bit, but it's going to come up that's happening, you know, 3 or 4 times a week for us.
Uh, it's an enormous saving. Uh, and we're also less likely to miss stuff out than we than we did previously, because it was just.
Well, speaking personally, because I was extremely poor. And of course, you want us to do it.
Uh. Um, anyhow, so that's that's the stages that we're at.
And at each of these stages, we've got things around, um, kind of validation checks and so on.
The one I just wanted. land on in particular is prompt design, as something is called prompt engineering.
Um, that's the prompts that you give it.
Um, and the more precise and carefully engineered your prompts are, the more the more likely it is that you will get to the results that you want.
Right. Um, so so they're using the prompt so you don't just ask your first question.
Um, why is Tomlin a great poet? Um, you then have these other prompts that you would then kind of go through.
Uh, it actually will tell. Sorry. It will tell you it said something is a great poet.
Uh, he writes in Cambridge. He does these things. So, um, and none of which is particularly true.
So you have to be really careful. But if you then use prompt, is he better than so-and-so?
And no critic so far has thought that Tomlin is better than Shakespeare.
Um, or whatever it might be. Um, but I mentioned the comparisons with human judgement as well.
I think that what do. What do expert humans say? What does what?
What does AI say? What do we think about the balance between that?
Oh, another great I talked about, um, uh, just doing doing your, uh,
track record using citation requirements is another incredibly boring bit of our work.
Um, and it will just do that for you, which has got to be got to be good.
Um, so it's got a number of useful things that you can do at each stage.
All of this is quite positive, right? Um, and it is kind of encouraging, um, a degree of optimism about AI, at least amongst my fellow evaluators.
So even in the book I mentioned, all the previous chapters are pretty smart.
They're not entirely gung ho about it, but they're pretty much.
This has got to happen because it's a competitive market and it's making us more efficient.
It's making us more effective. And I think there is insufficient step back.
about what you know, what might go wrong and what are the what are the issues around that?
Yeah. Chris, I want to start a campaign to drop the term prompt engineering.
Huh? Um, because I read this book How to Talk to AI.
So it really changed my life. I know you shouldn't have books that changed your life.
Um, because the idea that there are these magic phrases that you can put in actually is part of the problem with the way we are thinking about AI,
that it's this kind of oracle box, and if you put in the right magic phrase, then it will spit up.
Uh, and what this book talks about, I can't remember the author, is you've got to have a conversation with it.
It is just a random text generator. And so if you give it a crappy prompt, you will get something that's incredibly bland and possibly confabulation.
But if you actually tell it something unique, like, I'm a professor at the University of Oxford,
and I've got to give a lecture to the students later on about, um, developmental evaluation and what I really am.
And the students are coming from these backgrounds, and I want to make just six PowerPoints,
like, I give it all that information and then it produces something.
And then I say, that's not what I want at all. And the reason it's no good is this, this and this.
And then it will say, oh, sorry. And then it changes its mind and then you go back and forth.
But it's not like there's a magic prompt.
It's just that if it if you're going to not just generate random bits of statistics, like, you know, the statistically most probable words,
you've got to tell it something unique and then you get great stuff out of it and then it stops confabulation.
But I think one of the problems is the word engineering. Yeah.
I could probably. At least you die in a particular way of thinking. I would call them prompt conversations because it's it's a bacteria in dialogue.
Hey, they've got vaccine in there. Yeah, I like that. That's that's a really, really nice, nice thought.
But it is the term that's very often used. Okay. So we've just heard about the reasons for exercising some care.
And I want to now begin to move into why we need to exercise some care.
Having the really up to now we've been emphasising some of the virtues and benefits providing you do it with care.
But there are some other challenges. It's quite helpful to try and separate out the levels of risk.
Or if you like, you know, what is it good for? What is it? What where is it quite straightforwardly helpful and where is it?
A more complicated thing. And um, and this will change over time.
By the way, I think that this is already I mean, you we mentioned the the literature review.
I think we just get far fewer hallucinations now. I don't know how that's happening, but it is.
Um, so, um, it's very good for text summaries.
Um, uh, for basic qualitative coding. Translation. Um, uh, it can, uh, it can do basic code generation statistics and document formatting.
So quite, quite a, I mean, these are, you know, not not useless things by any means.
Um, but for more complex analysis, you need to have a bit more care, probably doing what we're talking about,
checking it against other agents, checking it against expert, uh, expert views.
Um, the kind of logic model, theory of change development, real time data collection.
Uh, you're familiar with the idea of theory of change, aren't you? If you all been. Yeah, yeah, we've done a lot of downloads of that.
That is probably the one, um, idea that is unique.
There's a unique contribution from the evaluation world, actually.
Uh, there's that most of the time. If you like parasitic on the other social sciences.
But the theory of changes is quite kind of critical to the way that we think.
Um, but we're talking over lunch about the sometimes the theory of change can be very static.
And what you're really interested in would be a theory of, um, of, of, of evolution, of the trajectory that you're on.
If you've got a complex intervention or you've got an intervention in a complex system.
Um, it can be one a very small intervention in a very large system.
And you need to understand the trajectory of that wider system before you can begin to make sense of what's happening.
Of how the intervention of interest is, is shaping that.
Um, so I'm just doing some work for the Government of Northern Ireland, not in health for some reason.
Um, or because they wanted a realistic evaluation on the training system in Northern Ireland.
So the training system in Northern Ireland has received vast amounts of money in the last 4 or 5 years.
There are some great examples of trainers. There's some great further education outfits providing good quality training.
The gap between the skills needed in the economy and the skills available hasn't shifted, if anything has got worse.
So you've got what appears to be a good system, but it's not delivering.
It's a bit like, you know, the operation was a complete success, but the patient died.
So, um, so but but in order for us to make sense of that, we have to understand that at the UK level,
what's happening to training, what's happened to young people in particular, NEETs not in employment, education or training.
Um, and how is that evolving? How is that distinctive, rather different in Northern Ireland?
What shapes that? And then why is the training system landing in the way that it is within this very complex and evolving system?
But unless you think about that trajectory, then I think you're you're missing that.
I think a really useful area for. I would be trying to understand trajectories more.
Um, that where we're particularly in complex systems.
Um, and, uh, I've spent quite a bit of time trying to understand why interventions to reduce health inequalities don't work.
Um, and there's that's pretty much as broad as that.
They never have. We haven't seen any significant, uh, state led interventions that have actually improved health for everyone,
but they haven't narrowed the inequalities.
Brown was a bit of an example where that because of a start, we saw some some just slight flexing of the gradation, um, which links,
uh, poverty or social inequalities with health inequalities that just kind of tilted a little bit there, but it hasn't changed.
And that's because what we need to be able to do is to understand the intersection amongst neuroscience, uh, foetal development.
Um, uh, biology, sociology, economics, uh, uh, history, very much kind of historical circuit and.
Bring those together and it's beyond our capacity as humans to to manage that.
I kind of see some of these really areas of high level complexity, trying to use AI to explore those and bring in multi disciplines.
Because the other problem is that the disciplines don't speak to each other very easily.
Um, I mean, there's almost no sociologists will really have any familiarity with neuroscience, I think, and foetal development.
And yet that's kind of crucial to if you're looking at health inequalities for people in their 30s,
those are to a very large extent linked to their first thought the first thousand days after conception.
So very, very powerful. And it's intergenerational as well.
It floods through the generations. So understanding that it's very difficult for us.
Um, but I would like to think that we can build models using AI that are genuinely interdisciplinary.
So welcome. Are trying to do interdisciplinary work or fund interdisciplinary work, I should say.
Ukri Trisha was just talking about that are doing doing work in that space.
Um, it's very difficult for us that even when we want to talk to other disciplines to do that.
Well, I suspect the AI can do that better than we can. Um, it is less it is less locked into its own disciplines.
Um, so but the so this kind of causal inference, um, is at the moment it's not reliable at all.
But I wonder whether that's the kind of area where we can begin to push forward, um,
going into this kind of interdisciplinary, highly complex social interactions that produce outcomes that are of, of interest.
So I distinguish between flatter things or smooth terrains and rugged terrains.
A rugged terrain is where the same intervention has very different outcomes depending on where it lands in that terrain.
Um, and so the same that it will.
Yeah. And a smooth terrain is where the same intervention has very similar, uh, outcomes where,
wherever it's implemented, when you're doing a randomised controlled trial,
what you're trying, what you consciously do is to turn what might be a rugged terrain into a smooth terrain,
and that you narrow down what the variables that you're looking at.
You focus on one population and you focus on one, one particular, um, uh,
intervention and one particular outcome or a set of outcomes, and you kind of get your measure.
That's a very, very smooth terrain. What you're talking about is very rugged terrain.
Uh, and that is where I think I wonder whether there's more we can do.
What? It's beyond my limited capacity.
If you guys are great, I would get off and try and really think about causal inference in rugged terrains and how we can make some progress on that.
And I'm in a good place to start might be health inequalities,
but that might actually be such a challenging thing because it is the causal mechanisms are so varied and context dependent.
I think it would be.
But I think, yeah, I think making some progress beyond the evaluation conclusions that say the results are patchy and context dependent,
I think would be very helpful because I've finished up seeing that so many times, and I'm embarrassed when I have to say that again.
Yeah, there's another dimension of complexity here, Thomas.
No, in inequalities. Um, it's it's partly because terrains are rugged, like you say.
It's partly because there are so many multi-level causal mechanisms all interacting.
And that is mathematically complex. But there's also another aspect of complexity which I.
I really value your view on how I might help with this one, because I think it can.
Is the idea that certain findings and certain solutions are just politically unacceptable or ideologically unacceptable?
And I'm thinking of Margaret Thatcher pulping the Black Report, for example,
that the idea that you could say that poverty is a social determinant of help.
Rather than, say, people's behaviour. 80% of people's health is explained by their own behaviour.
So the second of those explanations, airbrushing out the fact that being poor might shape your behaviour.
And that's what we call value complexity.
Now, I'm fine about the mathematical complexity. I'm playing around with AI doing that, but I just wonder, could we bring in value complexity?
Could we get AI to start saying, rank these solutions or tell us where the political touch points are here.
Um, yes, a lot we have, um, I mean, just in passing the black report, of course, um,
it passed unnoticed at the time that it looked to health inequalities amongst men.
Women didn't really appear in it. Um, but, um, you didn't have any women in it?
No, no, I say it's about it's about inequalities amongst men. Yeah.
Yeah, it's it's worth it. Um, so, um, the the yeah, the, the I what I wonder whether you have, um, clusters of,
um, solutions that can be organised around differing ideological positions.
Um, uh. Oh yes. That, that might just be probabilistic.
but it'll be quite in going back to that point. You know that these normally are associated with these particular views.
So you would um, so that's a really good idea. And it could do that.
Yeah, it would definitely be able to do that.
Um, and um, so yeah, if you, if you could understand if you want to really go down a market driven set of solutions for reducing,
uh, harmful health behaviours, then you would be going down the road which says you want to make sure the competition
amongst providers and the information to consumers both drive opportunities to,
to, uh, eat healthily, for example, to gamble less, to drink less, to smokeless.
Um, and so that's the solution that says, um, and part of that is responsible ization in the present language.
Um, so what the, uh, tobacco companies did originally, what the gambling industry is doing today.
Um, what the food industry and alcohol industries are also following their playbook is to use responsible ization as a first step,
despite the fact that many of us might want to argue that the individuals making their choices over gambling addiction,
particularly addictive behaviours, are not in a position to exercise their choice responsibly and freely.
Um, and that that's shaped by, for example, availability of quality shops near where you live.
Um, the way in which supermarkets, uh, press certain products on you uh, the two for one office and alcohol.
Well, you know all this stuff, right? Um, the, the glass sizes.
Have you come across that one? Good, good study. Theresa. Motto in Cambridge, um, uh, this is the perverse results of research.
Right. Um, uh, the glass size when people were buying, uh, wine by the bottle in restaurants,
there was randomised buy days of the week and across the different restaurants, and they would be given different glass sizes.
And the ones that were given like, like neat little small glasses,
drank less wine than bought fewer bottles than the ones that had their kind of small buckets in front of them.
Uh, and we're kind of guzzling that down. Um, but of course, of course, that information is fantastically useful for public health policymakers,
but it's also very useful for volunteers who want to sell more of their wine.
So the results of that study seems to be that now, whenever you go into a Cambridge,
uh, restaurant, uh, the glass sizes are getting bigger and bigger. Um, and it just because you drink, you know, you don't drink more.
You say, well, just I'll just have one glass or whatever. And then anyway, you know, the point.
Um, the point is about this obesity genetic environment. Um, we do create an obese people.
Um, and, um, so just to go back to the clustering point,
there's a set of solutions that would relate to what you might call public health interventions addressing inequalities,
building on social justice and meaningful empowerment.
That's one cluster that would go together.
There's another cluster, I would suspect, which is about encouraging competition, encouraging responsible behaviour,
empowering individuals so that they can can behave responsibly, um, and using market mechanisms to give you better health outcomes.
And that's the kind of neoliberal cluster. I'm sure there's probably an authoritarian cluster in all of that as well.
Uh, well, actually the aids, the first response and oh, sorry, Covid,
they allegedly one of the junior ministers, um, uh, when asked about how they were going to deal with Covid.
The response was just a scare. The pants off them was the was the idea to terrify them so that they start to behave themselves,
which is, of course, as we know, are not a very good way of changing behaviour in the long term.
So how do we get on to this? We got into this because there are areas where it now seems we're pretty comfortable with how we are using AI.
Um, on, on these, you know, just improving the drafts that we've got and reviewing documents and a little bit of that.
We're a little bit more complicated on logic models, theory of change development, but actually it's pretty useful for first drafts on that.
And then this uncertain area where, um, the causal inference, normative assessment,
it's still really kind of emerging as to how comfortable we're going to be using it in that, in that space.
But, um, now that we've been thinking about normative assessment, You've got your new idea to take that.
Take that forward. Just looking at our time, we're doing pretty well.
Um, but taking a bit more deeply into what might be problematic, uh, in, uh, in this then.
Um, so, um, the transition from AI is a two under the supervision of humans to AI tools or functions that are more autonomous.
Um, and this is the superintelligence, uh, challenge, where, uh, AI agents are no longer given their parameters.
No longer take their prompts, engineered or not from from humans, but are taking it from other agents and are responding in that,
in that world and that that kind of creates, um, significant issues about evaluative knowledge you might have.
So your evaluation comes to certain conclusions. But when you're asked, how did you arrive at that?
You can't actually say how we got to that conclusion. Um, your answer is, is going to really be about probabilistic logic and about algorithms.
We use these algorithms to the extent that we know what algorithms were used.
These are the ones that were used.
And this is why we're getting the kind of results that we got that you would call algorithmic truth, right for evaluators.
We're also interested in surfacing differences of experience amongst service users.
We're interested in different notions of justice. We're interested in, um, differing accounts of what something means or different different groups.
Um, and that we would call deliberative truth.
I don't want to argue that one is necessarily better than the other.
I think the algorithmic truth is interesting, but it's not. It's probabilistic.
It is not the same as deliberative truth, which is what we normally talk about when we're talking certainly an evaluation.
What is the what? What are your findings?
Our findings are and you would talk about that kind of deliberative process in the hybrid world towards which we are heading.
Then those two, I think you want to be able to talk clearly about those two ideas without necessarily abandoning one over the other.
Um, so the um, yeah, the current discourse, I think, in sorry,
I should say for evaluators, this is um, it really is about optimising AI implementation,
and it's not really getting to grips with those deeper problems about the difference between deliberative truth and, and algorithmic truth,
um, large language models, which is the main there's not the only bit of AI, but it's the main thing that we are often using.
Um, the other thing that they do is that they present their findings with exactly
the same language as we as humans use when they present deliberative findings,
write the truth in their own way. So we are seduced into this false.
This is the the other bit that Trish was talking about, about using the word engineering.
We're seduced into a way of thinking which is not merited by the technology that we're using.
Uh, and, um, yeah.
So this kind of the key thing words for me, inclusive deliberation, interpreting meaning from different groups,
communicating to diverse audiences, um, and addressing what you think is the value or worth of the thing.
Those that sentence for me is kind of where, where I think the important, uh, pushback from an evaluation point of view needs to, needs to set.
Um, and it's not that we don't use the other stuff, but the the algorithmic truth doesn't get to those, those points.
Um, and, um, that's just this has, um, I think six I'm going to talk about, um, uh, particular challenges that it creates.
Okay. So there are kind of six little points are what there is now as we head towards our conclusions.
Um, the there is a problem if we arrive at evaluative conclusions and recommendations based on moral reasoning that we cannot understand.
Um, and we simply have to be very overt about that.
We can't, um, simply bring that into a conclusion, uh,
and treat it in the same way that we would treat a conclusion that we've arrived at through the collecting our own data, analysing our own data.
Interviewing people. Doing our ethnographic studies, whatever it is that we've done and formed a judgement, however imperfect that judgement is,
it would be our judgement and we are entitled to say this is our judgement and we are like and we put it out for peer review.
We're welcome. We welcome your thoughts on that. That is very different from saying this is the we've done this, this work.
We're going to be transparent and tell you that we've used these different AI agents in these ways.
So we're not going to lie about it. But then we we can't then say these are our conclusions.
You can only say these are the conclusions that that emerge driven by this technology.
Um, and the integration of AI into evaluative practice, um, will disrupt the traditional model of the responsible evaluator.
Um, and we need to be able to talk overtly about distributed agency.
What have we done? How do we do that? What is the AI done?
Which bits do we know about from AI? What bits do we not know? And then we can we can talk about distributed agency across that.
And I think that's fine in my in my view.
Um, the EU 2024 act, um, the EU, I mean obviously a particular problem that the EU and wouldn't be wonderful to be part of it,
but um, the particular problem of the EU is that you've got to get all the member states to agree before you get.
So it tends to be quite behind the times, the recommendation that that AI can really only be used if it's got human oversight.
That ship has sailed and it already had sail by 24, I think.
But um, then so there is a problem about how you use AI within and comply with EU requirements on that.
I think, um, and it's much more in the format that I'm talking about that we need to go forward.
Um, and and. Yeah. So they've got.
I think the EU has got themselves in a little bit of a pickle on that, as I understand it, which we might be able to use.
Um, and their knowledge, concentration and representation, this is I mean, one of the big issues is that, uh, and of course,
you can use prompts to try and address this, but AI agents are generally trained on dominant discourse in the global north.
Um, and, um, when you're looking at, um, minority views in the global North that you won't find those very,
uh, apparent, but also indigenous views are almost entirely absent from this.
So if you're if you're trying to understand the uptake of a health intervention in Hawaii and you're using only an AI,
a kind of standard AI agents, it will tell you that there's a lack of concordance, there's a lack of understanding.
There's a refusal to participate.
There's a kind of irrationality on the part of the local population, and they're not engaging with this perfectly sensible thing.
On whatever, whatever the health intervention is ignoring.
I mean, ignoring, um, indigenous understanding of health care.
So an example of that would be after one tsunami, um, the, the, the Western intervention was encouraged,
was actually pushing the idea of counselling, uh, step apart from the people, your family and friends.
You talk with your counsellor, uh, you go through the trauma. Um, and actually,
what was very clear is what they were saying is what I need to do when I'm
after a traumatic event like this is to meet with my family and my community,
dance and sing. Um, and that's how I heal.
And it's kind of like, oh, yeah. So there's, there's this, this kind of what counts as knowledge.
And how is that represented in in the way that we are working with, with AI?
Um, so as it says it can. It reinforces, um, profound asymmetries that are already there,
but it actually amplifies those unless you deliberately go about addressing those and undercutting that.
Um, however, what I don't want to argue this has been the kind of theme that somehow human knowledge is, um, absolutely clear.
And there's no there's no inequalities. There's no, uh, you know, that we don't we experts don't exclude indigenous knowledge and all of that as well.
That all knowledge is constructed and all social knowledge is constructed.
All human knowledge is constructed. What we need to understand are the different ways in which that knowledge is being constructed.
Um, and so in saying the first point that it's reproducing asymmetries,
I don't want to contrast that with this perfect realm of unbiased human knowledge.
What I want to do is to say, we've got, you know, that we need to think about these different forms of construction and,
and biases and how they and how they may, may work together.
But I do think it's very, very important. The shin point, um, which is that algorithmic truth is computed.
It's a probabilistic, you know, it's a probabilistic truth and it's not and it is rooted in sociotechnical systems.
And you have to understand that if you want to be able to know how you want to,
uh, to, to use, use it, which isn't to say that you shouldn't be using it.
You just need to know that that's what's going on. Um, and some people, I think, quite rightly raised the spectre of AI colonialism.
It's just like, um, without necessarily intending it becomes another form of colonialism or neo colonialism.
So where are we going next? Um, I mean, what I think.
I mean, you guys will be you guys that drive it. I mean, um, I think that, uh,
the main point is that we we can't meaningfully review conclusions when we've no understanding about those we arrived at,
and we have to be very open and overt about that.
And we can't take responsibility for the outcomes derived through that process when it's beyond our cognitive ability to do so.
Um, so the response in many quarters EU, uh, UK evaluation Society, um, to a limited degree.
Um, so this is kind of like the uh, is um, is, by the way, um,
introduced guidelines on using AI for reviewers when they're reviewing, uh, Nature research.
Um, and the kind of thing we have to be told. So, like reviewing proposals, it goes out for external review and it would seem some reviewers,
instead of actually sitting down and reading the damn thing or pulling it through AI anyway.
So the NIH is saying, don't do that anyway. Um, but, um, so it's a procedural solution.
It's one and it's not a bad start. You know, where you're just saying, um, that you, you, you, you have to be transparent and overt about,
um, how you adhere to specific ethical constraints and you produce and do all of that.
The difficulty is that that's I mean, it is very tempting, and that is important to aim for that.
It's very difficult now to deliver that in practice.
Um, because the procedure, the human control, human led procedures don't have sufficient grip to be able to control what the AI is doing.
Um, and so I think a better approach is going to be to be much more clear about the hybridity of what we're doing,
what is human, what is not, and how we combine those, um, in, in saying these are judgements arrived at one way.
These are judgements arrived at in other ways. Isn't it interesting that they align or they don't align or whatever?
Um, and then you have your deliberation using, using that uh, element.
Um, and of course a superintelligent AI will is, is whether if it does happen will be a major challenge to that.
Um, almost at the end now, but it'd be great to open up for questions five, five questions one is about kind of data and value inputs.
And that's those are the questions about who who's creating the data. How is that being done?
What cultural, geographic and social values dominate training application.
Um, and what analytic frameworks are going into that. And that's how do we not just by default allow AI colonialism to to operate?
Um, the interpretive authority filter is who participates in making sense of the data.
Um, it's very tempting for, you know, people like us, the experts sitting in the room just to make make sense of the data.
There's a really strong case for saying we just have to take the data back to the service users,
back to professionals in the field and say, this is what we're finding. Well, how does that cohere or not with your experience?
What what does that mean for you? And if once you find that out, would you would you change your behaviour?
Is that something that you think, oh, right. Actually actually I do need to change that.
So going back to the GPS and saying we've done the stats and I can tell you your behaviour is not reducing unplanned admission.
What what are you going to do about that. So um, then agency and accountability checkpoints.
Uh, and that's the thing about who did what when and just having and um, documenting that as you go through your, your your work.
Um, and then that kind of final alignment and transparency.
How does the final evaluative recommendations reflect kind of ethical standards, local values and clear accountability.
That kind of local values is really important. Um, how will the how do these findings resonate?
And in in localities up and down the country, the public health team in nice.
Years ago used to have the five WS, which is will it work on a Wednesday afternoon in Wigan.
Um, um, which is at IHI. You know, this is a great idea thought up by experts.
Uh, but actually,
will it be remotely meaningful on the ground when we try and make it work and that that five WS question maybe doesn't have to be wicked?
Um, that, um, is kind of another way of thinking.
Well, who needs to. Who do we need to consult about? Uh, about the meaning.
What meaning we can get to this. The final bit is that if you're going to be an evaluator in the future, you've just got to acquire these skills.
You know, it's not a it's not a matter of choice. I'm afraid you're going to be using this need to be able to talk to people that
are experts in using AI and and understand what it is they're telling you.
Um, it's no longer something that you can say, well, I hope it just passes us by or I hope that somebody else.
I hand that over to the AI team, the techie experts.
So at Rand, we've created a kind of AI expert team. And it's, you know, I think it will work.
Okay. But the worrying bit is that somehow in people's heads, they think, oh, those guys do AI and I do evaluation and research.
Um, and it it can't that isn't going to work.
So, um, the skills and capabilities and you're going to be good at this, you're going to need to have those skills as you develop your careers.
Um, is this a transformative moment? Um, a word transformation is overused.
But putting that to one side.
Um, I mean, for sure, the hybridity of human and AI intelligence, um, has changed the evaluation workflow where we started.
Without a doubt, that is now looking very different.
Um, it's quicker, it's more responsive, and it's got biases written into it because of the AI that we're using.
So but it's changed. Um, the skill sets that we require last change, we're not going it's not going to go back.
Um, and as Trish was saying, if you haven't done it, you've just got to go out and do a few hours training.
It's not, you know, it's not rocket science. Um, it's digital science.
Um, so it's like in 1971, the economist Simon, um, talked about, uh, the info glut, then just the glut of information.
And you said that there's the the problem today is not a lack of information.
Um, it's that we've got more information than we've got the ability to attend to.
The value is an attention. And back. Back in 71, he was saying people will pay for your attention.
Which of course is exactly what's happening now. Um, and, um, but it's a tension which is then very confined to what they want you to attend to.
Um, and that does pose problems for how we think of ourselves as a community and as a society.
Um, and, uh, yeah. And your ability to, to really, um, interpret, uh, a vast amount of data.
I mean, certainly I've been working in with EU policymakers a little bit.
Not a huge man in the last year or so. Um, what they constantly say is what they don't, what the last thing they want is more information.
They want to be able to make sense of it. Firstly, they want to when they need it because of the timescales, but they also want to be inspired.
That was the bit that surprised me.
They said we want we want research that makes us think of new ideas that we haven't thought of and that you won't get them from AI.
Interestingly, um, I agree with Tom, but I would I would challenge the idea that AI is good at the kind of routine stuff and creative stuff.
I want to get a t shirt with move 37 on it. You know that, um, example where the AI made a move and it was move 37 in a game of go,
and everyone who was watching the computer playing the human at go thought, oh, look, the computer's gone completely crazy.
It's just done this really stupid move.
Um, and all the go experts were saying the computer will lose, but actually it was totally transforming how the game was played.
And so the AI nerds will have t shirts saying route 37 and what they mean by that is actually really good.
AI can be incredibly creative only if you have the right kind of prompt conversations with it.
Um, and you said earlier on the bit about making connections across the disciplines because the AI isn't in a tribe,
whereas I only read the stuff that's produced by my own tribe. Um, and I mean, I'm just looking into discipline through AI.
I'm plugging systems I didn't even know existed, but also it will take stuff from there,
repackage them, and then come up with very novel, interdisciplinary ideas.
So I think one of the great advantages of using AI is in generating creative ideas.
So I was talking last night to someone from welcome, uh, on this very topic about interdisciplinarity, that their funding work in that space.
And I was suggesting one of the ways into that would be to take in any one discipline.
What is at the boundary of your discipline, where we're really coming up against problems that we can't solve.
And to open that question up to interdisciplinary engagement, uh, as, as as just as a way of structuring that process.
And I don't know the answer to that, but I think that strikes me as quite an interesting possibility.
And that would include bringing creative, uh, as well as other social research disciplines.
I'll leave you with two thoughts. One is that human the the radical incompleteness of human knowledge.
Maybe it's just like there is radically that any discipline will only give you a partial account of the world.
Any AI agent will give you a partial account of the world.
Any. Um, however, we we go about trying to understand the world.
There's a radical incompleteness, which is why we should listen to each other,
which is why we should listen to indigenous communities and the wisdom that they will have generated, which is so all of that becomes.
So once you adopt a slightly more humble position, um, and say, actually knowledge, there is a radical incompleteness to what we what we can know.
Um, but that kind of I think that opens you up to, uh, partly you become a bit more humble,
but also opens you up to being genuinely curious about what other people, other things might other approaches might tell you.
And the final thought I'll just leave you with, oh, no, it's not there, is it?
I've got there's some of the reading I've used is just there at the end of the. We'll share this presentation.
I was in Copenhagen last week and um, at an evaluation conference, of course.
Um, and, uh, so I'll just leave you with a kind of thought of Heidegger, which, to paraphrase him, he would say, use AI and you will regret it.
Don't use AI and you will regret it, too. Very good.