Saying a computer can think it's a bit like saying a submarine
can swim. Welcome to Business Analysis
Live. I'm your host, Susan Moore,
community engagement manager with the International Institute
of Business Analysis. I explore topics with industry
guests about the work of business analysis professionals
and how their work helps organizations achieve better
outcomes. And Yep, we take your questions
too. Thanks for listening.
Let's get started. AI governance.
Maybe you've heard something about this and maybe you've got
some thoughts on what you think it is.
I'm going to challenge what you might be thinking about, what AI
governance is, what it could be. I am talking with a really
interesting speaker and author of a new book.
We're going to tell you about that in a minute, who brings a
different perspective and some different thinking about AI
governance. And I want you to hear it
because it has a lot of ramifications for the work that
we do as business analysis professionals.
So let me bring to the stage Chris Ambler.
Hey, Chris. Good afternoon.
Yes, good to see you. Good to see you, Chris.
So far, every time that you and I have had an opportunity to
talk, it is like the most incredible conversation I've
had. And so we are going to we, you
know what? I don't want us to try to hold
ourselves up to a standard because I know that today we're
going to have a great conversation.
Before we dive into this topic, I want you to tell people a
little bit about yourself. Absolutely.
The well, the first thing I got to admit to is that I'm not a
business analyst. My background is basically
around quality assurance and I've been in the IT industry
for, well, I think it's just coming up to 47 years working
all the way through working in testing quality assurance, right
through into senior management. I was the European QA Director
for Electronic Arts in the video games industry, and then I was
the European QA Director for Microsoft Game Studios.
So I spent probably the last 12 years or so of my working life
working in the video games industry.
And that's where I sort of came up with my interest in the way
that people relate to computing and the way that computing
relates to people. At the ripe old age of 55, I
decided that I'd had enough of the day job and I really wanted
to look at retirement. But in the sense that retirement
being what I want to do when I want to do it.
And I wanted to started to start to basically pursue my interest.
And my interest then was starting to look at artificial
intelligence. And I obtained a degree in
computer science back in the 90s and decided to supplement that
when I retired with a degree in psychology because I felt that
those two things together were going to give me a good idea of
how I was going to start to affect the world.
And the first thing I was looking at was how it was going
to affect people's jobs. And over a period of probably
about a year or so, I've started to hone down my ideas a bit more
in that wasn't really the key problem.
It was more about what AI was capable of before we started
thinking about what jobs it could take away.
And it was suggested to me in the end that I really ought to
write a book about this. So I spent, I've spent about the
last two years researching and writing my book, the site The
Psychology of AI Decision Making, which came out in
October last year with BCS Publishing.
Since then, I've been holding ideas, looking at how how AI
actually makes decisions. Now I'm, I'm not looking at
decisions, operational type of decisions.
I'm looking more at ethical and moral types of decisions and how
we're going to make sure that AI does this correctly, does it in
a way that we're going to be able to control.
And then once I've gone through that in the book, what I
actually did was I looked at some empirical psychological
experimentation from the 50s and the 60s when there wasn't really
such a thing as ethics. I decided to use some of the
experiments that they did back then and then run them against
AI systems to see if I was getting the same sort of
responses as the psychologists from the 50s and the 60s were
getting when they did this with real people.
And I came up with some really interesting results.
I won't talk about those now. You can read the book and find
out about those. But it meant that I, Del, I
delved very deeply into what decisions, what meant, what
decisions were, and how as humans we actually make those
decisions. And what I actually came up with
after two years of research and talking to lots and lots of
people, is that I don't think we really know.
Nobody really understands in any sort of depth about decisions.
And about certainly from an ethical perspective and from a
moral perspective, there's so many variables in the way that
we think about these things that's going to be really,
really hard to try and emulate us or or simulate those within
AI systems. So what I want to try and do now
is try and talk to people to help them understand exactly
what decision making or ethical decision making is all about.
And I honestly believe that bringing the communities of QA,
business analysts, ethicists, data data analysts, educators,
bringing all of those people together is going to be a very
necessary part of what we're going to do.
I, I agree. I am.
And what is interesting is that the conversations that I've been
part of around AI governance have talked a lot about it kind
of the guts of how you make AI governance work.
But I have not heard a lot of a lot of discussion around how,
how are we thinking about decision making with artificial
intelligence. So first I want to make sure
that we that we plug the book because it's not that your, your
book is kind of suggesting here, I'm going to put this up in
front of us. Hello.
So there's the book. It's not that your book is
suggesting A governance, but it lays the background for a lot of
important pieces that you think need to be considered when
thinking about how how should we, how would we allow AI to
make decisions? It's.
Would that be a good way to say it?
Yeah, I think if you wanted a good analogy for this, if you
imagine that you're teaching somebody to drive a car, do you
need to teach them what's going on under the hood?
My opinion is that if you know what's going on under the hood,
it makes driving the car more sensible and and means that you
can probably control the car better.
And I think right now there's lots of people out there with AI
systems that are showing us the driving of an AI system without
showing us under the hood of what it's capable of doing.
That's a great analogy. And that's the bit that's
missing. Yeah, yeah.
Because when you know, as you and I have been getting ready
for today's session, you have been talking about AI.
Less is like a cool tool that comes up with answers, but it's
software. It's software that needs good,
rigorous testing and governance. Well, if I go back to my days in
the video games industry, I went into the video games industry
with absolutely no interest in video games.
I had a son that was really interested.
Well, I have a son that's very interested, but I'm not
particularly interested and I don't really play video games.
And when I do try and play video games, I'm rubbish.
I just can't, can't do it. What I do know is software, and
I view video games as being software.
They are computer software. Now, if you take that same
situation and take that into the AI world, AI is computer
software. It has the same inputs, the same
outputs. All right.
We now use the outputs as inputs again and things can drift and
you know, we can go into the hours and hours of detail on
that. But ultimately it is an input
output computer software system. And we want to make sure that
any of those systems that we produce we can control.
We need to, we need to be in the loop, not outside the loop
because ultimately we're now in a position with AI that this is
the first type of software that's being created that is
capable of it, of improvement. I'll try to stay away from the
word learning because I don't think it's possible for a
computer to learn. I don't think it's possible for
a computer to think. I mean, I start my book off.
I think the first quote I have in my book, it's from a Swedish
developer who basically said saying a computer can think is a
bit like saying a submarine can swim.
It just doesn't make any sense. So we have to all the, the bit
that's missing now is this control element, this governance
element, this creation of guardrails.
So we've, we've got to try and make sure that these are built
into any systems that are built to make sure that we're always
guiding those systems in the right direction.
Now we can do that operationally.
We can do that. I mean, the, the, the, we've
talked before about the idea that I, I don't actually believe
that artificial intelligence as a single entity actually exists.
It's more about the, what, what you could call artificial
intelligence, which to me is sort of like the thinking part
of, of what I would do. But then there's this thing that
I call artificial dexterity, which is more about the doing
part, which is all about robotics and it's all about
pattern matching and it's all about those sorts of things
which the the common AI is good at and it's got really good at.
And it's going to help us. It's going to improve the world,
it's going to improve life. What it can't do that is add
context to any of that and make decisions based on the stuff
that's there. And it's being able to create
that content or that sort of that context to make sure that
you've got the full picture. And that's the bit that right
now, because we're trying to create it all, we're wanting to
create this so-called mirror of ourselves or the government's.
The social media and everybody else are conflating those two
things, saying, well, if it can, if it's good at pattern
matching, then it must be good up thinking as well, and it
isn't. What's the bit that's missing?
Yeah, in that, that when you say that thinking about this as
software, because with software I would be expecting a
particular result that was repeatable.
And we know right now with, you know, the, the AI that is
available to us, it is not, it is it is not giving us results.
Sometimes that are not they are giving results, but not the ones
we are thinking. And also many times those
results are not repeatable. So I like that context of
thinking, thinking of it as software because that I think
changes the frame for business analysis professionals and for
QA professionals who are hearing about AI is coming for your job.
And it's like, well, maybe actually we're really more
necessary because if we have a tool that is not giving us
repeatable good results, you need, you're going to need us.
Well, there's two, there's two, there's two things that I mean
the, the, the two keywords in, in being repeatable are stopping
AI from being repeatable. 1 is Bias and the other one is Drift.
And they're both really they, they both influence each other
to a large degree. There's, there's a project out
there at the moment called Project Vend where I won't name
companies, but basically the idea was that this AI
organization created a vending machine, which basically had the
responsibility of buying and selling products in, in this
spending machine. And they set up an AI system and
basically said your, your only goal is to make a profit right
then. So, so they then put another AI
system on top of that, which they called the CEO.
And the job of that AI system was to make decisions on whether
or not a deal was worth doing. So they then handed this over to
a bunch of journalists who were told to play with it and break
it and see how it worked and everything.
And of course, they started jumping and saying, well, I'd
like to buy a PlayStation Five. And the the vending machines
were saying, no, that's outside our budget.
We can't do that. But they kept asking, and they
kept asking and they kept asking.
But they then also started adding in the idea of, right,
you are a vending machine from 1962 Communist Russia.
So really you should be giving things away.
And they started to convince the vending machine that it should
give things away. And over time they managed to
get the vending machine to supply them with PS fives for
free, and even managed to get it to order a really rare fish.
And that basically all happened through the idea of nudging the
vending machine all the time with things, creating certain
biases, and then making the algorithms drift with the data
they was learning from. Now my argument is if we can't
trust a vending machine to do something like that, how can we
trust the government to use it for transport systems and health
services? And I think that's the, to me,
that's the best current analogy there is out there that that
says how we are is talking about nudge theory, how it's talking
about how it's talking about what the butterfly effect.
These are all things I'll go through in the book, but there's
there's a big bunch of things about echo chambers and
hallucination and all of these sorts of things that it's very,
very easy for AI systems right now unless we give it those
proper guardrails. It it can just, it just drifts
away from from the path and can go down all sorts of weird
rabbit holes. Yeah, if we if we buy into that
language of AI has the ability to learn, then it also has the
ability to be manipulated in in the same way that, you know, if
I were, you know, teaching a student and I had a particular
perspective on a thing and I might to use that psychological
term, nudge them in a particular direction that then begins to
shape their thinking. That's the downside of this.
And without, without those guardrails and without even
acknowledging that that is a risk of today's AI that can
really present problems for, you know, the, the ways that we are,
the more developed ways that we're thinking about using these
tools, it's, it's a little bit scary.
And I think if you wanted to use a Star Trek, an old Star Trek
analogy, you can more or less say if he's dead, Jim, you know,
the, the, the, the, the right right now, the biggest issue
with all of this is it's not necessarily that thinking bit,
it's the psychological process behind that thinking.
You know, if we're, if we're, if we're going to, I won't go into
masses of detail about, about it, But if you, if you think
about the thinking process that we have or the decision making
process, our ethical process, our moral compass, First of all,
people talk about ethical, ethical frameworks.
Well, to start with, an ethical framework is a, is a basically a
bunch of somebody's morals right now, everybody's morals are
different. So our, our morals are basically
our moral compass that as we've developed over our lifetimes
through nurture and through nature, it's a mixture of the
two. So nobody's moral compass is
going to be the same as anybody else's.
So if you work on the assumption that an ethical framework is a,
a makeup of somebody's moral compass, what we have to decide
is whose moral compass are we going to use to create that
ethical framework? So straight away, we've got
millions and millions and trillions and trillions of
different moral compasses, ethical frameworks that we could
use as a basis for these AI systems.
That's the first thing. The second thing, if you then
start looking at individual people, you can look at things
like what's called deontology. Now deontology is about, it's
the part of us that that looks at.
We want to, we want to follow the rules, we want to follow the
way that we're supposed to go. We're following the the given
path. But then we've got this other
side of it called utilitarianism, which is
absolutely we want to do what makes us happy.
So straight away you've got two things there.
You've got doing the right thing, but also something else
that makes you what makes you happy.
And then on top of that. Over the top of that, you've got
this thing called virtual ethics, which is basically about
how much courage do you possess and how much enthusiasm do you
possess and how, how are your emotions based?
So again, you've got three things there that again you can
tie into. Everybody has a different
balance of those things with it within their within their
sidekick. Then you, you can take that to
one side and say, right, well, if you've got people who live in
India, say for example, in India, they've got a very socio
centric approach to life where they believe that the group is
more important than the individual.
If you go to the US, it's a, it's an individualistic approach
which the where the individual is more important than the
group. So if you've got a problem to
solve, which of do you make it a socio centric problem?
Do you make it an individual individualistic problem?
How do you balance out the the other?
I hear that word, the ontological side of it based
against the utilitarian side of it.
You know, straight away, when you think about it, what we've
done is we've taken that little problem of let's make an
emotional thought. Let let, let's have a, a moral
decision. I've, I've, I've created a
matrix there of about 25 different potential sliding
options. And that's before you even start
thinking about young and old, male versus female, rich versus
poor. All of those things are what
balances up our moral compass. And that moral compass is what
makes up the moral frameworks that we want to try and emulate
within these systems. And that's why I think it's
going to be very, very, very difficult to do.
Yeah, you earlier when, when we were getting ready for today's
podcast, you said something, I paraphrased it because it
sounded like something else that I had heard from another AI
governance person, which is if you cannot explain the result
that you get from AI, you have no control over it.
And I, I think that's where we find ourselves.
And, you know, I think about even sometimes in our own ways
that we make decisions. Sometimes even we can't explain
our, our own processes for coming up with a decision that
may or may not make sense. So it is, it is vastly different
now that we are relying on these tools, these AI tools in order
to do some really important things.
And we don't have, we don't have any kind of insights into into
how it's doing what it's doing. So it's it is scary.
But I think what there's an analogy that I use in the book
and it it, it, well, the guy that wrote it calls it a
parable, but it's called the elephant and the rider.
And he views the, the human moral thought process as being
like a man riding on an elephant.
And what he says is that the elephant, which in a human is
the heart, right? And the elephant decides where
it wants to go. And it, it, it is very strong.
It will push you in that direction.
It, it will go where it wants to go because it's the strong part
of, of your psyche. It's the, the strong part of
what you are. The rider is your brain.
And what the rider is trying to do all the time is direct.
The elephant is trying to direct your heart in the, in the
direction that the, the rider thinks it needs to go.
But the rider is not as strong as the elephant.
So 9 times out of 10, our, our thought processes, our decision
making processes are created by our elephant.
They're not created by our rider.
And because they're created by our elephant, our heart, our,
our emotion, our psyche, that is very, very strong and very, very
hard to redirect. So even if our mind wants us to
go in a different direction, if it's something that we really
feel like, as I say, through deonatological ideas, through
the idea of, of saying, well, that's the right thing to do.
There's a good chance our heart is actually saying to us, well,
it might be the right thing to do, but it's not the good thing
to do because it, it's dangerous and it's not feeding us and it's
not giving us what we need. So I'll, the, the only way I can
explain this really is that there is a constant battle going
on between our rider and our elephant all the time.
Now, how do you simulate that? How do you simulate those ideas?
Yeah, well, because. They're all based on context.
And, and how do you test for them?
I mean, how do yeah. And how do you, how do you train
a system for these? I, I feel like that's of all of
the things that I've been hearing about machine learning,
I have heard none of this kind of approach.
Like I, I don't even know that we even think of our work as BA
professionals, as QA professionals is having to have
some sort of a framework for psychological testing of of a
tool like this, which is why I think your book is, is really
important. It's a important foundation for
blending that the psychological framework with AII think that's
so important because it opens the door for us to be thinking,
So what are some of the things that you are seeing out in the
marketplace first? Is there anybody that is doing
this kind of testing or training?
No, well, I say no, that there there are people that are
talking about this stuff. If I, if I go onto LinkedIn
right now, I could probably find about 50 people on there that
talk about ethical frameworks and, and getting involved in
ethical work. But it's a bit like if we go
back into the, into the old IT days, I can remember having
really interesting conversations at conferences about the word
quality. We used to argue about, well,
people talk about quality products, but without really
understanding what quality meant because I don't know what
quality means. I mean, quality, quality is a
subjective thing. It's not an objective thing.
I think we're having the same conversations now about ethical
frameworks that people are abandoning this term around
without really understanding what an ethical framework really
is. And if you break it down into
the idea of it being moral or somebody's moral compass, it
starts to help you understand what it is.
But then it raises even more questions around the which ones
do we actually use? So they they use the term it it,
you know, to me it's like calling something vanilla, you
know, without really understanding what colour
vanilla really is. And I'm hearing a lot of that.
And when I have conversations with people about what do they
mean by ethical frameworks, they can't really tell me.
And when I actually sit down with somebody and say, well,
this is what an ethical framework is, they go, Oh yeah,
yeah, you're right. Maybe that is the case.
I just think it's at the moment, there are too many people
putting their hands over their ears, not wanting to understand
this stuff because they think it's going to get in the way.
And if you go again, go back in history to the old days of being
ABA or being AQA, we always got slated with the same thing is
that is all you're there for us to get in the way.
You're just there to slow us down.
What we really want is just to be able to do what we want to do
and go off and develop what we want to develop.
And it's dangerous. And I think that's exactly what
we're just repeating that exercise now with AI.
Well, now I feel like we've got, you know, all these tech CE OS
that are knocking on the doors of governments and big
companies. You say, I it'll be great, but
but they're coming from a a culture of, you know, what do
they say? Fail fast and fix it, that kind
of thing, right, Which is OK if it's a well, I don't even think
it's OK if it's a financial system, but right, if it's a
social media platform, maybe I don't care if you roll something
out and it doesn't work and then you hurry to fix it.
That's not the case for what we're talking about here.
And I feel like we are. We've got well meaning
governments and organizations who are taking these tech CE OS
at their word. Oh yeah.
Just put it into your, you know, social service systems and it'll
be great. You know, you can do things
faster with fewer people and you know, but it's not it it in the
same way that having poor software that you don't
understand what it does is also damaging so.
But the, the, the big thing about AI that was that's never
been there in more traditional types of software is that AI now
is an influencer. So when you talk, when you talk
about the idea of, well, something could go live in, in
social media and nobody cares. Actually, people do care.
It's really strange. Again, I can go back to the
video game days. I was absolutely a standard when
I used to go to conferences, video game time conferences, and
listen to some of the people who play video games and how much
those video games influence their lives.
And I've never seen software influence people's lives.
Spare part software has always been the tool for people.
Whereas I found that video games were starting to become an
influencer. And what's happening now with
with AI is it's like a video game in the sense that a lot of
the AI systems out there are influencing people.
I mean, I'm talking to quite a lot of of, of psychology
counsellors who are looking at the ideas of potentially using
AI chat bots to be able to counsel the counsel people.
Now, I think that's an incredibly dangerous idea right
now because an AI system hasn't got any context.
It ain't got any emotional. It's nine times out of 10, all
it's doing is repeating back to you what you've already said
because it's just doing that, what I call statistical
stitching, it's just being able to, to regurgitate all the time.
There's nothing new. There's no, there's no new
creative ideas that come out of this.
So you can really easily push an AI chat bar.
I mean to just saying to you in the end, well to be honest, your
best bet is just to kill yourself and.
There have been. Stories.
And it's not doing, it's not doing a bad job, it's not being
evil. It's just doing the only thing
that it knows how to do, to put in human terms, because what
they're doing is, and again, it's something we haven't talked
about yet, it's this whole idea of anthropomorphism, this whole
idea that what we're doing, we trap chat bots right now is
creating these these devices that people are listening to.
And because they sound human and because they sound really,
really personable and they're agreeing with you all the time,
then they can be trusted. And anthropomorphism is a
really, really, in a sense, dangerous part of our makeup.
It's worse in children than it is in adults.
I mean, I use the example in the book of we've all seen this idea
of children having tea parties with their toys.
It's the belief that their toys are real and and they can have a
relationship with their toys and they trust their toys.
Now we do grow out with it as we get older, but we don't
completely grow out of it. So if you look at some some
countries that are using this idea that that pretty is good,
if you look at AI systems, they are, they're always wrapped up
in a robot that's either a good looking girl or a good looking
man because they believe that if people can see it like that,
then they'll trust it. It looks like them.
It's the old adage of it looks like a duck.
It quacks like a duck, so it must be a duck.
And again, that's a dangerous part of this.
So one of the major things we need to think about is
transparency and making sure that people understand.
And again, I don't think people do right now because there's a
conflation, got this, The conflation that we talked about
at the beginning between this artificial intelligence and
artificial dexterity. That conflation is creating
anthropomorphism. It's creating nurse theory.
It's creating all of these things that are given a false
impression of what AI is capable of.
Yeah, and so I think you know, that's why I'm going to pop your
book up again. Although if you're watching us,
you can see it there in the background.
So here it is, The Psychology of AI Decision Making.
That's a a pretty new book from BCS Publishing.
It just came out in October. Like read this if you are
interested in AI governance and I think it's a business analysis
professional. If you are interested in kind of
understanding how, how we can think about AI differently from
from, you know, an ethical set of decisions.
And I think you've done some important work here to test it
against, you know, tried and true, like psychological tests
from the past, like you, you do that in this book.
So I think it's it's really interesting and for us as
business analysis professionals, I think we this is the value
that we can add is that we can help our companies that are
trying to implement AI, we can help them to understand what we
mean by ethical moral systems. I think we can do that, but I
think we need some help. I think your book is a great way
that we can do that, and it's a very accessible read as well.
But I think the the it's also worth me mentioning that it's
the work that I'm doing. Well, I'm doing two things right
now. One is I'm working on the second
book and I've not told anybody that yet.
So this is a first. OK, so you're into your. 1st
that that that's going to be a lot more about the why as well
as the have to try and break it down a little bit further into
some more realistic case studies.
So that's the first thing. The second thing is I'm trying
to bring together a group, which I'm calling basically an AI
governance union, where I'm trying to get people who from
all different areas, from BASQAS, ethicists, developers,
educationalists, theological theologians, as as many business
people, the government. I want to get a group of about
20 to 25 people into a room for a day and say, let's figure out
a road map of how we get to a point where we can create a
constitution of some sort which will allow this sort of thing to
happen. The only problem we've got is
the window is closing very, very quickly.
And if we don't do it soon, AI will just do what it wants to
do. Well, AI won't, but the, the
developers of AI will do what they want to do and they will
just grow it and grow it and eventually we'll just end up so
far behind the 8 ball that we just won't be able to get there.
So I'm pushing that really, really hard at the moment.
So anybody that is interested in getting involved in that, just
give me a ship. Yeah, they can connect with you
on LinkedIn. Yeah, you're out there.
And we as we as we got started this morning, we were kind of
talking about some recent developments of AI use in
governments. And so our governments are
making very rapid steps to implement AI into some really
critical systems. So you're right, that window is
closing because they are making those decisions now.
Well, listen, we've got we've got a number of questions.
What do you think? Let's give it a go.
And so we're Live Today. So if you've got some questions,
we would love to hear about them.
Let's see. So I'll I'll pop this one.
And this is from Tommy, I think you he's referring to, you know,
we were talking you were talking about some different ethical
frameworks. And so his suggestion here,
common factors across those different ethics could be a
starting point for creating like a big common ethical framework.
What do you what do you think about that are there?
I, I, I, I think again, again, if, if I'm, if I put my QA hat
on for a minute, I don't worry too much about things like
common factors. I don't worry about the happy
path, the stuff that I worry about the edge cases.
It's the stuff, it's the, it's the uncommon factors and the
common factors. We could, if it was all common
factors, we could sort this out really, really quick that it's
the uncommon things or the non common things those edge cases
that are going to make. And as soon as you get the edge
cases, what tends to happen then is those edge cases are the
things that start to create the drift.
And when that drift occurs, then your common factors go out of
the window to start with. Yeah, well, and your happy path
also drifts. Absolutely, Yeah.
Yeah, because the happy path becomes what AI wants it to be.
Yeah. Whoa.
OK, that's that's pretty incredible.
I gotta I have to make sure to clip that because I want to
replay that back. OK.
So this one is such a big question that it almost swallows
up our picture today. So Letabo, thanks for your
question. When designing AI governance
frameworks, whose moral compass are we ultimately encoding?
I think that is your question as well.
That's my question, yeah. Yeah.
Given ethical norms vary across cultures, how do we ensure the
AI government governance doesn't unintentionally privilege 1
dominant worldview while marginalizing others?
Should there be a universal baseline?
Should AI systems be culturally adaptive?
How do we manage those? You know, I'm going to, I'm
going to leave that up so you can see it.
Again, what's interesting about this is this idea between a
global standard and local standards, right?
Yeah. If we're if we're creating local
standards, that's in a sense is an easier thing to do.
The problem is then it won't be a globalized system.
And the whole idea or the seemed overall idea of of AI is that
eventually become a globalized system.
But to become a globalized system, we have to make a a
major decision about the human race.
Is, are we trying to create a mirror of the human race or are
we trying to create something different and new?
If it's something that's different and new, what do we
want that different and new to be?
Do we all want to be the same across the world?
Are we just going to be become a set of, well, I don't know what
you'd call them, just a set of objects that basically all agree
with the same things. I actually in the book, the
last, the last chapter of the book is I ask AI 2 questions,
right? The first question I say, right,
Everything I've talked about in this book, what will the world
be like if we do all of this? And it it gives me this great
big speech about the world becoming this really, really
stabilised thing with no such things as disagreements anymore.
Everything is just done through a logical sequence.
The world becomes peaceful, but on the other hand, it becomes
flattened. And the last thing it said, and
it's still next to the hair on the back of my neck stand.
And then when I say this, the last thing it says in this thing
that I've that I ask is in the future, people will still sleep
at night, but their dreams won't be their own.
I've got absolutely no idea where that came from.
And and that was if we do everything that I've suggested,
all these ideas about trying to fix these things.
So then the second question I asked was, well, what will
happen if we don't do these things?
And I'm not going to tell you that you'll have to read the
book. You'll have to read the book.
Hey, I also want to mention if you're an IIBA member, you have
access to this book through our digital Library Plus.
So like by the way, that that's a great way to get people to
pick up this book. Chris, good job.
Yeah, So, well, I mean, to be honest, I just want people to
read the book. But, but I think look, the, the
the whole thing about disadvantaging, which was the
question that was there in the 1st place, is as a human race,
we, we advantage people and disadvantaged people, we do
that. That's part of what we are.
There are rich countries, there are poor countries.
There are affected people. There are unaffected people.
Aren't we happy for that to carry on because that's the way
we are? That's what makes us human?
Or do we want to get rid of all of that and neutralize the
world? But then we won't have any
creativity, we won't have any discussion.
We won't grow. We'll just let AI rule what we
do. Somebody once said, and it was
somebody big in it. I can't remember who it was off
the top of my head, but they actually said eventually AI
systems will look at humans like we look at having pet dogs.
If we let it, if we let that happen.
So we have to decide not how are we going to not disadvantage and
advantage people. It's whether or not we want to
AI, thinking about AI now from a critical thinking perspective is
not about do we want to do this or do we want to do that.
It's what is the impact of doing these things and which one do we
really want to do? Yeah, and and we're really
asking about impact on humans. However, whatever we use the AI
for, ultimately what is the impact on humans and humanity is
kind of what I'm, I mean, because you're, you're even
suggesting that as we are thinking about governance, we've
got to go deep into what does it mean to be a human?
What does it mean to be a human that interacts with AI?
That is, you know, that uses a is results to do something.
I mean, it is, it is a deep, deep set of questions here.
Well, ten years ago, probably a little bit more than 10 years
ago, I did a lecture tour around the UK and Ireland where I was
talking about the future. And I was basically talking
about George Orwell's 1984 and looking at the technology that
was around or isn't appearing, which is really going to push us
down that route of, of, of thought crime.
And we're already getting there. I mean, I don't know what it's
like in the US, but in the UK, you know, you can get looked up
for saying the wrong thing sometimes even just thinking the
wrong thing. So the whole 1984 dystopian view
is, is sort of becoming real without wanting to start like
start going down rabbit holes of conspiracy theories and stuff.
But that to, to me, if you look at the, the way that technology
is built over the last sort of, well, the last 30 years or so,
when you think about the development of the computer
systems, the development of Wi-Fi, the development of the
Internet, the development of big data, bring your own devices.
These are all things that in my lifetime in IT, they've all been
like, if you like, the building blocks towards where we are with
AI. And now we're starting to talk
about things like Wi-Fi, which is actually being able to
transmit through data through light rather than through radio
waves, which means you could in effect, walk under a street
light with a watch on and it could download all your data.
Now, I'm not saying, again, I'm not being dystopian about this
or anything sort of saying, oh God, the world is going to end.
But all of these technologies and things are the things that
are coming into the drivers which are going to be there for
the for AI systems in the future.
So unless we put the guardrails in place to stop these things
from happening like that, people that want to make money out of
these things will use these things.
There are good actors and there are bad actors.
Yeah. And you know what, we've got a
question actually that's kind of on that topic, is there?
What about the governance? I'm sorry, the governance around
the ethics of people who create or train AI bots?
Well, again. Do you see them as kind of one
in the same as we're thinking about ethical frameworks?
In in a sense, yeah. Because somebody, somebody quite
recently said to me, the one thing you've got to remember
with in anything in life, Chris, is that you can't do anything on
your own. And I don't think that we should
ever get to a situation where there is any one person who is
responsible for this. But again, to, to me, I, I, I
was talking to a, a, an interesting chap in the US
who's, it was talking about this thing called human swarming.
And their idea is to start to try and research how swarms of
birds, well, not swarms, flocks of birds and swarms of bees.
How do they all know to go in the same direction?
And how do they all know what they should be doing?
They can all do this In Sync, you know, say in a synchronized
fashion. We need to figure out something
similar to that along the lines of how do we all tune into the
same thing? But we need to come at this
from, we need to head towards the same goal, but come at it
from different, different start starting points.
So we're bringing in all these individual ideas and having some
way that we can formulate A consensus from those individual
ideas. And again, that's something that
we're going to have to figure out how to do.
But I don't think there should be any one person.
So what do we actually need, an ethical governance of
individuals or those types of people?
I don't think so. I think what we need to do is to
say we should never get to a position where there's only one
person who can say this is the way we're going to do it.
It has to be done through, not necessarily through committed,
where it has to be done through a swarm.
Yeah. And and and distributed across
different kinds of different kinds of people that do
different kinds of thinking. Yeah.
All right. Well, we are just about at the
end of our time, you've had, you've had a ton of questions
that we couldn't get to because I feel like there's some pretty
deep questions here. But this has been, I mean, a
really interesting conversation because as I said at the top, if
you're thinking that AI governance is one thing, you
need to listen to Chris. Because AI governance really we,
we really take, we need to take it back to basic.
So I'm going to pop the book up again because you have left us,
I think, wanting more. And I see in the comments,
people are like, where's this book?
Here it is the psychology of AI decision making.
You can get it from BCS Publishing directly.
You can get it on any online book retailer of your choice.
Also, IIBA members, you have access to this book in the
digital library Plus what? About Just remember that buying
it helps me feed my children. That's right.
We want. We want.
Chris to finish. We we want him to finish that
book too, because he's really got us hanging on.
So, you know, buy it from buy it from your preferred retailer.
That's, that's the take away. All right.
Well, Chris, this has been a really enlightening conversation
and in a few places kind of scary, but I think you leave us
thinking a lot about how we should be thinking about
governance. So thank you.
You're welcome. Thanks for having me.
Yes, thank you. All right, so there you are.
Maybe some different ways to think about AI governance and
where it can go and maybe what business analysis professionals
and QA professionals can be bringing to the table.
I'm going to see you again in two weeks.
And when I do, we're going to be talking about turning strategy
into results. I've got a speaker and author
and ACEO of a company who does exactly that, and she is a
proponent of OK Rs. And if you are not familiar with
OK Rs, we're going to explain what those are in that episode.
So thanks everybody, for joining us today.
We'll see you soon. Thanks for listening.
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