Reed, welcome to The Road to Accountable AI.
Thanks for having me.
Tell me a little bit about how you got into this field of AI ethics, because
relatively speaking, you've been doing it for quite some time.
That's good.
Relatively speaking, I've been doing it forever.
Yeah, I mean, there's sort of a grander story, I suppose, but to sort of keep it
brief, I was a philosophy professor specializing in ethics.
I had the idea for an ethics consulting company many, many years ago.
I didn't see any kind of market for it.
Fast forward, I heard some things.
I thought, okay, maybe there's a market for this actually.
And so I left academia and I started an ethics consulting company.
with the intention of making it focus on emerging technologies and perhaps AI in
particular.
I didn't know how quickly the AI space would grow.
Thankfully, it grew quite quickly.
And so I started advising and consulting on how to do AI responsibly, ethically, at
least not negligently.
That's the goal here.
What does the academic training philosophy bring you in this kind of applied field?
Well, before people called it responsible AI, they called it AI ethics.
We're talking about the ethical implications of AI, ethical, reputational,
regulatory, and legal risks.
If you want to know something about AI ethics, you better know something about
AI, and you better know something about ethics.
And I know a bunch about ethics.
That's what the PhD and the 10 years of being a professor and the whatever it was,
15 plus years of academic research and publishing do.
So when people make certain kinds of claims about responsible AI or AI ethics,
frankly, a lot of times they just don't have a good grip on the ethics of things.
And so they say things that are false, misleading will lead them down wrong
paths.
And so having that kind of experience, understanding, frankly, expertise under my
belt means that I can navigate the conversation really well.
I'll give you one quick story to sort of put some meat on the bones of that.
When I very, when I was really just early transitioning from academic life to my own
company, I joined IEEE, that's the standardizing organization for engineers.
because they were working on this project called Ethically Aligned Design.
And it was about how do we align, what do they call it?
Autonomous and intelligent systems with our human values or something along those
lines.
And I got myself onto this committee that was in charge of figuring out what are the
guidelines or what's the advice around research and design of AI from an ethics
perspective.
And I got on the first call and I was anticipating not being able to say
anything because I thought these are a bunch of engineers.
They, I don't know.
I don't really know anything about AI.
I'm not an engineer.
I'm not a computer scientist.
I'm not a mathematician.
I'm an ethicist.
So I expected it to be a sort of meeting in which I just sat back and listened to
the experts.
What I learned within the first few minutes is, oh, this isn't some advanced
engineering discussion.
This is first semester, first year students talking about ethics.
They don't know anything.
They're engineers.
And...
What wound up happening to my sort of amazement is I wound up essentially
leading the entire conversation, just leading because they were just saying
things that, you know, my first year, first semester undergraduates used to say
about ethics and they were just fundamentally confused.
And so I went in there and I sort of led the conversation and had to clear up a
number of confusions, answer a bunch of questions, direct the discussion in way
that would actually be fruitful.
And so it turns out that it's really the ethics expertise that's more useful than
the AI expertise in a lot of these conversations.
Well, you know, not necessarily from that specific example, but what are some
misunderstandings that people tend to have if they don't have some real background?
Well, one would explain, I think, the transition from talking about AI ethics as
was the standard to talking about responsible AI as has become the standard
among corporations.
They just don't know what the heck to do with ethics.
The number one misconception, which I opened my book, Ethical Machines, with
this is they just think it's squishy, it's subjective, it's just a matter of opinion,
it's how you feel about stuff.
And if you think of ethics as just how you feel about stuff, you know, I like
chocolate, you prefer vanilla.
I like genocide, you prefer peace.
If we think of it along those ways, then of course you're not going to get your way
to actionable steps to identifying and mitigating the risks.
So unless you think about ethics as something that's more just touchy -feely
stuff, you're not going to actually get your way to designing a framework,
designing policy, designing procedures, designing metrics, designing training.
Otherwise you just tell everyone to consult how they feel about stuff.
That's not, that's silly.
And so,
Getting rid of that misconception, articulating that ethics is or at least
can be conceived of as being perfectly objective, and that there are ways of
doing ethical inquiry that are responsible versus irresponsible, that there are ways
of articulating it in concrete ways that lead to action, that's a phenomenally
useful perspective that most people simply don't have.
Let me just play devil's advocate a little bit more, though.
People talk about responsible AI, but nowadays people also talk about things
like AI governance or AI risk management or AI safety, which are all terms that
don't necessarily have that kind of ethical loading.
Do you think that's misguided?
Should we still be talking about ethics or are these different things?
So I've argued, this is a point I make in my book, that we shouldn't use the term
responsible AI, that the term responsible AI is a term that was invented by
corporates essentially, so they don't have to say the word ethics because they don't
want to deal with it.
Incidentally, this is in line with some advice that I got early on when I was
still a professor.
I was talking to an executive at Starbucks and she said, not unreasonably, you might
not want to say that you're doing ethics consulting.
People don't like the word ethics.
They don't get it.
And I said, no, no, I'm just going to own the word ethics then.
If that's the answer, I'm just going to own the word ethics.
And I changed it incidentally to ethical risk because I thought risk was a language
that businesses understand.
Now, look, there's no principled reason to oppose the term responsible AI.
It's not as though that's completely bonkers from a ethical or a conceptual
perspective.
That's reasonable.
I think though, this has been my experience that...
What happens when people use the term responsible AI in practice is that they
throw in ethics -y stuff into the responsible AI bucket, but then they throw
a bunch of other stuff in as well.
So they'll say, yes, responsible AI is ethics and safety and regulatory
compliance and engineering excellence and cybersecurity.
It's just all the stuff.
And then what happens is they say, okay, now let's address the responsible AI
bucket.
And then the experts in the room are experts on things like...
engineering excellence and cyber security and regulatory compliance.
And so what do they do?
They prioritize all of those things to the exclusion of talking about the ethics
stuff.
And so I think that they miss out on huge swaths of risk because they've decided
let's just, you know, we're focused on responsibility now and these things within
those buckets.
And so I think again, while in principle there's nothing wrong with it,
pragmatically or in practice, what happens is that ethical risks get pushed to the
sidelines.
Yeah, it's fascinating.
I mean, I'm a professor of legal studies and business ethics, and we have for a
long time had a lot of the same conversations more broadly in terms of,
you know, what does it mean?
How should we talk about it?
You know, what's more effective practically in terms of getting businesses
to change their behaviors?
Do you think these conversations that we're having about AI are really just a
microcosm of the larger issues for business?
Or are there some things about AI that are really distinctive in these discussions?
Probably a little bit of both.
You know, I think that...
Sorry, just a quick...
I'm getting some feedback.
I can hear myself a little bit.
Is it coming out of your speaker and back into your microphone?
I could put my earbuds in.
Yeah, I'm not hearing anything, but.
Yeah, it's probably on my hand.
OK.
It's coming out of me.
Yes, it might be.
Let me just.
this connects up with the headphones.
But yeah.
Can you say something?
Sorry.
Okay, yeah, now I'm hearing you in the earbuds.
Okay.
Let's let's try that.
So now, now I can hear you in the earbuds.
Hopefully that'll.
All right.
I can't even see your earbuds.
That's good.
I guess I can see them barely, but if I'm, if I'm really looking.
So, okay.
So let's say you asked the question and obviously this look at edit out, you asked
the question, is this sort of microcosm for ethics and business or is there
something special about AI?
Yep.
Um, so it's a little bit of both.
I think that one reason why the ethical risks of AI are much discussed is because
it's of the nature of the beast that is AI.
machine learning that certain kinds of risks are quite likely to be realized
despite the best intentions in some cases of the people designing the AI.
So we get things like biased AI and black box AI, we get privacy violations and
those kinds of ethical breaches are not usually the result of bad actors, of bad
apples.
And so since we've got this powerful new tool, people want to use it for innovation
to drive revenue, to drive internal efficiency, et cetera.
But it comes with these associated risks.
We naturally get this kind of conversation around risks.
I also think that there's probably some contingent story to be told about why
people are paying particular attention to this technology in ways that people don't
pay attention to ethical breaches by corporations more generally in a way.
So perhaps there's some story to be told about how the people in the technological
space, they tend to be younger, they tend to be more activist -y.
And so you get the overlapping diagram of being a technologist and being sort of
activist -y, social media active, social justice warrior.
And since you get that overlap of the diagram, you get as a matter of sort of
historical contingent fact, lots of attention on the ethical impacts of AI.
Yeah, it's interesting.
And obviously we've seen a lot of evolution in terms of adoption of AI and
awareness of AI in the last several years.
Have the kinds of questions that companies ask you or the things that they bring you
in for changed?
I wouldn't say that they've changed radically.
I think that the frequency with which the conversations are had, the frequency with
which people want help with this sort of thing has increased over the years.
There are certain kinds of risks that have popped up very high on the radar as of
late, specifically since ChatGPT came out.
Very much increased concerns around cybersecurity or the security integrity of
their data.
That's been a major issue, of course.
And then, of course, the hallucination issue that ChatGPT or other models will
output just false.
false statements.
So I think, you know, if I said, you know, what are the big risks that have come out
lately?
It'd be the cyber risks and the hallucination risks.
The bias risks that are associated with generative AI, those have been around
since we've been talking about AI ethics at all.
So that's not really new, but it's perhaps exacerbated or, or especially now that it
comes in visual form, it's captured the imagination of people a bit more, but
nothing has fundamentally changed.
I don't think it's just awareness has grown.
It's just that awareness has grown.
So in terms of generative AI, you mentioned the hallucination potential.
How should we think about that from an ethics standpoint?
You're on mute now, it looks like.
Sorry, I muted myself by accident.
Not about.
The hallucination problem is not tremendously, no, I was thinking something
else.
The hallucination problem is a problem both for the technologists and for
ethicists.
So, one, everyone wants accurate AI.
Nobody wants things that are said that are false.
That makes it useless.
So, one, there's just a sort straight -up technical problem.
It only veers into the lane of the ethicist when it's giving outputs that are
ethically significant or can have an impact on, say, the livelihood of people.
So, if it's hallucinating about where you should put your money, let's say it's
acting as a financial advisor,
or it's hallucinating about what the appropriate diagnosis is, or it's
hallucinating about whether you can combine these two kinds of drugs when
you're asking about your prescriptions or something along those lines, then the
ethical impacts are serious.
So there's a way in which, you know, I think about the ethics of hallucinations,
I think I think this, as something akin to the ethics of bridge building.
You know, when you build a bridge, you don't want the thing to fall apart,
otherwise it would be a useless bridge.
Of course, if it falls apart, if the technical aspects are all out of whack,
then so too will the ethical impacts be out of whack, because then people will be
on the bridge and it'll collapse and they'll die.
So that's really ethically bad.
But it's really fine.
It's not, if you like an ethical problem as such to solve, it's more like there's
these quasi -technical problems like outputting false information that has or
can have serious ethical impacts.
But there are these questions with generative AI about what's the appropriate
response, what might be toxic or improper, culturally insensitive, or biased in ways
where reasonable minds can differ.
So if a company brings you in to figure those things out, how would you advise
them?
So there's a couple of things to say here.
I mean, the first thing to say is that usually my company, myself, my company is
not brought in at that really granular level of we've got this particular model
for this particular use case.
And we want to know what are the standards that should govern this particular model
that happens.
Sometimes it's starting to come up a little bit more precisely because of
particular applications of LLMs for exactly the reasons that you've just
stated.
Usually what they're looking for is how do we even, how do we get our organizational
hands around this?
more generally.
So how do we create policy around this kind of thing?
How do we create the procedures and augment existing workflows for our say
data scientists and product managers to look for these kinds of risks, to flag
these kinds of things?
How do we put together the appropriate risk board or committee so that there's
someone to whom they can elevate those kinds of concerns?
How do we create the grounds on which that committee is going to make decisions about
what the appropriate benchmarks are for sufficiently safe or sufficiently
appropriate?
Something along those lines.
As for how those substantive decisions get made, there's roughly two kinds of things,
two kinds of tools that I like to reference.
One is an ethics statement or an AI ethics statement.
Some people are going to call them responsible AI statements.
Those are really, if you like, high level articulations of values connected to
guardrails of action.
So because we value privacy, we'll never do X.
We'll never sell your data to a third party.
Because we value accountability, we will integrate people's
compliance with our AI ethics program into their performance reviews.
We'll put ownership of the AI ethics program in the hands of a C -suite
executive, that sort of thing.
So there's those high level policies that are, I'll give you one more, because we
value justice or fairness, we will vet our AI models for bias at each and every stage
of the AI life cycle.
So these are sort of commitments of action tied to values that get at least some
procedural guardrails in place.
and some, some substantive actions that are ruled out after that.
I mean, even getting that as a big deal and implementing those procedures, that's
a big deal.
So forget about, you know, as an act, as at least as a former academic and you, you
know, you want to deal with those really tough cases.
Let's deal with these really tough cases where there's a lot of gray area.
How do we decide what's appropriate for this LM?
But the truth is, and I would love for my, for most of my customers and my clients to
be like, yeah, let's help us with these.
really hard, tough gray area cases.
But that's, that's not where their heads are.
Their heads are, can we just, that's running.
Gray area cases is running.
We're just learning how to walk here.
We're barely crawling at the moment.
How can we crawl safely without like falling off the table?
And so, sorry, I'm going on a bit of a ramble.
So one is there are those kinds of conversations about really concrete models
and what constitutes appropriate.
One thing to do is to have general kinds of policies.
The second thing to do is to have what I call ethical case law.
So this is when you articulate certain kinds of cases that you would say this
would be inappropriate.
This would be not appropriate.
Sort of get internal alignment on those being what I like to call the ethical
nightmares.
And then when you have a particular novel case, like should it be allowed to say X
to Y, let's say something with a certain political content to a general audience,
you can compare it to your ethical case law, internal ethical case law and see
whether...
that provides sufficient guidance.
And then of course, there's going to be deliberation among the ethics board or the
risk committee, whatever.
The thing that I was backing up to say too soon, I sort of interrupted myself is the
truth of the matter is while the hard cases are sexy and they're interesting,
especially to academics, they are not where most companies are because most
companies are dealing with the not crazy gray cases.
You know, maybe an open AI is dealing with those gray cases, a Google, a Microsoft,
but a financial services company.
Insurance industry, health care and life sciences, food and beverage, apparel,
retail, they're not dealing with those crazy situations.
They're just hiring people, right?
They're using AI software to hire people.
How do we not do that in a discriminatory and biased way?
So I don't know, this is my sort of, okay, I'm going to stop talking now, but this is
my very long -winded way of saying most companies don't deal with those kinds of
tough cases.
Yeah, no, that was great.
I there was a lot of real good meat in what you put out there.
So let me just unpack some of it.
If companies are just starting down this path, what's the most important thing for
them to do first?
I always like to say that sort of table stakes is a call it a risk assessment or a
gap analysis or AI ethical risk maturity assessment.
I think most organizations don't sort of know where they are and what they have in
relation to what those risks may be.
So looking to see what existing governance structures do we have that cover this sort
of thing?
What, what do we lack?
What kinds of policies do we have that cover this thing?
What policies do we lack in most cases?
If a company has done literally nothing on the responsible AI front,
then you might not even need to do that gap analysis risk assessment maturity
assessment.
You know, you've got nothing.
And now you've got to start with an AI ethics statement, which is going to
articulate what those standards are and the procedures that are associated with
those standards.
And frankly, if you can, the metrics that are associated with those procedures, I
like to draw a really clear line from values of a business or sorry, AI ethical
risk values to procedures and the metrics that are used.
to track compliance and impact of those procedures.
So that's a good first step.
And then sort of part and partial that is then to start building a customized
framework.
When I say framework, I mean something somewhat specific.
It's going to include policies, additional governance structures, governance bodies,
like an ethics board, it's going to include metrics, might include procedures,
might include race matrices, that sort of thing.
Yeah, so it's one of things I really love about your book, Ethical Machines, that
it's rooted in serious ethics, but it's very pragmatic in terms of these kinds of
things that you're talking about.
But how much do companies really differ?
You're talking about customization, but I think there's probably no company would
say, well, our values, we don't care about privacy, or our values, our discrimination
is OK.
So where is it really going to be customized?
There's actually quite a bit of customization to be done.
It's actually most of what we do is the customization bit.
There are lots of things that vary.
So for instance, you're correct.
Two different organizations, a hundred organizations are going to say we value
privacy.
What that means is going to vary quite a bit across organizations.
Some will say we'll never sell consumer data to a third party.
That's part of what our commitment to privacy is.
Other companies will say, no, no, no.
We're going to sell that data.
That's what we do.
It's a major revenue source for us.
So we respect people's privacy, which means we will always ensure that we're
going to aggregate and anonymize that data and use various kinds of privacy
preserving techniques like Q anonymity.
But that's, um, K anonymity, but, uh, that's, uh, you know, that's very
different from saying we're not going to sell the data at all.
There are differences in what the governance structures have to look like.
So look, some organizations are structured like partnerships.
And some are, if you like the sort of the classic pyramid of CEO on top and then
everyone below that.
When you have an organization that has sort of like a CEO on top and the CEO
says, this is how it goes.
This is a gross oversimplification, but more or less, okay, if you've got the buy
-in of the CEO, that's more or less how it's going to go.
If on the other hand, you have say a giant consultancy spread across dozens of
markets where there's not that kind of top down change.
because each you need to get buy -in from each of the partnerships, each of the
heads of each of the partnerships.
Now governance looks totally different because there's not, if you like, one
board to rule them all.
They might say like, yeah, that's good for you guys in the US, but we're over here in
the EU.
And so we're going to do things, we're going have to do things a little bit
differently over here.
Our standards are going to be different here.
Some people want to apply the same standards across all markets.
So yes, we're in EMEA, we're in the EU, we're in the US, you know, North America,
et cetera.
And we're going have the same standards across the board.
Others say, no, no, no, we're going to have different standards for different
markets, different risk thresholds for different markets.
We're going to roll this out in different ways.
So we're going to roll it out by market, for instance.
So first we're going to roll out this program in the U .S.
Then we're going to expand to North America, and then we're going to expand to
the U and then to EMEA, et cetera.
Others say, no, no, we're not going to roll out by market.
We're going to roll out by role.
So first we're going to roll it out to all the data scientists.
I don't care where geographically they happen to be.
And then we're going to roll out to HR.
And then we're going to roll it out to marketing.
And so there's different ways of, well, that's going to be customized.
Who's going to sit on the board?
What kind of authority does the board have?
Can the ethics board be overruled by a more senior executive?
That's it.
Some organizations can say, no, it can't be overruled.
That's the final say.
Other will say, yeah, it can be overruled by say the CEO or something along those
lines or a chief data officer.
Chief data officer is not actually on that board.
So there's a tremendous amount of, what does training look like?
What are risk?
We already do risk matrices in our organization.
And the way that we do risk assessments is different how our peers do risk
assessments.
We want responsible AI or AI ethical risk assessments that cohere with, that
harmonize or integrate with how we already think about risk in our organization, how
we already do those risk assessments.
And same with metrics.
We want metrics that mirror the kinds of risk metrics that we already have at the
enterprise wide or department level, et cetera.
So there's a tremendous amount of customization that needs to be done.
That's why there's the NIST AI assurance or whatever it's called framework.
And it's great.
It's super smart.
It's totally generic though, necessarily.
And so you give that document to, here's a really smart, generic framework.
And there is a tremendous amount of work left to be done.
But are there right -
of understanding what the organization is and customizing the AI ethics.
So I think there's reasonable and unreasonable.
And I think this is a place where we sort of can think about it in terms of the
political spectrum.
I think that there are companies who are sort of on the left side of the spectrum,
take a Patagonia.
There are companies on the right side of the spectrum, take a Hobby Lobby.
And we sort of think, yeah, that's fine in the corporate world having that.
I mean, most anywhere you think about it.
Having that kind of diversity of values, that's acceptable to us.
There's beyond the pale, right?
So you don't want KKK values anywhere around that would be beyond the pale.
So when are there right and wrong answers?
I think that...
There are some wrong answers certainly, but there's lots of right answers that are
mutually incompatible, just like Patagonia's stance on some issues is going
to be incompatible with the stance of, say, a hobby lobby.
That said, there's also right and wrong answers as to, hey, does doing this
actually help us to achieve our goals?
Is this means sufficient to achieve our end?
And then there are certainly right and wrong answers.
They'll say, oh, we'll just do this.
We'll just tell everyone x.
And we'll say, that ain't going to do it.
You could.
That's not what's going to happen.
You could tell them that, but that will definitely lead to a lot of things falling
between the cracks.
We highly recommend against that.
So I think there's right and wrong and reasonable and unreasonable in the realm
of what the values are, what the standards are, what the procedures are.
And then there's right and wrong in terms of, are these means sufficient for
achieving our ends?
So you gave an example of can the CEO overrule the ethics board?
How would you help a company think through what the answer is?
Well, I mean, it's this.
Look, if you really want to take the ethics of this seriously, then you have an
ethics board, cross -functional ethics board, well -trained, both in, if you
like, ethical deliberation on the kinds of issues they're going to face, but also
well -trained in understanding, because usually it's this sort of board that's
also going to oversee the rollout, compliance, and impact of the program,
well -trained in how to oversee a program like this.
And if you really want to prioritize ethics and trustworthiness, etc., then you
say the decision by the ethics committee is the final one.
That's it.
What they say, if they say this is a no -go, then it's a no -go.
And that's really good if you want to prioritize more than, you know, not maybe
not more than anything, but very highly prioritize something like long -term
trustworthiness of the brand, then you do that.
That said, I think we need to be honest here and say, there's going to be cases
where doing the unethical thing is going to be really profitable.
There are sort of AI ethicists out there who say, oh, the right thing is also the
profitable thing.
And so we're like, no, that we're true.
We wouldn't have to talk about ethics.
We'd be done here because they would already do the profitable thing and then
the ethics that would fall out of that.
So there's clearly conflicts.
And so if you say, listen, if you want to give yourself some wiggle room where
the...
The reward is so great, the ROI, say bottom line ROI is so great that you think
the CEO should have the ability to override the ethics committee.
That's not, it's not crazy.
It's a little bit dangerous in the short term anyway, but it's not a crazy view to
hold.
In fact, I think in some cases it's quite reasonable to say, you know, yeah, this is
a business at the end of the day and we're willing to take bigger risks.
It's not.
It's it's it's gonna tell me another context.
I mean, there are certainly cases in which CEOs choose them morally abhorrent for
some short -term profits.
That's a bad thing.
But should it be sort of, if you like, structurally disallowed by the
organization?
Probably not in many cases.
If I'm looking at a company and trying to assess how serious they are, how effective
they are in terms of what they're doing on AI ethics, are there things that I should
look for, their tells?
Let's start with that, sure.
I'm a customer or I'm someone just trying to evaluate the company from the outside.
You'll have no idea.
No clue.
The people, the sort of statements that they put out are phenomenally superficial.
They're not going to make their policy public.
They're usually not going to, you know, when we work with our clients, we train
them how to do auditing, how to ready themselves for internal and external
auditing of their AI ethics program.
And in principle, they can make that stuff public.
Number one, most companies are not there yet.
They haven't done enough.
They haven't done enough to audit.
They're still designing the program.
Some are at that audit stage, but most are not.
And two, most of them are not going to be public about it.
It just puts a target on their back.
In the early days, this doesn't happen anymore, but five years ago, companies
were way more resistant to being public about what they said about AI ethics or
ethics generally because they felt that if we say that we really care about ethics,
we're putting a target on our back for journalists to catch us.
So if we say we care a lot about AI ethics and then something goes haywire, we're
going to get skewered.
by the journalist.
So let's just shut up about it.
Keep quiet.
We'll do this stuff internally, but let's not tell everyone about it.
That's changed over the last five years.
Now it's more like, oh, whoa, everyone else is putting out AI ethics statements.
We better put out an AI ethics statement to keep up.
So that's good.
But the statements are, by and large, phenomenally superficial and mean nothing.
The average citizen, they don't know anything.
Even if they look at the privacy policy of the organization, I've seen many examples
of paper tigers, many examples where,
do you measure success in terms of the work that you do with organizations?
How do I measure success?
That's a good question.
I mean, you know, if they're just doing the things that we're telling them to do
more or less, I mean, when I say what we tell them to do, we're giving them
options.
We say, look, you can build your program this way or that way.
You know, for instance, we say, you can build an AI ethical risk program or
responsible AI program, or you can build a digital ethics program.
The digital ethics program will include AI, but it's also a bit broader than that
to include other kinds of technologies, including ones that we don't, either
applications of AI,
that we don't have yet, new kinds of AI we don't have yet, new kinds of technologies,
new applications, new technologies.
So let's say blockchain, quantum, AR, VR.
Ideally, they would all say, oh yeah, digital ethics.
It's the broadest, it's the most comprehensive, it's the most proactive way
we can address this.
These issues aren't going away, they're only growing.
So we say, look, digital ethics is ideal.
But sometimes they say, listen, we'd love to do that.
We don't have the internal buy -in for that.
What we have, we have board support for building an AI ethics program.
That's what we're not going to go back to them and say, you know, we want to do
something bigger or we want to do something broader.
That's where our buy -in is.
That's the budget we have.
That's where our focus is.
That's what our priorities are.
So that's what we're going to do.
Is that a success or a failure on my view?
It's still a success.
You know, it's not the ideal, but it's, it's a success.
What's another success if they actually move on to implementation?
There are.
plenty of organizations that will start to write an ethics statement and then just
stop.
That's a failure.
You've got to get to the point of implementation.
You've got to get to the point of training your people.
This is, this is crucial.
There's got to be that, if you like role agnostic training, the training that
everyone across the organization gets, then there has to be role specific
training.
There has to be updates or augmentations of workflows of say data scientists,
people in HR, et cetera.
There has to be documentation.
You know, there's got to be all these.
things where it actually impacts the day -to -day operations.
So what we like to say to our clients is, okay, you've done all this, blah, blah,
blah, now we got to get the implementation, because you can write all
this stuff, you can have this policy, but that doesn't mean that the data scientist
knows what they need to do tomorrow at 9 a .m.
when they get to work.
This needs to be made clear.
And if they don't get to that, that's maybe not yet a failure, but it's not yet
a success.
One thing that's changed in this area in recent years has been that there's been an
upsurge of regulation and legislation and discussion and passage of things like the
AI Act in Europe.
How does that impact on what you do or on what companies actually do on the ground?
Because on the one hand, now there's going to be all these requirements.
On the other hand, if this just becomes a check the box for compliance, then it
might actually be counterproductive.
So what do you see?
I think it's too early to tell.
It's still TBD.
Yeah, there are regulations out there.
You know, New York City came out with a regulation not so long ago.
I think it passed this past summer that all HR tools, sorry, all AI tools involved
in the HR hiring decision -making process need to be audited by a third party.
And that's not being enforced.
I have no idea if it's had any impact at all.
The EU AI Act.
promises to do more That's plausible, especially if you're in the EU.
Of course, there are lots of companies in the US who operate in the EU So they'll
have to make sure that they're compliant as well It's gonna do something.
I think it's just frankly it's too early to tell How much of an impact it's going
to have one thing that I that I anticipate happening could be false The vast majority
of owners of this kind of these kinds of problems within organizations are on the C
suite on the tech side of the house
So a chief data officer, a chief analytics officer, sometimes a chief information
security officer or a chief information officer, chief digital officer, blah,
blah, blah, blah, blah.
It's the risk and compliance people, not so much involved.
We ask, we say, look, it's great.
We need to bring them to the table.
We need them in these conversations.
Legal is pretty standard to say, well, bring them to the table.
But usually the risk and compliance people are not spearheading it.
So I think operationally we'll probably see a change where it gets.
less in the hands of tech people and more in the hands of those risk and compliance
people, or at least if it's less in the hands of the data people, the risk and
compliance people will have to get more involved because that's their job.
So I think we'll see more of that.
That in principle will be helpful because those chief data officers, what do they
know about risk and compliance?
They're not risk and compliance folks.
They're engineers, they're data scientists, they build cool shit.
That's not what they do.
And so I think that having...
trained risk and compliance people probably owning the program or at least
playing a significant role in it will increase adoption.
Will it be mere box checking?
I don't know.
Probably the beginning is my guess.
There's also a way in which companies tend to overestimate what they're actually
doing.
So we'll ask companies, oh, so are you doing X, Y, and oh yeah, yeah, yeah.
You're checking for bias?
Yeah, yeah, yeah, yeah.
We check for bias.
And then we sort of start digging around and we find out, oh, not really.
I mean like...
The thing that they're doing is so minimal.
And so there's also a level of self -deception, or at least ignorance, about
how robust what they're doing actually is.
So, you know, I'm optimistic, but it's a cautious optimism.
I wouldn't be shocked if it turned out to be box -checking.
And is there anything else that you see potentially coming in the next few years
that's going to have a major impact on AI?
I mean, there's two kinds of things, broadly speaking, that could have the
impact.
The, um, and they're, they're compatible.
One is a big disaster.
Think of like Cambridge Analytica, but for AI.
And so if you get that kind of thing, that will help.
And, uh, help.
I mean, you know, that'll be the silver lining.
That'll be a cause of people thinking more seriously.
That might happen.
It wouldn't surprise me.
But the other thing is just, you know, death by a thousand cuts.
It's so.
there's this AI incident and then this one and then this one and then this one and
this one, neither one of which is a massive crisis, but the aggregate should
be terrifying, I think, eventually to people in the C -suite and the board
responsible for protecting the brand or their organization.
So I just think it's going to be the case that we're seeing increased adoption and
as you get increased adoption, you get increased deployment and increased
deployment means increased probability of things going ethically sideways.
And so you get more things going ethically sideways and they're going to be
published, et cetera, et cetera.
On that note, by the way, increased adoption, there are some people who say,
oh, generative AI, it's been a year already, or it's been a year and two
months, whatever.
We haven't seen the massive change yet.
And I just think, just chill, just hold on.
We're talking about the most cutting edge technology out there.
They're working on it.
They're figuring it out.
Most of our clients are running dozens, and in some cases, hundreds of pilots
using generative AI.
They're just trying to figure out how this thing works.
They're trying to figure out.
Does it solve a business problem for us?
What business problem does it solve?
How do we use it responsibly?
So, you know, for all the talk about, oh, it's been, it's out here.
No, not really.
Open AI released their stuff a year and a bit ago.
Google a little bit less than that.
Then there's all these other, you know, llama and anthropic.
Just wait a second.
We're going to see, I would, unless things go oddly haywire, we're going to see lots
of adoption, lots of deployment.
in 2024, let alone 2025.
And with the increased adoption, we're going to see more bad incidents.
All right, well, more bad incidents, but hopefully also more good work for people
like you.
Yeah, I mean, I should say, you know, this comes against the backdrop of being, I
don't know, not a pessimist about the technology.
I'm not a catastrophist.
I mean, there are some people out there who are screaming that the sky is falling
and everyone's going to die and this is a disaster and it's not ready for prime time
and it never will be.
I just think that's sort of stupid.
I mean, I've seen use cases that work and they do amazing things.
There are cases, I was recently talking to an organization yesterday that said they
implemented a generative AI solution for certain kinds of...
search and summarization of a process that used to take 40 hours and now it takes
four.
And they are happy with the results.
They're happy with the metrics.
They're happy with how it's level of accuracy.
They're happy with the level of risk that there is in the particular use case that
it is.
Just give it, there'll be more.
That's a well -funded, you know, organization with lot who has lots of
power to throw out these kinds of problems.
Others don't have that kind of power or they're slower.
They want to see what others are doing.
But yeah, I'm not a catastrophist.
I think that there's tremendous good opportunity.
And yeah, there's lots of opportunity though to make sure for people like me
that to help organizations make sure they don't go off the rails.
Reed, thanks so much for this conversation.
It's been really instructive and fascinating.
my pleasure.