Welcome to the Road to Accountable AI.
I'm Kevin Warbeck, Professor of Legal Studies and Business Ethics at the Wharton
School of the University of Pennsylvania.
For decades, I've studied emerging technologies from broadband to blockchain.
Today, artificial intelligence promises to transform our world, but AI won't reach
its potential without accountability, mechanisms to ensure it's deployed in
responsible, safe, and trustworthy ways.
On this podcast, I speak with the experts leading the charge for accountable AI.
Whether to restrict open weight foundation models is a big topic of discussion
in AI safety and policy circles.
Thanks But not one that the general public and business community are
necessarily focused on my guest, Kevin Bankston from the center for
democracy and technology talks about why this issue is so important,
both to the future of innovation in AI and to our ability to address.
Serious AI risks, as you'll hear, Kevin has strong views and in full disclosure,
I signed on to an open letter that CDT wrote on these issues, but in our
conversation, we cover the arguments on both sides of the issue and why
this topic is actually so important to understanding what the future
path holds for the development of AI.
Kevin, pleasure to have you on the podcast.
Before we get into talking about open weight models, just a quick Tell us
a little bit about your background and for those who aren't familiar
what CDT is, what's the work that the organization is doing on AI?
Sure.
I'll start with CDT.
CDT is, I would say, the premier internet and technology policy think
tank and advocacy organization in DC.
We've been around for, I want to say, almost 30 years at this point.
Non partisan, non profit, focused on protecting civil
rights and civil liberties.
In the digital world that encompasses a whole lot of things.
It's a broad spectrum organization, but obviously right now AI is at the forefront
of a lot of technology policy discussions.
And I, um, with my colleague, Miranda Bogan co founded
the new AI governance lab.
At CDT, um, which is focused on helping develop best practices and standards
around evaluating and mitigating AI risks to help build a technically informed basis
for eventual legislation and regulation, taking advantage of not only my 20 odd
years in civil society at organizations.
Like and including CDT.
After that 20 odd years, I was approached by Facebook, now Meta, to basically
help them figure out their path forward on responsible AI development,
which was an intriguing possibility.
And so I spent about four years building their AI policy team, being
one of the founding senior leaders of their responsible AI product team,
which consults with all the other product teams on their AI products.
Thank you And did a lot of good work, but obviously working in a big company like
that in a lot of ways is very stressful and challenging and time consuming.
And I am an, at this point, a late middle aged fella who just, more
accurately, my wife just had a baby.
And I decided for a variety of reasons, but especially including
that one, I was ready to leave.
The corporate world and come back to civil society and apply what I had learned.
And also what Miranda, as my colleague on my team at Metta had learned
around responsible AI development.
So now Miranda who did trail trailblazing groundbreaking work at
Metta in terms of addressing issues of race and gender bias in ads delivery.
She is the director of the new governance project at CDT.
While I'm a senior advisor to that project and to the leadership at
CDT on other AI policy issues.
I currently teach the First Amendment and copyright in regard to AI technology
as an adjunct professor at Georgetown.
Those are issues that CDT has not spoken greatly about.
We do address the First Amendment in regard to this issue a little bit in the
comments we're going to be talking about.
But speaking generally, if I talk about the First Amendment here, attribute
that to me as Kevin the scholar, not necessarily CDT's positions.
Great.
So this is why you are a perfect person to talk about these issues.
I want to ask you in particular about the debate that's happening now
around open weight foundation models.
So first, for those who aren't necessarily familiar, what are we talking about here?
So this will require a little bit of a history lesson about What
open source software is before we get to what open source AI is.
So in the nineties, you began to see the evolution of this new form of.
Software development and licensing around this open source concept where.
You would have companies or even voluntary projects with a bunch of contributors
developing their code openly, as in collaboratively, or when publishing their
programs, also publishing the source code itself so that other people could use that
to build their own software, to modify it for their own uses, et cetera, et cetera.
Usually this is accomplished through something called an open source license,
which basically says, You can use this and there are no use restrictions on it.
We are essentially.
Although we still own the copyright in this technically, we are
licensing it broadly for public use.
So, why is this important and, speaking generally, good?
Certainly the internet and digital technologies built around and
on the internet rely an enormous amount on open source software.
At this point, 96 percent of all codebases include some open source code.
It's the original open sourcing of the Netscape browser in the 90s, which
led to the open source Mozilla Firefox browser, which was a predecessor to
later the Google Chrome open source browser that enabled competition
so that we didn't all just have to use Microsoft Explorer on the web.
Now, I want to be clear, open source is not and will not be Always a
silver bullet for competition.
And it can be leveraged by already powerful stakeholders
to make them more powerful.
Certainly it's enhanced Google structural power, the fact that it controls
ultimately the development of Android, for example, but then again, that, that
power has also helped counterbalance the power of other people, other companies,
like your Microsoft's and your Apple's, and enabled a large number of hardware
vendors to compete that wouldn't.
Have had an operating system to use and it's complicated ecosystem.
But ultimately open source in the context of regular software has
been a boon for competitiveness.
Also a boon for security in the world of open source.
They say enough eyeballs make all bugs shallow.
This is because if you have.
Your source code interrogable by anyone.
That means you have a lot of eyes looking at it and finding flaws
and helping you patch those flaws.
That's why, for example, we have the security world has generally
been supportive of the open sourcing and wide availability of.
Penetration tools for breaking into computer systems, not because
they want to help people break into systems, but because they want to
help people detect how to break into systems so they can fix those things.
And so speaking generally, open source has also been a boon.
For our overall technical security.
Now we have to get to AI though, and open source is a little bit of a misnomer
when talking about AI for reasons I'll get to first, I'll have to back
up and say, what are we talking about when we talk about AI very generally.
When we're talking about AI, we're talking about machine learning,
which is using software to look at vast amounts of data to find
patterns in it that are useful to us.
In the current context, we are usually talking about large language
models or other generative models.
And to break that down as simply as possible and get to the answering
the question of what are weights.
I'll just say to make a large language model, you take a whole lot of data.
Often everything you can scrape from the web, data you've
licensed from other vendors, a large amount of language, right?
A large amount of language and feed it through an algorithm that creates what
is essentially a massive mathematical space that sort of summarizes all that
data, not directly, but rather each.
Not even each word, each part of a word, and they're called tokens in this
context, gets its own little spot in this sort of semantic map of all of that
content, so that, for example, as it has its ingested more and more data, it
begins to not understand, but be able to place close to each other related
semantic concepts like dog and puppy, for example, are close together in this map.
What happens then is once you've trained that model to create that mathematical
map, which is what we call weights, Because the, to put it in non technical
language, like all of that semantic content has been weighted to some extent
or another in this mathematical model.
You need the weights to be able to infer anything for the model
to actually give you an output.
Open weights, and so now the analogy to open source comes in.
There, as with regular software, there's both open and closed approaches.
The closed approach is, for most of its models, The chat, a good example
is OpenAI and the way it treats most of its most powerful models, which
is it is available through them.
You have to either go to their website and pay the 20 bucks to access their
user interface for chatting with ChatGDP, or you're a developer who
pays to access their API, their application programming interface.
To build apps or services, internal or external on top of their model.
Either way, though, they have centralized control over the model.
Users and developers do not have access, direct access to the weights.
And yeah, so that's closed AI.
Open weights AI, and I hesitate to call it open source for reasons I'll explain,
is when you publish, at the very least, the model software and the weights.
So that another developer could deploy that on their own infrastructure
with their own modifications.
So in many ways it is similar to open source software because
these open weights models are typically offered under a license.
That either that, that allow for free redistribution and or don't have use
restrictions and or otherwise give you freedom to play with the model
and build derivative models in a way that you can't do with a closed model.
I don't call that.
Open source for a few reasons.
Um, one, most of the components of the model are not in the form of source code.
And in fact, this critical heart of the model, the weights, is not really
interpretable by a human at all.
Um, but I also don't call it open source automatically because
it depends on the license.
Speaking generally, an open source license is one that does
not have use restrictions on it.
And yet, as we'll talk later, there are emerging some new kinds of
licenses that, for AI safety reasons, do actually attempt to restrict
the allowable uses for a model.
Okay, so let's get into the policy issues.
It sounds companies can decide, open, close, as they do with software.
What's the concern?
There are a number of concerns, but I'd love to start with the benefits.
Because really, the policy question requires us.
To weigh the benefits and the risks.
And I think the benefits, although there are some ways that open source software
and open weights AI are not identical, there are a lot of ways that they are
similar, particularly in their benefits.
And I break these benefits down into three basic categories.
One is simply distributing power, whether in the market or in the
culture, which I'll explain basically.
As with open source software, we expect and we currently see open weights AI
being a strong competitive pressure against open against closed providers, not
least because it is free, we are seeing very fast diffusion of the technology.
We see very large enterprises adopting it, including for internal
uses like Dell and Wells Fargo.
There's a great quote from Adele.
Senior VP.
That's basically like, why would we pay for a general purpose model that
does not know much about our company?
And if we want to teach it that we have to upload all of our private
documents to their cloud and we have to pay for it, or We could use this
free software and create our own bespoke model for our own purposes.
That is more efficient, more tailored to our needs, et cetera, et cetera.
And so you're seeing that the second big category of benefits
is simply the catalyzing of.
Innovation, not only innovation in AI, but in all fields that can leverage the AI.
The first LLMs that were built were open research.
They were openly published the Google, the Google research scientists who
created the first LLMs in 2017 ish.
That is the root of the explosion of AI innovation today is the fact
that research happened in the open.
Thanks to open development.
You also see an explosion of developers.
Taking open models and then using them to build smaller, more
efficient, customized models.
Um, Build models that are small enough to run locally rather than
in the cloud, which has both privacy benefits and environmental impact
benefits and just bandwidth benefits.
Like you don't have to spend all that network infrastructure.
Research around AI models also enables a wide swath of security and safety
research that can't happen effectively.
with closed models and can enable the faster development of including
the faster development of tools to detect and prevent bad things.
There's another way in which it can help security rather than hinder it.
And then all the other fields that leverage this AI,
they will also move faster.
And so there is a benefit of simply speeding up the process of
innovation and opening that process to a lot more stakeholders as well.
And then finally, there's the, the transparency benefits.
Which also brings security and accountability benefits.
Like with regular open source software, with enough eyeballs, a lot
of those problems will be discovered and can be remedied because the
whole world essentially is your red team, the team that's testing it.
There's a lot of research that's happened that couldn't have happened
without open source models around guardrails, the guardrails that
companies use to try to prevent bad things from happening with their models.
A lot of those, the challenges with those guardrails have been
discovered through open source.
Models, um, develop in a way that actually reflects on.
The flaws of the closed models as well, because they're similar
architectures, the closed and the open models, things that you learn about
open models can be applied and you're thinking about closed models as well.
So it's a way of even getting more transparency into the closed models,
even though we don't actually have literal transparency into the closed
models and it's enabled research around bias and discrimination that
wouldn't have been possible without access to the model and the weights.
And one of the things we've discovered in all that testing is, and this is
Problematic and worrisome, but also relevant to the analysis of whether open
source is worth the risks, considering the benefits is the guardrails that
we are currently using to try to.
Basically say no, have a model say no to you.
When you try to do something dangerous or problematic, those are very fragile
and they're fragile, whether or not you are using open or closed models.
And we wouldn't have known that without research that was enabled by open models.
This goes to an important concept that we'll need to talk about when
we talk about the risks, which is what is the marginal risk of open
source or open weights models.
Compared to other technologies that are available, whether it's the internet
itself or closed models that are also available to be used, because
if there's not a big, or there, if there's not a meaningful marginal
risk that is differential risk between those two things, there's no reason
to target open source specifically.
For restriction and we should probably think about a different
approach Okay Sounds wonderful Why then are there concerns?
Let me ask you in a more targeted way Ntia national telecommunications
and information administration in the commerce department Launched
this request for comment in early 2024 about open weights models.
What, why did the government feel the need to even ask whether these should
be allowed to exist in a free form?
Yeah, I think there are a number of different categories of
risk and a number of different.
phases of concern that we've gone through at this point in the past two
years, since chat, GTP dropped for real, I'd say the first category I
would call emergent existential risks.
I would say these were very much at the forefront of.
Initial thinking about the dangers of AI because there is a community, we'll
call them the AI safety community, although there are plenty of other
different types of AI safety folks and stakeholders, there is a community
around AI safety that essentially.
Evolved over 20 years from work being done in the Oxford philosophy department
around potential existential risks that humanity might face and one of
those these people have theorized for a while now is the possibility of some
out of control super intelligent AI that we lose control of and that does
something catastrophic to harm humans.
for its own reasons or because it misunderstood its instructions.
And I don't want to denigrate the people who are raising this concern.
Speaking generally, I think these are very intelligent, very well
intentioned, sincere people who want to see these concerns addressed and
don't want to see AI destroy the world.
The more likely threat than the sort of vague super AI is the possibility
that these systems, as they get more intelligent, could significantly
aid adversaries, whether state adversaries or non state adversaries,
in developing chemical, biological, radiological, or nuclear weapons.
or cyber weapons, hacking, hacking tools and whatnot, and
automated attack approaches.
We will talk at more length about that because it ties in to the NTIA
process, but really the key issue there, as is the key issue generally,
is there actually a marginal risk?
Does open source pose a greater risk of those things than other technologies?
And I'm not going to spoil that.
I'll tell you how that turned out.
A third category is simply content issues of harmful or problematic
or illegal content, particularly of concern are deep faked child sexual
abuse material, virtual CSAM or deep faked NCII non consensual intimate
imagery, often called revenge porn.
And this is going to be a problem and is a problem.
But again, the marginal risk issue is a question that needs to be
asked, and we'll ask it shortly.
Finally, the fourth category, and where I think most of the concern
is settling now, and which I'm sure we'll talk about later, is China.
China specifically.
What do we do about Chinese competition in the AI field?
How do we deal with China as a national security competitor
in regard to AI as well?
And I think we are at a point now.
Where that's probably the most relevant consideration for policymakers in the U.
S.
As they look toward possible restrictions on open source now.
But, so that, that's the sort of range of risks I think
people are most concerned about.
Okay, let's drill down on some of those.
I, I agree we can put aside the existential risk.
But with regard to marginal risk, you made the point that, that guard
rails on these foundation models are fragile, but isn't it true that it's
substantially easier to remove a guard rail if you have access to the weight.
So if it's a, an open model, then it's fairly trivial for someone to take a,
that's a hostile actor to take out those guard rails, much more challenging.
If the company keeps control over the model.
It depends the you may have seen chat GTPs.
model released last week.
And then within minutes on Twitter, people sharing their, their attacks,
their prompts that were able to get around certain content guardrails.
The Taylor Swift sexually explicit deep fakes that we saw making the
rounds earlier in the summer, those were generated by Microsoft's.
Closed image creator, but yes, open source code, open source models are
more easily modifiable in a wide range of ways, including by bad actors.
But then that, that, that leads to the question of what about other technologies?
How does it compare to those?
And that's something that the NTIA got into, but to set up the NTIA
process a This came up in the context of the Biden AI executive order that
came out October 30th, I want to say last year, and which called on NTIA
to do a report on open weights AI.
This was prompted as best we can tell by there being staff at the National Security
Council of the White House and staff at the Bureau of Industry and Security,
the export control guys at the Commerce Department who were concerned particularly
about the CBRN and cyber risks.
And we're considering the possibility or looking at the possibility
of putting restrictions on the publication of open weights because
of those national security concerns.
It seems that somewhat cooler heads prevailed in pushing
instead pushing for that.
Not to be decided in the EO, but rather to delegate to NTIA, the National
Telecommunications Information and Information Administration, essentially
the president's lawyer, the president, not the president's lawyers, the
president's technology advisors, um, to do a more sustained study of the issue.
The issue in particular being what are the benefits of open weights models?
What are the risks of open weights models based on that?
What are potential policy approaches the president should take?
Or not and and so that was the process and they put out the call for comment
earlier this year A bunch of people filed comments including us and i'll
talk about that And then they finally released a report I guess a couple of
months ago And so let's talk about that and i'll talk about the comments that
cdt filed first a not so humble brag.
I'm proud to say This is a dubious achievement, but our comments were the
longest and most detailed comments filed in the proceeding, which had hundreds upon
hundreds of comments, more salient, our comments were cited in the report from
the NTIA more than any other comments.
And the only other single source that was cited more than our comments was a
paper that we coauthored with academics at Stanford and Princeton focused on
defining marginal risk around open models.
I'm glad to say, I feel that we had a very positive influence on this process.
We also worked closely with NTIA to ensure they heard from a variety of other civil
society voices from civil rights groups to civil liberties groups to open source
advocates and everything in between, but the gist of what we were saying in the
paper I mentioned in our comments is.
We are not ruling out categorically the possibility that some restrictions
on open weights may ultimately be necessary, but at this point at, with the
capabilities we see online now and that are coming shortly, there is not enough
evidence to support those restrictions.
And by, by that, not enough evidence that there's actually a significant
marginal risk from open models.
This is probably clearest.
In the context of the like biological and nuclear and cyber conversation there by
the time of our comments, there had been several academic papers and papers from
security think tanks like Rand research by Microsoft and open AI research where they
basically set up teams to compete on who could come up with the best evil terrorist
plan around biological weapons and gave One team access to the best models
and one team access to the internet.
And what they found was there was very little difference in the capacity of
those teams to plan an attack, which, which stands to reason because what the
models know is what they've seen online.
And so it may give some incremental additional aid in.
in collecting some of your sources when you're looking for information
to help plot a plot, but it really didn't make a huge difference.
Similarly, There was research on how much models, which certain models can also
help instead of just speaking in language, they can also code, they can create
software, and so there were tests about how effective they were in helping create
penetration tools and other attack tools.
And again, they found only a incremental difference, not really a major difference.
between what a coder could do without a model and what a coder could do with a
model in terms of offensive cyber stuff.
It's also worth noting, as with the case with open source software, that capability
to, to create tools also is now in the hands of defenders as well as attackers.
And as we've seen in the open source context, that has tended to ultimately
benefit defenders more than attackers.
And so NTIA, which was primarily focused on those as the most realistic,
National security related threats that they were asked to assess
essentially concluded We don't see a significant marginal risk here.
That doesn't mean there might not be one at some point.
Therefore, we do endorse continued monitoring.
I guess what a doctor would call watchful waiting, like it's not something that
requires intervention now, but something that we need to be watching closely,
including recommending continued investment in the development of best
practices and standards around safety.
As well as market surveillance and other measures to see how
these models are being used.
Because so much of the risk is actually going to turn on what context it is
being used and by what types of users.
And basically advise the president to hold off on restrictive measures.
And I think this was the right decision in a number of ways.
But especially by analogy to a similar fight over tech policy that we had
in the 90s, that we haven't talked about yet, but that has to be talked
about in this context, which is the fight over open source encryption
code in the 90s and early 2000s.
I think we have a very similar issue with AI today.
And this is now I'll talk about China, because it's a very similar situation
where in the encryption case, it was.
We are afraid our adversaries will misuse this technology and
therefore we want to try to limit the spread of this technology.
But the technology we're talking about is literally bits.
It's not a physical thing that we can physically constrain.
And so it's really hard to prevent the spread of software on a global internet.
And that's as true today, if not more than it was then.
And there are those that are concerned that if we are doing open source AI models
that We are giving away our IP to our competitors in China, that we are giving
away software that could be integrated into military operations by China.
Both of those are true in the sense that certainly there will be stakeholders in
China that make use of this software, but.
Even if our software is not available, they will make use of similar software,
whether it comes from France or the United Arab Emirates or China itself, where
there are plenty of large models being created today without reliance on U.
S.
technology.
So I think it's a question of, yes, we could attempt to restrict this.
We would probably fail.
It would probably leak anyway, but even if we did not fail, it
likely wouldn't be effective.
in preventing China from getting comparable technology elsewhere.
Meanwhile, we will have slowed down our own innovation cycle, slowed down the
diffusion of the technology generally, and the many economic benefits and
other benefits that will accrue from that, and have hindered our ability
to compete in the global marketplace.
Around this kind of software, which I think is especially important to consider
when you think about China's geopolitical position right now and things like
the Belt and Road Initiative, which is its massive global attempt to provide
infrastructure, including communications infrastructure to the global South,
to Africa, to South America, to the Middle East as a way of consolidating
geopolitical power, our having a robust open ecosystem of AI models.
Is probably our best shot at preventing China from dominating the
AI market in those global markets.
And they're actually at a unique disadvantage compared to our open
models because, because they are China.
The CCP has already passed regulations that basically enforce
ideological purity on Chinese models.
Basically, censorship in line with The party line in china, which makes
them necessarily much less useful to a lot of stakeholders So if we can
compete on a global stage With free, powerful, customizable AI software,
and China is competing with less good software, then that actually strengthens
our geopolitical position in regard to China, rather than weakening it.
Would be Our argument and the argument at this point of a number of relatively
conservative stakeholders as well as your center left types like me Okay,
there's a lot more that I would love to get into this with you about but
we have limited time I don't want to just ask one or two more questions.
Sure One is that the company used to work for, Meta, is the only one
of the major frontier AI labs that, that seems to be pushing forward with
releasing its most powerful models, the LLAMA models as open weights.
So can you understand, of course, you, you are not speaking for them.
Can you talk a little bit about.
Maybe why it seems like they've taken that direction and how is that going
to affect this debate that it's not now just an issue about AI safety, but it
plays into all of the competitive and other debates in the technology industry.
Sure.
Yeah, I'm not going to attempt to divine the mind of Mark
Zuckerberg or say anything.
Confidential that I may or may not have learned inside of Meta, but I
can note some things that other people have said and some obvious facts.
I think one of them is simple is the simple fact that Facebook now
Meta doesn't have a cloud services division through which it wants
to sell access to closed models.
It's it's whole business model is different.
It offers consumer products.
To consumers that rely on AI, but the them dominating the AI
model market is not their goal.
As one could argue is the goal of your Google's, your Microsoft's,
your Amazon's, your open AI's.
So they just have different incentives.
One of those incentives is by opening their largest model, which is also,
they've said the model that is going to be the primary basis of a lot of features
in their products, they can get the traditional benefits of open development.
They can get those millions of eyes on their, on their model.
They can benefit from seeing what customizations people
are doing on platforms like.
GitHub or HuggingFace and integrating the good ones into their production model.
And then there's also simply finally the obvious fact that it is what
they meta when ChachiTP dropped.
Meta had long been doing open source releases of research models.
Some may recall the dubious release of Galactica shortly before ChachiTP,
which was a, an LLM trained on and focused on scientific literature.
That spouted nonsense and was taken down pretty quickly.
And so you already had this large research investment in developing LLM technology
that wasn't necessarily being productized.
And there's the obvious fact that if.
Meta threw that out there while open AI and Microsoft were trying
to consolidate a closed AI position.
It would slow down those competitors.
Many of your listeners may have heard of the memo inside of Google.
I believe it was called open AI has no moat and neither do we in the sense of
a competitive moat against open source.
And I think.
People inside of Facebook probably followed a similar logic.
I am neither confirming or denying.
And in fact, I was on parental leave for much of this decision making, but
recognized that offering this would not only create a lot of benefit and use
for a lot of people, but also slow the adoption of closed models offered by
their competitors, who again, Meta doesn't offer a cloud service, but it does offer
a lot of other services that are in competition with a lot of these companies.
And so I think as a number of commentators have noted, that was a
clear benefit to Facebook if they could throw an obstacle in front of their
fast moving opponents in the market.
As I make the distinction between open weights and open source, Meta is
a good example of why we need to be really careful about our terminology
there, because Mark Zuckerberg loves to call LLAMA open source and use
that terminology, but the license that Meta is licensing under is
definitely not an open source license.
In some ways that are arguably good and in some ways that are arguably bad.
Most notably, their original license forbade you from building
a model using outputs from their model, which is actually a very
powerful way of building smaller, more condensed versions of models.
Thankfully, they fixed that.
But now if you do that, your model has to be Marked as built with llama and you
don't typically see that kind of branding requirement in the open source community
And so I'd love for them to get rid of that and then also as has been much
commented on they also have a clause that prohibits any company with more than 700
million average monthly users To use the model this is to basically prevent google
and microsoft from using the model for free And if they want to use it, they'll
have to go to to meta to license it again Like that's not the sort of thing you'd
see in a regular open source license carving out particular users At least
it is limited to Companies that had that level of users at the time of the license.
So it's not like you could build your company on LLAMA, ultimately get to 750
and then suddenly have the bottom pulled out of your business, but it's still fair
to say not anti competitive and something that, that especially if they want to
keep calling it open source, they need to take out of their license or they
need to stop calling it open source.
All right, there's a lot more that we could talk about here Obviously the
ntia report will not be the last word, but we're going to need to wrap up for
time kevin Thank you so much for going through and in so much detail this really
important set of issues with us Anytime.
It was a pleasure.
Great to have you.
This has been The Road to Accountable AI.
If you like what you're hearing, please give us a good review and
check out my sub stack for more insights on AI accountability.
Thank you for listening.
This is Kevin Werbeck.
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