Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. This season
Speaker 1: on smart Talks with IBM, Malcolm Gladwell and team are
Speaker 1: diving into the transformative world of artificial intelligence with a
Speaker 1: fresh perspective on the concept of open What does open
Speaker 1: really mean in the context of AI. It can mean
Speaker 1: open source code or open data, but it also encompasses
Speaker 1: fostering an ecosystem of ideas, ensuring diverse perspectives are heard,
Speaker 1: and enabling new levels of transparency. Join hosts from your
Speaker 1: favorite pushkin podcasts as they explore how openness and AI
Speaker 1: is reshaping industries, driving innovation, and redefining what's possible. You'll
Speaker 1: hear from industry experts and leaders about the implications and
Speaker 1: possibilities of open AI, and of course, Malcolm Gladwell will
Speaker 1: be there to guide you through the season with his
Speaker 1: unique insights. Look out for new episodes of Smart Talks
Speaker 1: every other week on the iHeartRadio app, Apple Podcasts, or
Speaker 1: wherever you get your podcasts, and learn more at IBM
Speaker 1: dot com slash smart Talks.
Speaker 2: Hey Malcolm Glabell, here, I'm back in your feed today
Speaker 2: because we are re releasing an episode of Smart Talks
Speaker 2: with IBM on a very timely topic, AI governance and
Speaker 2: why regulation is critical to building responsible and accountable AI.
Speaker 2: I hope you enjoy it. Hello, Hello, Welcome to Smart
Speaker 2: Talks with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM.
Speaker 2: I'm Malcolm Glabwell. This season, we're continuing our conversation with
Speaker 2: new creators visionaries who are creatively applying technology in business
Speaker 2: to drive change, but with a focus on the transformative
Speaker 2: power of artificial intelligence and what it means to leverage
Speaker 2: AI as a game changing multiple for your business. Our
Speaker 2: guest today is Christina Montgomery, IBM's Chief Privacy and Trust Officer.
Speaker 2: She's also chair of IBM's AI Ethics Board. In addition
Speaker 2: to overseeing IBM's privacy policy, A core part of Christina's
Speaker 2: job involves AI governance, making sure the way AI is
Speaker 2: used complies with the international legal regulations customized for each industry.
Speaker 2: In today's episode, Christina will explain why businesses need foundational
Speaker 2: principles when it comes to using technology, why AI regulation
Speaker 2: should focus on specific use cases over the technology itself,
Speaker 2: and share a little bit about her landmark congressional testimony.
Speaker 2: Last May, Christina spoke with doctor Lori Santos, host of
Speaker 2: the Pushkin podcast The Happiness Lab, a cognitive scientist and
Speaker 2: psychology professor at Yale University, Laurie is an expert on
Speaker 2: human happiness and cognition. Okay, let's get to the interview.
Speaker 3: So Christina, I'm so excited to talk to you today.
Speaker 3: So let's start by talking a little bit about your
Speaker 3: role at IBM. What does a Chief Privacy and Trust
Speaker 3: Officer actually do.
Speaker 4: It's a really dynamic profession and it's not a new profession,
Speaker 4: but the role has really changed. I mean, my role
Speaker 4: today is broader than just helping to ensure compliance with
Speaker 4: data protection laws globally. I'm also responsible for AI governance
Speaker 4: I co chair or AI Ethics Board here at IBM,
Speaker 4: and for data clearance in data governance as well for
Speaker 4: the company. So I have both a compliance aspect to
Speaker 4: my role, really important on a global basis, but also
Speaker 4: help the business to competitively differentiate, because really trust is
Speaker 4: a strategic advantage for IBM and a competitive differentiator as
Speaker 4: a company that's been responsibly managing the most sensitive data
Speaker 4: for our clients for more than a century now and
Speaker 4: helping to usher new technologies into the world with trust
Speaker 4: and transparency, and so that's also a key aspect of
Speaker 4: my role.
Speaker 3: And so you joined us here on smart Talks back
Speaker 3: in twenty twenty one and you chatted with us about
Speaker 3: IBM's approach of building trust and transparency with AI, and
Speaker 3: that was only two years ago. But it almost feels
Speaker 3: like an eternity has happened in the field of AI
Speaker 3: since then, And so I'm curious how much has changed
Speaker 3: since you were here last time. Were the things you
Speaker 3: told us before, you know, are they still true? How
Speaker 3: are things changing?
Speaker 4: You're absolutely right, it feels like the world has changed
Speaker 4: really in the last two years. But the same fundamental
Speaker 4: principles and the same overall governance applied to IBM's program
Speaker 4: for data protection and responsible AI that we talked about
Speaker 4: two years ago, and not much has changed there from
Speaker 4: our perspective. And the good thing is we've put these
Speaker 4: practices and this governance approach into place, and we've had
Speaker 4: an established way of looking at these emerging technologies. As
Speaker 4: the technology evolves, the tech is more powerful, for sure,
Speaker 4: foundation models are vastly larger and more capable and are
Speaker 4: creating in some respects new issues. But that just makes
Speaker 4: it all the more urgent to do what we've been
Speaker 4: doing and to put trust and transparency into place across
Speaker 4: the business to be accountable to those principles.
Speaker 3: And so our conversation today is really centered around this
Speaker 3: need for new AI regulation and part of that regulation
Speaker 3: involves the mitigation of bias. And this is something I
Speaker 3: think about a ton as a psychologist, right, you know,
Speaker 3: I know, like my students and everyone who's interacting with
Speaker 3: AI is assuming that the kind of knowledge that they're
Speaker 3: getting from this kind of learning is accurate, right, But
Speaker 3: of course AI is only as good as the knowledge
Speaker 3: that's going in. And so talk to me a little
Speaker 3: bit about like why bias occurs in AI and the
Speaker 3: level of the problem that we're really dealing with.
Speaker 4: Yeah, well, obviously AI is based on data, right, It's
Speaker 4: trained with data, and that data could be biased in
Speaker 4: and of itself, and that's where issues could come up.
Speaker 4: They come up in the data, they could also come
Speaker 4: up in the output of the models themselves. So it's
Speaker 4: really important that you build bias consideration and bias testing
Speaker 4: into your product development cycle. And so what we've been
Speaker 4: thinking about here at IBM and doing we had some
Speaker 4: of our research teams delivered some of the very first
Speaker 4: toolkits to help detect bias years ago now right and
Speaker 4: deploy them to open source, and we have put into
Speaker 4: place for our developers here at IBM and Ethics by
Speaker 4: Design playbook that's sort of a step by step approach
Speaker 4: which also addresses very fully biased considerations, and we provide
Speaker 4: not only like here's a point when you should test
Speaker 4: for it and you consider it in the data, you
Speaker 4: have to measure it both at the data and the
Speaker 4: model level or the outcome level, and we provide guidance
Speaker 4: with respect to what tools can best be used to
Speaker 4: accomplish that. So it's a really important issue. It's one
Speaker 4: you can't just talk about. You have to provide essentially
Speaker 4: the technology and the capabilities and the guidance to enable
Speaker 4: people to test for it.
Speaker 3: Recently, you had this wonderful opportunity to head to Congress
Speaker 3: to talk about AI, and in your testimony before Congress
Speaker 3: you mentioned that it's often said that innovation moves too
Speaker 3: fast for government to keep up. And this is something
Speaker 3: that I also worry about as a psychologist. Right our
Speaker 3: policy makers really understanding the issues that they're dealing with,
Speaker 3: and so I'm curious how you're approaching this challenge of
Speaker 3: adapting AI policies to keep up with the sort of
Speaker 3: rapid pace of all the advancements we're seeing in the
Speaker 3: AI technology itself.
Speaker 4: I think it's really critically important that you have foundational
Speaker 4: principles that applied to not only how you use technology,
Speaker 4: but whether you're going to use it in the first
Speaker 4: place and where you're going to use and apply it
Speaker 4: across your company. And then your program from a governance perspective,
Speaker 4: has to be agile. It has to be able to
Speaker 4: address emerging capabilities, new training methods, etc. And part of
Speaker 4: that involves helping to educate and instill and empower a
Speaker 4: trustworthy culture at a company so you can spot those
Speaker 4: issues so you can ask the right questions at the
Speaker 4: right time if you try. We talked about during the
Speaker 4: Senate hearing, and IBM's been talking for years about regulating
Speaker 4: the use, not the technology itself, because if you try
Speaker 4: to regulate technology, you're very quickly going to find out
Speaker 4: regulation will absolutely never keep up with that.
Speaker 3: And so in your testimony to Congress, you also talked
Speaker 3: about this idea of a precision regulation approach for AI.
Speaker 3: Tell me more about this. What is a precision regulation approach,
Speaker 3: and why could that be so important.
Speaker 4: It's funny because I was able to share with Congress
Speaker 4: our precision regulation point of view in twenty twenty three,
Speaker 4: but that precision regulation point of view was published by
Speaker 4: IBM in twenty twenty. So we have not changed our
Speaker 4: position that you should apply the tightest controls, the strictest
Speaker 4: regulatory requirements to the technology where the end use and
Speaker 4: risk of societal harm is the greatest. So that's essentially
Speaker 4: what it is. There's lots of AI technology that's used
Speaker 4: today that doesn't touch people, that's very low risk in nature.
Speaker 4: And even when you think about AI that delivers a
Speaker 4: movie recommendation versus AI that is used to diagnose cancer, right,
Speaker 4: there's very different implications associated with those two uses of
Speaker 4: the technology. And so essentially what precision regulation is is
Speaker 4: apply different rules to different risks, right, more stringent regulation
Speaker 4: to the use cases with the greatest risk. And then
Speaker 4: also we build that out calling for things like transparency.
Speaker 4: You see it today with content right, misinformation and the like.
Speaker 4: We believe that consumers should always know when they're interacting
Speaker 4: with an AI system, So be transparent, don't hyd your
Speaker 4: AI define the risks. So as a country, we need
Speaker 4: to have some clear guidance right and globally as well
Speaker 4: in terms of which uses of AI or higher risk
Speaker 4: where we'll apply higher and stricter regulation and have sort
Speaker 4: of a common understanding of what those high risk uses
Speaker 4: are and then demonstrate the impact in the cases of
Speaker 4: those higher risk uses. So companies who are using AI
Speaker 4: in spaces where they can impact people's legal rights, for example,
Speaker 4: should have to conduct an impact assessment that demonstrates that
Speaker 4: the technology isn't biased. So we've been pretty clear about
Speaker 4: apply the most stringent regulation to the highest risk uses
Speaker 4: of AI.
Speaker 3: And so so far we've been talking about your congressional
Speaker 3: testimony in terms of, you know, the specific content that
Speaker 3: you talked about, But I'm just curious on a personal level,
Speaker 3: you know, what was that like right like right now
Speaker 3: it feels like at a policy level, like there's a
Speaker 3: kind of fever pitch going on with AI right now.
Speaker 3: What did that feel like to kind of really have
Speaker 3: the opportunity to talk to policy makers and sort of
Speaker 3: influence what they're thinking about AI technologies like in the
Speaker 3: coming century.
Speaker 4: Perhaps I was really an honor to be able to
Speaker 4: do that and to be one of the first set
Speaker 4: of invitees to the first hearing, and what I learned
Speaker 4: from it essentially is, you know, really two things. The
Speaker 4: first is really the value of authenticity. So both as
Speaker 4: an individual and as a company, I was able to
Speaker 4: talk about what I do. You know, I did need
Speaker 4: a lot of advanced prep, right. I talked about what
Speaker 4: my job is, what IBM has been putting in place
Speaker 4: for years now. So this isn't about creating something. This
Speaker 4: was just about showing up and being authentic. And we
Speaker 4: were invited for a reason. We were invited because we
Speaker 4: were one of the earliest companies in the AI technology space.
Speaker 4: We're the oldest technology company and we are trusted and
Speaker 4: that's an honor. And then the second thing I came
Speaker 4: away with was really how important this issue is to society.
Speaker 4: I don't think I appreciated it as much until following
Speaker 4: that experience. I had outreached from colleagues I hadn't worked
Speaker 4: with for years. I had an outreach from family members
Speaker 4: who heard me on the radio. You know, my mother
Speaker 4: and my mother in law, and my nieces and nephews
Speaker 4: and my friends of my kids were all like, oh,
Speaker 4: I get it. I get what you do. Now, Wow,
Speaker 4: that's pretty cool, you know So that was really probably
Speaker 4: the best and most impactful takeaway that I had.
Speaker 2: The mass adoption of generative AI, happening at breakneck speed,
Speaker 2: has spurred societies and governments around the world to get
Speaker 2: serious about regulating AI. For businesses, compliance is complex enough already,
Speaker 2: but throw anever involving technology like AI into the mix,
Speaker 2: and compliance itself becomes an exercise in adaptability. As regulators
Speaker 2: seek greater accountability in how AI is used, businesses need
Speaker 2: help creating governance processes comprehensive enough to comply with the law,
Speaker 2: but agile enough to keep up with the rapid rate
Speaker 2: of change in AI development. Regulatory scrutiny isn't the only
Speaker 2: consideration either responsible AI governance. A business's ability to prove
Speaker 2: its AI models are transparent and explainable is also key
Speaker 2: to building trust with customers, regardless of industry. In the
Speaker 2: next part of their conversation, Laurie asked Christina what businesses
Speaker 2: should consider when approaching AI governance. Let's listen, So.
Speaker 3: What's a particular role that businesses are playing in AI governance? Like,
Speaker 3: why is it so critical for businesses to be part
Speaker 3: of this.
Speaker 4: So I think it's really critically important that businesses understand
Speaker 4: the impacts that technology can have, both in making them
Speaker 4: better businesses, but the impacts that those technologies can have
Speaker 4: on the consumers that they are supporting. You know, businesses
Speaker 4: need to be deploying AI technology that is in alignment
Speaker 4: with the goals that they set for it, and that
Speaker 4: can be trusted. I think for us and for our clients,
Speaker 4: a lot of this comes back to trust in tech.
Speaker 4: If you deploy something that doesn't work, that hallucinates, that discriminates,
Speaker 4: that isn't transparent, where decisions can't be explained, then you
Speaker 4: are going to very rapidly erode the trust at best
Speaker 4: right of your clients and at worst for yourself. You're
Speaker 4: going to create legal and regulatory issues for yourself as well.
Speaker 4: So trusted technology is really important, and I think there's
Speaker 4: a lot of pressure on businesses today to move very
Speaker 4: rapidly and adopt technology. But if you do it without
Speaker 4: having a program of governance in place, you're really risking
Speaker 4: eroding that trust.
Speaker 3: And so this is really where I think a strong
Speaker 3: AI governance comes in. You know, talk about from your
Speaker 3: perspective how this really contributes to me containing the trust
Speaker 3: that customers and stakeholders have in these technologies.
Speaker 4: Yeah. Absolutely. I mean you need to have a governance
Speaker 4: program because you need to understand that the technology, particularly
Speaker 4: in the AI space, that you are deploying, is explainable.
Speaker 4: You need to understand why it's making decisions and recommendations
Speaker 4: that it's making, and you need to be able to
Speaker 4: explain that to your consumers. I mean, you can't do
Speaker 4: that if you don't know where your data is coming from,
Speaker 4: what data are you using to train those models, if
Speaker 4: you don't have a program that manages the alignment of
Speaker 4: your AI models over time to make sure as AI
Speaker 4: learns and evolves over uses, which is in large part
Speaker 4: what makes it so beneficial that it stays in alignment
Speaker 4: with the objectives that you set for the technology over time.
Speaker 4: So you can't do that without a robust governance process
Speaker 4: in place. So we work with clients to share our
Speaker 4: own story here at IBM in terms of how we
Speaker 4: put that in place also in our consulting practice to
Speaker 4: help clients work with these new generative capabilities and foundation
Speaker 4: models and the like in order to put them to
Speaker 4: work for their business in a way that's going to
Speaker 4: be impactful to that business, but at the same time
Speaker 4: be trusted.
Speaker 3: So now I wanted to turn a little bit towards
Speaker 3: Watson x governance, and so IBM recently announced their AI platform,
Speaker 3: Watson X, which will include a governance component. Could you
Speaker 3: tell us a little more about watsonx dot governance.
Speaker 4: Yeah, I mean before I do that, I'll just back
Speaker 4: up and talk about the full platform and then lean
Speaker 4: into Watson X because I think it's important to understand
Speaker 4: the delivery of a full suite of capabilities to get data,
Speaker 4: to train models, and then to govern them over their
Speaker 4: life cycle. All of these things are really important. From
Speaker 4: the onset you need to make sure that you have.
Speaker 4: For our watsonex dot AI for example, that's the studio
Speaker 4: to train new foundation models and generative AI and machine
Speaker 4: learning capabilities. And we are populating that studio with some
Speaker 4: IBM trained foundation models which we're curating and tailoring more
Speaker 4: specifically for enterprises. So that's really important. It comes back
Speaker 4: to the point I made earlier about business trust and
Speaker 4: the need to have enterprise ready technologies in the AI space.
Speaker 4: And then the watsonex dot data is a fit for
Speaker 4: purpose data store or a data Lake and then watsonex
Speaker 4: dot gov. So that's a particular component of the platform
Speaker 4: that my team and the AI Ethics Board has really
Speaker 4: worked closely with the product team on developing, and we're
Speaker 4: using it internally here in the Chief Privacy Office as
Speaker 4: well to help us govern our own uses of AI
Speaker 4: technology and our compliance program here, and it essentially helps
Speaker 4: to notify you if a model becomes biased or gets
Speaker 4: out of alignment as you're using it over time. So
Speaker 4: companies are going to need these capabilities. I mean they
Speaker 4: need them today to deliver technologies with trust. They'll need
Speaker 4: them tomorrow to comply with regulation which is on the horizon.
Speaker 3: And I think compliance becomes even more complex when you
Speaker 3: consider international data protection laws and regulations. Honestly, I don't
Speaker 3: know how anyone on any company's legal team is keeping
Speaker 3: up with us these days. But my question for you
Speaker 3: is really how can businesses develop a strategy to maintain
Speaker 3: compliance and to deal with it in this ever changing landscape.
Speaker 4: It's increasingly more challenging. In fact, I saw statistic just
Speaker 4: this morning that the regulatory obligations on companies have increased
Speaker 4: something like seven hundred times in the last twenty years.
Speaker 4: So it really is a huge focus area for companies.
Speaker 4: You have to have a process in place in order
Speaker 4: to do that, and it's not easy, particularly for a
Speaker 4: company like IBM that it has a presence in over
Speaker 4: one hundred and seventy countries around the world. There is
Speaker 4: more than one hundred and fifty comprehensive privacy regulations, there
Speaker 4: are regulations of non personal data, there are AI regulations emerging,
Speaker 4: So you really need an operational approach to it in
Speaker 4: order to stay compliant. But one of the things we
Speaker 4: do is we set a baseline. And a lot of
Speaker 4: companies do this as well. So we define a privacy baseline,
Speaker 4: we define an AI baseline, and we ensure then as
Speaker 4: a result of that that there are very few deviances
Speaker 4: because it incorporates in that baseline. So that's one of
Speaker 4: the ways we do it. Other companies, I think are
Speaker 4: similarly situated in terms of doing that. But again, it
Speaker 4: is a real challenge for global companies. It's one of
Speaker 4: the reasons why we advocate for as much alignment as
Speaker 4: possible on the international realm as well as nationally here
Speaker 4: in the US, as much alignment as possible to make
Speaker 4: compliance easier for easier and not just because companies want
Speaker 4: an easy way to comply. But the harder it is,
Speaker 4: the less likely there will be compliance. And it's not
Speaker 4: the objective of anybody, governments, companies, consumers to have to
Speaker 4: set legal obligations that companies simply can't meet.
Speaker 3: So what advice would you give to other companies who
Speaker 3: are looking to rethink or strengthen their approach to AI government.
Speaker 4: Think you need to start with, as we did, foundational principles,
Speaker 4: and you need to start making decisions about what technology
Speaker 4: you're going to deploy and what technology you're not, What
Speaker 4: are you going to use it for, and what aren't
Speaker 4: you going to use it for? And then when you
Speaker 4: do use it, align to those principles. That's really important.
Speaker 4: Formalize a program, have someone within the organization, whether it's
Speaker 4: the chief privacy officer, whether it's some other role, chief
Speaker 4: AI ethics officer, but have an accountable individual and accountable organization.
Speaker 4: Do a maturity assessment, figure out where you are and
Speaker 4: where you need to be, and really start, you know,
Speaker 4: putting it into place today. Don't wait for regulation to
Speaker 4: apply directly to your business because it'll be too late.
Speaker 3: So Smart Talks features new creators these visionaries like yourself
Speaker 3: who are creatively applying technology in business to drive change.
Speaker 3: I'm curious if you see yourself as creative.
Speaker 4: You know, I definitely do. I mean, you need to
Speaker 4: be creative when you're working in an industry that evolves
Speaker 4: so very quickly. So you know, I started with IBM
Speaker 4: when we were primarily a hardware company, right, and we've
Speaker 4: changed our business so significantly over the years. And the
Speaker 4: issues that are raised with respect to each new technology,
Speaker 4: whether it be cloud, whether it be AI now where
Speaker 4: we're seeing a ton of issues, or you look at
Speaker 4: emergent issues in the space of things like neurotechnologies and
Speaker 4: quantum computers. You have to be strategic and you have
Speaker 4: to be creative and thinking about how you can adapt
Speaker 4: agilely quickly a company to an environment that is changing
Speaker 4: so quickly and.
Speaker 3: With this transformation happening at such a rapid pace. Do
Speaker 3: you think creativity plays a role in how you think
Speaker 3: about and implement, specifically a trustworthy AI strategy.
Speaker 4: Yeah, I absolutely think it does, because again, it comes
Speaker 4: back to these capabilities, and there are ways, I guess
Speaker 4: how you define creativity could be different, right, But I'm
Speaker 4: thinking of creativity in the sense of sort of agility
Speaker 4: and strategic vision and creative problem solving. I think that's
Speaker 4: really important in the world that we're in right now,
Speaker 4: being able to creatively problem solve with new issues that
Speaker 4: are rising sort of every day.
Speaker 3: And so, how do you see the role of chief
Speaker 3: privacy officer evolving in the future as AI technology continues
Speaker 3: to advance, Like what stuff should CPOs take to stay
Speaker 3: ahead of all these changes that are come in their way?
Speaker 4: So the role is evolving in most companies, I would
Speaker 4: say pretty rapidly. Many companies are looking to chief privacy
Speaker 4: officers who are ready understand the data that's being used
Speaker 4: in the organization and have programs to ensure compliance with
Speaker 4: laws that require you to manage that data in accordance
Speaker 4: with data protection laws and the like. It's a natural
Speaker 4: place and position for AI responsibility. And so I think
Speaker 4: what's happening to a lot of chief privacy officers is
Speaker 4: they're being asked to take on this AI governance responsibility
Speaker 4: for companies and if not take it on, at least
Speaker 4: play a very key role working with other parts of
Speaker 4: the business in AI governance. So that really is changing.
Speaker 4: And if chief privacy officers are in companies who maybe
Speaker 4: haven't started thinking about AI yet, they should, so I
Speaker 4: would encourage them to look at different resources that are
Speaker 4: available already in AI governance space. For example, the International
Speaker 4: Association of Privacy Professionals, which is the seventy five thousand
Speaker 4: member professional body for the profession of chief Privacy Officers,
Speaker 4: just recently launched an AI Governance Initiative and an AI
Speaker 4: Governance Certification program. I sit on their advisory board. But
Speaker 4: that's just emblematic of the fact that the field is
Speaker 4: changing so rapidly.
Speaker 3: And so, you know, speaking of rapid change. When you're
Speaker 3: back here on smart Talks in twenty twenty one, you
Speaker 3: said that the future of AI will be more transparent
Speaker 3: and more trustworthy. You know, what do you see the
Speaker 3: next five to ten years holding. You know, when you're
Speaker 3: back on smart Talks in you know, twenty twenty six,
Speaker 3: you know twenty thirty, You know what are we going
Speaker 3: to be talking about when it comes to AI technology
Speaker 3: and governance.
Speaker 4: So I try to be an optimist, right And I
Speaker 4: said that two years ago, and I think we're seeing
Speaker 4: it now come into fruition, and there will be requirements,
Speaker 4: whether they're coming from the US, whether they're coming from Europe,
Speaker 4: whether they're just coming from voluntary adoption by clients of
Speaker 4: things like the NISS Risk Management Framework, really important voluntary frameworks.
Speaker 4: You're going to have to adopt transparent and explainable practices
Speaker 4: in your uses of AI. So I do see that happening.
Speaker 4: And in the next five to ten years, boy, I
Speaker 4: think we'll see more research into trust in techniques because
Speaker 4: we don't really know, for example, how to water mark.
Speaker 4: We were calling for things like water marking. There'll be
Speaker 4: more research into how to do that. I think you'll see,
Speaker 4: you know, regulation that's specifically going to require those types
Speaker 4: of things. So I think again, I think the regulation
Speaker 4: is going to drive research. It's going to drive research
Speaker 4: into these areas that will help ensure that we can
Speaker 4: deliver new capabilities, generative capabilities and the like with trust
Speaker 4: and explainability.
Speaker 3: Thank you so much, Christina for joining me on smart
Speaker 3: Talks to talk about AI and governance.
Speaker 4: Well, thank you very much for having me.
Speaker 2: To unlock the transformative growth possible with artificial intelligence. Businesses
Speaker 2: need to know what they wish to grow into first.
Speaker 2: Like Christina said, the best way forward in the AI
Speaker 2: future is for businesses to figure out their own foundational
Speaker 2: principles around using the technology, drawing on those principles to
Speaker 2: apply AI in a way that's ethically consistent with their
Speaker 2: mission and complies with the legal frameworks built to hold
Speaker 2: the technology accountable. As AI adoption grows more and more widespread,
Speaker 2: so too will the expectation from consumers and regulators that
Speaker 2: businesses use it responsibly. Investing independable AI governance is a
Speaker 2: way for businesses to lay the foundations for technology that
Speaker 2: their customers can trust while rising to the challenge of
Speaker 2: increasing regulatory complexity. Though the emergence of AI does complicate
Speaker 2: an already tough compliance landscape, businesses now face a creative
Speaker 2: opportunity to set a precedent for what accountability in AI
Speaker 2: looks like and rethink what it means to deploy trustworthy
Speaker 2: artificial intelligence. I'm Malcolm Gladwell. This is a paid advertisement
Speaker 2: from IBM. Smart Talks with IBM will be taking a
Speaker 2: short hiatus, but look for new episodes in the coming weeks.
Speaker 2: Smart Talks with IBM is produced by Matt Ramano, David
Speaker 2: jaw Nische Venkat and Royston Deserve with Jacob Goldstein. We're
Speaker 2: edited by Lydia Gene Kott. Our engineer is Jason Gambrel.
Speaker 2: Theme song by Gramoscope. Special thanks to Carli Migliori, Andy Kelly,
Speaker 2: Kathy Callahan and the eight Bar and IBM teams, as
Speaker 2: well as the Pushkin marketing team. Smart Talks with IBM
Speaker 2: is a production of Pushkin Industries and Ruby Studio at iHeartMedia.
Speaker 2: To find more Pushkin podcasts, listen on the iHeartRadio app,
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