Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Today, we
Speaker 1: are witnessed to one of those rare moments in history,
Speaker 1: the rise of an innovative technology with the potential to
Speaker 1: radically transform business in society forever. That technology, of course,
Speaker 1: is artificial intelligence, and it's the central focus for this
Speaker 1: new season of Smart Talks with IBM. Join hosts from
Speaker 1: your favorite Pushkin podcasts as they talk with industry experts
Speaker 1: and leaders to explore how businesses can integrate AI into
Speaker 1: their workflows and help drive real change in this new
Speaker 1: era of AI, and of course, host Malcolm Gladwell will
Speaker 1: be there to guide you through the season and throw
Speaker 1: in his two cents as well. Look out for new
Speaker 1: episodes of Smart Talks with IBM every other week on
Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts,
Speaker 1: and learn more at IBM dot com slash smart talks.
Speaker 2: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 2: from Pushkin Industri's iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
Speaker 2: we're continuing our conversation with new creators visionaries who are
Speaker 2: creatively applying technology in business to drive change, but with
Speaker 2: a focus on the transformative power of artificial intelligence and
Speaker 2: what it means to leverage AI as a game changing
Speaker 2: multiplier for your business. Our guest today is Christina Montgomery,
Speaker 2: IBM's Chief Privacy and Trust Officer. She's also chair of
Speaker 2: IBM's AI Ethics Board. In addition to overseeing IBM's privacy policy,
Speaker 2: a core part of Christina's job involves AI governance, making
Speaker 2: sure the way AI is used complies with the international
Speaker 2: legal regulations customized for each industry. In today's episode, Christina
Speaker 2: will explain why businesses need foundational principles when it comes
Speaker 2: to using technology, why AI regulation should focus on specific
Speaker 2: use cases over the technology itself, and share a little
Speaker 2: bit about her landmark congressional testimony. Last May, Christina spoke
Speaker 2: with doctor Lori Santos, host of the Pushkin podcast The
Speaker 2: Happiness Lab, a cognitive scientist and psychology professor at Yale University.
Speaker 2: Laurie is an expert on human happiness and cognition. Okay,
Speaker 2: 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 and 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 and 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 in 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 are they still true? How are
Speaker 3: 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 apply 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 have
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 our
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: deployed 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 place,
Speaker 4: and where you're going to use and apply it across
Speaker 4: 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
Speaker 3: approach 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 hydra AI
Speaker 4: clearly define the risks. So as a country, we need
Speaker 4: to have some clear guidance right in globally as well
Speaker 4: in terms of which uses of AI or higher risk
Speaker 4: where will 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 space 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 that the most stringent regulation to the highest risk
Speaker 4: uses 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: You know, what did that feel like to kind of
Speaker 3: really have the opportunity to talk to policy makers and
Speaker 3: sort of influence what they're thinking about AI technologies like
Speaker 3: in the 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 need a
Speaker 4: lot of advanced prep right I talked about what my
Speaker 4: job is, what IBM has been putting in place for
Speaker 4: years now, so this isn't about creating something. This was
Speaker 4: just about showing up and being authentic. And we were
Speaker 4: invited for a reason. We were invited because we were
Speaker 4: 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 you know. So that was really probably the
Speaker 4: 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 an ever 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. Of 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.
Speaker 3: So it's a particular role that businesses are playing in
Speaker 3: AI governance, Like why is it so critical for businesses
Speaker 3: to be part 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
Speaker 4: clients 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: So this is really where I think a strong AI
Speaker 3: governance comes in. Talk about from your perspective, how this
Speaker 3: really contributes to maintaining the trust that customers and stakeholders
Speaker 3: 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, but also in our consulting practice
Speaker 4: to help clients work with these new generative capabilities and
Speaker 4: foundation models and the like in order to put them
Speaker 4: to work for their business in a way that's going
Speaker 4: to be impactful to that business but at the same
Speaker 4: time be trusted.
Speaker 3: So now I wanted to turn a little bit towards
Speaker 3: watsonex governance, and so IBM recently announced their AI platform, watsonex,
Speaker 3: which will include a governance component. Could you tell us
Speaker 3: a little more about watsonex 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: you know, for our watsonex dot AI for example, that's
Speaker 4: the studio to train new foundation models and generative AI
Speaker 4: and machine learning capabilities, and we are populating that studio
Speaker 4: with some IBM trained foundation models which we're curating and
Speaker 4: tailoring more specifically for enterprises. So that's really important. It
Speaker 4: comes back to the point I made earlier about business
Speaker 4: trust and the need you know to have enterprise is
Speaker 4: ready technologies in the AI space. And then the watsonex
Speaker 4: dot data is a fit for purpose data store or
Speaker 4: a data lake, and then watsonex dot gov. So that's
Speaker 4: a particular component of the platform that my team and
Speaker 4: the AI Ethics Board has really worked closely with the
Speaker 4: product team on developing, and we're using it internally here
Speaker 4: in the Chief Privacy Office as well to help us
Speaker 4: govern our own uses of AI technology and our compliance
Speaker 4: program here. And it essentially helps to notify you if
Speaker 4: a model becomes biased or gets out of alignment as
Speaker 4: you're using it over time. So companies are going to
Speaker 4: need these capabilities. I mean they need them today to
Speaker 4: deliver technologies with trust. They'll need them tomorrow to comply
Speaker 4: 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 legal team is keeping
Speaker 3: up with this 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 as 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 obligation 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. Governance.
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, a
Speaker 4: chief AI ethics officer, but have an accountable individual and
Speaker 4: accountable organization. Do a maturity assessment, figure out where you
Speaker 4: are and where you need to be, and really start
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 with.
Speaker 3: This transformation happening at such a rapid pace. Do you
Speaker 3: think creativity is a role in how you think about
Speaker 3: 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, explainable practices in
Speaker 4: 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're calling for things like watermarking. There'll be more research
Speaker 4: into how to do that. I think you'll see regulation
Speaker 4: that's specifically going to require those types of things. So
Speaker 4: I think again, I think the regulation is going to
Speaker 4: drive research. It's going to drive research into these areas
Speaker 4: that will help ensure that we can deliver new capabilities,
Speaker 4: generated capabilities and the like with trust 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 means to deploy
Speaker 2: trustworthy artificial intelligence. I'm Malcolm Gladwell. This is a paid
Speaker 2: advertisement from IBM. Smart Talks with IBM will be taking
Speaker 2: a short hiatus, but look for new episodes in the
Speaker 2: coming weeks. Smart Talks with IBM is produced by Matt Romano,
Speaker 2: David jaw Nische Venkat and Royston Deserve with Jacob Goldstein.
Speaker 2: We're edited by Lydia jen 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,
Speaker 2: Apple podcasts, or wherever you listen to podcasts. H