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