Speaker 1: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industri's iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
Speaker 1: we're continuing our conversation with new creators visionaries who are
Speaker 1: creatively applying technology in business to drive change, but with
Speaker 1: a focus on the transformative power of artificial intelligence and
Speaker 1: what it means to leverage AI as a game changing
Speaker 1: multiplier for your business. Our guest today is Christina Montgomery,
Speaker 1: IBM's Chief Privacy and Trust Officer. She's also chair of
Speaker 1: IBM's AI Ethics Board. In addition to overseeing IBM's privacy policy,
Speaker 1: a core part of Christina's job involves AI governance, making
Speaker 1: sure the way AI is used complies with the international
Speaker 1: legal regulations customized for each industry. In today's episode, Ristina
Speaker 1: will explain why businesses need foundational principles when it comes
Speaker 1: to using technology, why AI regulation should focus on specific
Speaker 1: use cases over the technology itself, and share a little
Speaker 1: bit about her landmark congressional testimony. Last May, Christina spoke
Speaker 1: with doctor Lori Santos, host of the Pushkin podcast The
Speaker 1: Happiness Lab, a cognitive scientist and psychology professor at Yale University.
Speaker 1: Laurie is an expert on human happiness and cognition. Okay,
Speaker 1: 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 protects 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 you joined us here on smart talks back
Speaker 2: in twenty twenty one, and you chatted with us about
Speaker 2: IBM's approach of building trust and transparency with AI, and
Speaker 2: that was only two years ago. But it almost feels
Speaker 2: like in eternity has happened in the field of AI
Speaker 2: since then, and so I'm curious how much has changed
Speaker 2: since you were here last time. We're the things you
Speaker 2: told us before you are they still true? How are
Speaker 2: 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 apply 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 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 our
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 regulations
Speaker 2: the mitigation of bias. And this is something I think
Speaker 2: about a ton as a psychologist, right, you know, I know,
Speaker 2: like my students and everyone who's interacting with AI is
Speaker 2: assuming that the kind of knowledge that they're getting from
Speaker 2: this kind of learning is accurate, right, But of course
Speaker 2: AI is only as good as the knowledge that's going in.
Speaker 2: And so talk to me a little bit about like
Speaker 2: why bias occurs in AI and the level of the
Speaker 2: problem that we're really dealing with.
Speaker 3: Yeah, Well, obviously AI is based on data, rights 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 biased 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 it.
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 the 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 place,
Speaker 3: and where you're going to use and apply it across
Speaker 3: 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 appbsolutely 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 like.
Speaker 3: We believe that consumers should always know when they're interacting
Speaker 3: with an AI system, So be transparent, don't hydra AI
Speaker 3: clearly define the risks. So as a country, we need
Speaker 3: to have some clear guidance right in globally, as well
Speaker 3: in terms of which uses of AI or higher risk
Speaker 3: where will apply higher and stricter regulation and have sort
Speaker 3: of a common understanding of what those high risk uses
Speaker 3: are and then demonstrate the impact in the cases of
Speaker 3: those higher risk uses. So companies who are using AI
Speaker 3: in spaces where they can impact people's legal rights, example,
Speaker 3: should have to conduct an impact assessment that demonstrates that
Speaker 3: the technology isn't biased. So we've been pretty clear about
Speaker 3: apply that the most stringent regulation to the highest risk
Speaker 3: 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 be able to
Speaker 3: do that and to be one of the first set
Speaker 3: of invitees to the first hearing, and what I learned
Speaker 3: from it essentially is you know, really two things. The
Speaker 3: first is really the value of authenticity. So both as
Speaker 3: an individual and as a company, I was able to
Speaker 3: talk about out what I do. You know, I need
Speaker 3: a lot of advanced prep right. I talked about what
Speaker 3: my job is, what IBM has been putting in place
Speaker 3: for years now. So this isn't about creating something. This
Speaker 3: was just about showing up and being authentic. And we
Speaker 3: were invited for a reason. We were invited because we
Speaker 3: were one of the earliest companies in the AI technology space.
Speaker 3: We're the oldest technology company and we are trusted and
Speaker 3: that's an honor. And then the second thing I came
Speaker 3: away with was really how important this issue is to society.
Speaker 3: I don't think I appreciated it as much until following
Speaker 3: that experience. I had outreached from colleagues I hadn't worked
Speaker 3: with for years. I had an outreach from family members
Speaker 3: who heard me on the radio. You know, my mother
Speaker 3: and my mother in law, and my nieces and nephews
Speaker 3: and my friends of my kids were all like, oh,
Speaker 3: I get it, I get what you do. Now, Wow,
Speaker 3: that's pretty cool, you know. So that was really probably
Speaker 3: the best, the 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 an ever 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. Of 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: So this is really where I think a strong AI
Speaker 2: governance comes in. Talk about from your perspective, how this
Speaker 2: really contributes to maintaining the trust that customers and stakeholders
Speaker 2: 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 manage is the alignment
Speaker 3: of your AI models over time to make sure as
Speaker 3: AI learns and evolves over uses, which is in large
Speaker 3: part what makes it so beneficial that it stays in
Speaker 3: alignment with the objectives that you set for the technology
Speaker 3: over time. So you can't do that without a robust
Speaker 3: governance process in place. So we work with clients to
Speaker 3: share our own story here at IBM in terms of
Speaker 3: how we put that in place, but also in our
Speaker 3: consulting practice to help clients work with these new generative
Speaker 3: capabilities and foundation models and the like in order to
Speaker 3: put them to work for their business in a way
Speaker 3: that's going to be impactful to that business but at
Speaker 3: the same time be trusted.
Speaker 2: And so now I wanted to turn a little bit
Speaker 2: towards watsonex governance, and so IBM recently announced their AI
Speaker 2: platform Watson X, which will include a governance component. Could
Speaker 2: you tell us a little more about watsonex dot Governance.
Speaker 3: Yeah, I mean before do that, I'll just back up
Speaker 3: and talk about the full platform and then lean into
Speaker 3: Watson X because I think it's important to understand the
Speaker 3: 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: you know, for our watsonex dot AI for example, that's
Speaker 3: the studio to train new foundation models and generative AI
Speaker 3: and machine learning capabilities. And we are populating that studio
Speaker 3: with some IBM trained foundation models which we're curating and
Speaker 3: tailoring more specifically for enterprises. So that's really important. It
Speaker 3: comes back to the point I made earlier about business
Speaker 3: trust and the need you know, to have enterprise ready
Speaker 3: technologies in the AI space. And then the watsonex dot
Speaker 3: data is a fit for purpose data store or a
Speaker 3: data lake. And then watsonex dot gov so that's a
Speaker 3: particular component of the platform that my team and the
Speaker 3: AI Ethics Board has really worked closely with the product
Speaker 3: team on developing, and we're using it internally here in
Speaker 3: the Chief Privacy Office as well to help us govern
Speaker 3: our own uses of AI technology and our compliance program here,
Speaker 3: and it essentially helps to notify you if a model
Speaker 3: becomes biased or gets out of alignment as you're using
Speaker 3: it over time. So companies are going to need these capabilities.
Speaker 3: I mean they need them today to deliver technologies with trust.
Speaker 3: They'll need them tomorrow to comply with regulation, which is
Speaker 3: 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 any on any company's legal teams keeping up
Speaker 2: with this these days. But my question for you is
Speaker 2: really how can businesses develop a strategy to maintain compliance
Speaker 2: 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 is
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 up baseline and a lot of
Speaker 3: companies do that 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 governance.
Speaker 3: Think 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: putting it into place today. Don't wait for regulation to
Speaker 3: apply directly to your business, because it'll be 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.
Speaker 2: And with this transformation happening at such a rapid pace.
Speaker 2: Do you think creativity plays a role in how you
Speaker 2: think about and implement specifically a trust where the 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 in an AI Governance
Speaker 3: certification program. I sit on their advisory board. But that's
Speaker 3: just emblematic of the fact that the field is changing
Speaker 3: 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 techniques because
Speaker 3: 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: 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 Christina for joining me on smart
Speaker 2: 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 regulatory 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 Romano, David
Speaker 1: jaw Nische, Venkat and Royston Deserve with Jacob Goldstein. We're
Speaker 1: edited by Lydia Jene Kott. Our engineer is Jason Gambrel.
Speaker 1: Theme song by Gramoscope Special thanks to Carli Migliori, Andy Kelly,
Speaker 1: Kathy Callahan, and the eight Bar and IBM teams, as
Speaker 1: well as the Pushkin Marketing team. Smart Talks with IBM
Speaker 1: is a production of Pushkin Industries and Ruby Studio at iHeartMedia.
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