Speaker 1: Hello, Hello. This is Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries, I Heart Radio and IBM about what
Speaker 1: it means to look at today's most challenging problems in
Speaker 1: a new way. I'm Malcolm Glabbo. Today I'll be chatting
Speaker 1: with two IBM experts in artificial intelligence about the company's
Speaker 1: approach to building and supporting trustworthy AI as a force
Speaker 1: for positive change. I'll be speaking with IBMS Chief Privacy
Speaker 1: Officer Christina Montgomery. She oversees the company's privacy vision and
Speaker 1: compliance strategy globally, looking at things like immunity certificates and
Speaker 1: vaccine passports. Not what could we do, but what were
Speaker 1: we willing as a company to do? Where were we
Speaker 1: going to put our skills and our knowledge and our
Speaker 1: company brand in response to technologies that could help provide
Speaker 1: information in response to the pandemic. She also co chairs
Speaker 1: their AI Ethics Board. I'll also be talking with Dr
Speaker 1: Seth Dobrin, Global Chief AI Officer at IBM. Seth leads
Speaker 1: corporate AI strategy and is responsible for connecting AI development
Speaker 1: with the creation of business value. Seth is also a
Speaker 1: member of IBMS AI Ethics BORT. We want to make
Speaker 1: sure that the technology behind AI is as fair as possible,
Speaker 1: is as explainable as possible, is as robust as possible,
Speaker 1: and is as privacy preserving as possible. We'll talk about
Speaker 1: the need to create AI systems that are fair and
Speaker 1: addressed bias, and how we need to focus on trust
Speaker 1: and transparency to accomplish this. What might the future look
Speaker 1: like with an open and diverse ecosystem with governance across
Speaker 1: the industry. There's only one way to find out. Let's
Speaker 1: I did. One of the things I'm curious about is
Speaker 1: the origin of this interesting concern about the ethics and
Speaker 1: trust component of AI, or is this a later kind
Speaker 1: of of evolutionary concern. About ten years ago, when we
Speaker 1: started down this journey to transforming business using what we
Speaker 1: think about is AI today, the concept of trust came up,
Speaker 1: but not in the same context that we think about
Speaker 1: it today. The context of trust was really focused on
Speaker 1: how do I know it's given me the right answer
Speaker 1: so that I can make my decision. Because we didn't
Speaker 1: have tools that help explain how an AI came to
Speaker 1: a decision, you tended to have to get into these
Speaker 1: bakeoffs where you had to kind of set up experiments
Speaker 1: to show that the AI was at least as good
Speaker 1: as human if if not better, and understand why over
Speaker 1: time it's progressed as AI has in uh started to
Speaker 1: come up against real human conditions. And I think that's
Speaker 1: when we started thinking about what is going on with
Speaker 1: AI when it relates to bias, particularly you know, about
Speaker 1: five eight years ago there was an issue with mortgage
Speaker 1: particularly related to the zip code, but started giving you know,
Speaker 1: biases against people of certain races um and so I
Speaker 1: think those things combined have led to us to the
Speaker 1: point where we are today plufs. You know, the social
Speaker 1: justice movement over the last two years has has really
Speaker 1: accelerated a lot of the concern mm hmm. Because I
Speaker 1: noticed you're you're a lawyer by trade. It's an interesting
Speaker 1: subject because it seems like this is where AI experts
Speaker 1: like Seth and lawyers work together. It sounds like a
Speaker 1: kind of classic cross disciplinary endeavor. Can you talk about
Speaker 1: that a little bit. It's absolutely cross disciplinary in nature.
Speaker 1: For example, our AI Ethics Board, I'm the co chair.
Speaker 1: The other co chair is our AI Ethics Global Leader
Speaker 1: francescar Rossi, who's a well renowned researcher in AI. Ethics,
Speaker 1: so she comes with that research background. So we had
Speaker 1: a board in place, an AI ethics board in place
Speaker 1: before I stepped into this job, and there were a
Speaker 1: lot of great discussions among a lot of researchers and
Speaker 1: a lot of people that deeply understood the technology, but
Speaker 1: it didn't have decision making authority. It didn't have all stakeholders.
Speaker 1: Are many stakeholders across the business at the table, and
Speaker 1: so when I came into the job as a lawyer
Speaker 1: and as somebody with the corporate governance background, I was
Speaker 1: sort of tasked with building out the operational aspects of
Speaker 1: it to make it capable of implementing centralized decision making,
Speaker 1: to give it authority, to bring in those perspectives from
Speaker 1: across the business and from people with different focuses within
Speaker 1: the IBM corporation, lots of different backgrounds, and we have
Speaker 1: very robust conversations, and we also engage the individuals throughout
Speaker 1: IBM who either from an advocacy because they care very
Speaker 1: much about the topic, or they're working in the space
Speaker 1: individually and have thoughts around the topic, are doing projects
Speaker 1: in the space, want to publish in the space. We
Speaker 1: have a very organic way of having them be involved
Speaker 1: as well. Absolutely necessary to have that cross disciplinary aspect.
Speaker 1: You mentioned beginning your answer, talked book about robust conversations
Speaker 1: phrase I love. Can both of you give me an
Speaker 1: example of an issue that's come up with respect to
Speaker 1: trust and AI. So so, one example might be the
Speaker 1: technologies that we would employ as a company in response
Speaker 1: to the COVID nineteen pandemic. So there are a lot
Speaker 1: of things we could have done, and it became a
Speaker 1: question not of what we're capable of deploying from a
Speaker 1: technology perspective, but whether we should be deploying certain technologies,
Speaker 1: whether it be facial recognition for fever detection, certain contact
Speaker 1: tracing technologies are Digital health pass is a good example
Speaker 1: of a technology that came through the board multiple times
Speaker 1: in terms of like if we are going to deploy
Speaker 1: a vaccine passport, which is not necessarily what this technology
Speaker 1: turned out to be, but looking at things like immunity
Speaker 1: certificates and vaccine passports, not what could we do, but
Speaker 1: what were we willing as a company to do? Where
Speaker 1: were we going to put our skills and our knowledge
Speaker 1: and our company brand in response to technologies that could
Speaker 1: help to either bring about a cure or help to
Speaker 1: provide information in response to the pandemic. COVID is a
Speaker 1: great example because it highlights the value and the acceleration
Speaker 1: that good governance can bring. Because the way that we
Speaker 1: as an ethics board laid out the rules, the guardrails,
Speaker 1: if you will, around what we could would and wouldn't
Speaker 1: do for COVID help people just do stuff without worrying
Speaker 1: that we need to bring this to the board. It
Speaker 1: also laid very clear for this type of use case
Speaker 1: we need to go have a conversation with the board.
Speaker 1: It also provided a venue for us as a company
Speaker 1: to make decisions um and make risk based decisions where okay,
Speaker 1: this isn't a little bit of the of the fuzzy area,
Speaker 1: but we think, given what's going on right now in
Speaker 1: the world and the importance of this, we're willing to
Speaker 1: take this risk so long as we go back and
Speaker 1: we clean everything up later. And so so I think
Speaker 1: that's really important that number one, governance is set up
Speaker 1: so that it accelerates things, not stops them. And number two,
Speaker 1: that there's clear guidance into you know, it's not no,
Speaker 1: it's here's what you can do and here's what you
Speaker 1: can't do. And help the teams figure out how they
Speaker 1: can still move things forward in a way that doesn't
Speaker 1: infringe on our principles. Yeah, I want to sort of
Speaker 1: give this there's a concrete sense about how a concern
Speaker 1: about trust and transparency and such would guide what a
Speaker 1: technology company might do. Now a real example, So, if
Speaker 1: I want to make sure that people are wearing face
Speaker 1: masks and then just highlight that there is someone in
Speaker 1: this area that's not wearing a face mask and you're
Speaker 1: not identifying the person, I think we'd be okay with that.
Speaker 1: What we wouldn't be okay with that with is if
Speaker 1: they wanted to identify the person in a way that
Speaker 1: they did not consent to and that was very generic.
Speaker 1: So I'm going to go through a database of unknown
Speaker 1: people and I'm going to match them to this person,
Speaker 1: and so that would not be okay, and a fuzzy
Speaker 1: area would be you know, I'm going to match this
Speaker 1: to a known person, so I know this is an
Speaker 1: employee and I know this is him. This is something
Speaker 1: that we as a board would want to have a
Speaker 1: conversation with. If this employee is not wearing a mask,
Speaker 1: can I match them to a name or do I
Speaker 1: just send a security personnel over here because the employee
Speaker 1: is not wearing a mask. That's a harder I think,
Speaker 1: and that's a real world example that we face during COVID. Yeah,
Speaker 1: let's talk a little bit about diversity and shared responsibility
Speaker 1: as principles that matter in this world of AI. What
Speaker 1: do what do those terms mean as applied to AI,
Speaker 1: and what's the kind of practical effect of seeking to
Speaker 1: optimize those goals? You know, I think first of all,
Speaker 1: we need to have good representation of society doing the
Speaker 1: work that impacts society. So A, it's just the right
Speaker 1: thing to do. B. There's tons of research out there
Speaker 1: that shows that diverse teams outperformed non diverse teams. There's
Speaker 1: a Mackenzie report that says, you know, companies in the
Speaker 1: top quartile for diversity outperformed their peers that aren't by like,
Speaker 1: so tons of good research. The second thing is you
Speaker 1: just don't get as good results when you don't have
Speaker 1: equal representation at the table. There's lots of good examples
Speaker 1: of this. So there was a hiring algorithm that was
Speaker 1: evaluating applicants and passing forward, but all the applicants in
Speaker 1: the past for this company, you know, the vast majority
Speaker 1: of them were male, and so females were just summarily
Speaker 1: wiped out, regardless to some extent of their fit for
Speaker 1: the role. I wanted to ask Castina. A project comes
Speaker 1: before the board, and so a conversation might be the
Speaker 1: team you put together and the data you're looking at
Speaker 1: is insufficiently diverse, We're worried that you're not capturing the
Speaker 1: reality of the of the kind of world we're operating in.
Speaker 1: Is that is that an example of a conversation might
Speaker 1: you might have at the board level. Well, I think
Speaker 1: the best way to look at what the board is
Speaker 1: doing to try to address those issues of bias. I mean, so,
Speaker 1: for example, we've got a team of researchers that work
Speaker 1: on trusted technology, and one of the early things that
Speaker 1: they've done is to deploy toolkits that will help detect bias,
Speaker 1: that will help make a i AM more explainable, that
Speaker 1: will help make it trustworthy in general. But those tools
Speaker 1: initially very focused on bias, and they deployed them to
Speaker 1: open source so they could be built on and improved.
Speaker 1: Right and right now, the board is focused more broadly,
Speaker 1: not looking at it an individual problem in an individual
Speaker 1: use case with respect to bias, but instilling those ethical
Speaker 1: principles across the business through something we're calling ethics by Design.
Speaker 1: Bias was the first focus area of this Ethics by Design,
Speaker 1: and we've got a team of folks being led by
Speaker 1: the Ethics Board who are working on the question you
Speaker 1: asked Malcolm about, how do we ensure that the AI
Speaker 1: we're deploying internally or the tools and the products that
Speaker 1: we're deploying for customers take that into account throughout the
Speaker 1: life cycle of AI. So through this Ethics by Design,
Speaker 1: the guidance that's coming out from the Board starts at
Speaker 1: that conceptual phase and then applies across the life cycle
Speaker 1: up through in the case of an internal use of AI,
Speaker 1: up through the actual use and in the case of
Speaker 1: AI that we're deploying for customers are putting into a product,
Speaker 1: you not through that point of deployment. So it's very
Speaker 1: much about embedding those considerations into our existing processes across
Speaker 1: the company to make sure that they're thought of, not
Speaker 1: just once and not just in the use cases that
Speaker 1: the Board has an opportunity to review, but in our
Speaker 1: practices as a company and in our thinking as a company.
Speaker 1: Much like you know, we did this and companies did
Speaker 1: this years ago, um with respect to privacy and security,
Speaker 1: that concept of privacy and security by design which some
Speaker 1: may be familiar with that stem from the g d
Speaker 1: PR in Europe. Now we're doing the same thing with ethics.
Speaker 1: How unusual is what you guys are doing. I mean
Speaker 1: if I if I lined up all the tech companies
Speaker 1: that are heavily into AI right now, would I find
Speaker 1: similar programs in all of them? Or are you guys
Speaker 1: off by yourselves? So I think we take a little
Speaker 1: bit of a unique perspective. In fact, we were recently
Speaker 1: recognized as a leader in the ethical deployment of technology
Speaker 1: and responsible technology use by the World Economic Forum. So
Speaker 1: World Economic Forum and the Marcola Center Um of Ethics
Speaker 1: at at Santa Clara University did an independent case study
Speaker 1: of IBM that did recognize our leadership in this space.
Speaker 1: Because of the holistic approach that we take, we're a
Speaker 1: little bit different I think in some other tech companies
Speaker 1: that do have similar counsels in place because of the
Speaker 1: broad and cross disciplinary nature of ours. We're not just researchers,
Speaker 1: were not just technologists. We literally have representation from backgrounds
Speaker 1: spanning across the company, whether it be you know, legal
Speaker 1: or developers or researchers or or you know just HR
Speaker 1: professionals and the like. So that makes us a little
Speaker 1: bit unique the program itself. And then I think we
Speaker 1: hear from clients that are thinking for themselves about how
Speaker 1: do I make sure that the technology I'm deploying or
Speaker 1: using externally or with my clients is trustworthy? Right, So
Speaker 1: so they're asking us, how did you go about this,
Speaker 1: how do you think about it as a company, what
Speaker 1: are your practices? So on that point, we are CEO
Speaker 1: is the co chair of a something called the Global
Speaker 1: AI Action Alliance initiated by the WEF, and as part
Speaker 1: of that, we've committed to sort of open source our approach.
Speaker 1: So we've been talking a lot about our approach. I
Speaker 1: think it is a little bit unique, as I said,
Speaker 1: but we are sharing it because again, we don't want
Speaker 1: to be the only ones that have trustworthy the AI
Speaker 1: and that have this holistic, cross disciplinary approach, because we
Speaker 1: think it's the right approach. It's certainly the right approach
Speaker 1: for our company, and we want to share it with
Speaker 1: the world. It's not secret or proprietary, but if you
Speaker 1: talk to the analyst community that serves the tech the
Speaker 1: tech you know, the tech sector. They say far and wide,
Speaker 1: IBM is is is ahead in terms of things that
Speaker 1: we're actually doing as opposed to talking about it all
Speaker 1: while making sure that it is enforceable and impactful. So
Speaker 1: for instance, you know we were talking about we review
Speaker 1: you cases and we can require that the teams adjust them.
Speaker 1: That's unique, right, Most of the other tech companies do
Speaker 1: not have that level of oversight in terms of ensuring
Speaker 1: that their their outcomes are are aligned. There's a lot
Speaker 1: of good talk, but I think you know, the weft
Speaker 1: use case that came out on I think it was
Speaker 1: September really supports that that we're ahead. And then if
Speaker 1: you look at companies just in general that have AI
Speaker 1: ethics board. My experiences that with all the companies and
Speaker 1: I know interact with hundreds of of leaders and companies
Speaker 1: a year, less than five percent of them have a
Speaker 1: board in place, and even fewer of those kind of
Speaker 1: really have a rhythm going and know how they're gonna
Speaker 1: going to operate as a board. Yet m hmm. I
Speaker 1: wanted to talk a little bit about the rule of
Speaker 1: government here, these government leading or following here. UM, I
Speaker 1: would say they're catching up. I think we're following is
Speaker 1: probably the most improved, right because look, I think over
Speaker 1: the last uh couple of years, as we talked about,
Speaker 1: or maybe it's been almost ten years at this point
Speaker 1: in time, as these issues have come to light, companies
Speaker 1: have largely been left to themselves to impose guardrails upon
Speaker 1: their practices and their use of AI. Let's not be
Speaker 1: to say that there aren't laws that regulate for example,
Speaker 1: discrimination laws do would apply to technology that's discriminatory, but
Speaker 1: the unique aspects, to the extent there are unique aspects
Speaker 1: or issues that get amplified through the application of AI systems. Um,
Speaker 1: the government is really just catching up. So we've got
Speaker 1: the the EU proposed a comprehensive regulatory framework for AI
Speaker 1: in the spring time frame. UM. We see in the
Speaker 1: US the FTC is starting to focus on and algorithmic
Speaker 1: bias and just in general on algorithms and that they
Speaker 1: be fair and the like. So there are numerous other
Speaker 1: initiatives following the EU that are looking at frameworks for
Speaker 1: governing AI and regulating AI, and we've been involved, I
Speaker 1: mentioned earlier on our Precision Regulation recommendation. So we have
Speaker 1: something called the IBM Policy Lab, and what differentiates our
Speaker 1: advocacy through the Policy Lab is that we try to
Speaker 1: make concrete, actionable policy recommendations, so not just again articulating principles,
Speaker 1: but really concrete recommendations for companies and for governments and
Speaker 1: policymakers around the globe to implemented to follow um things
Speaker 1: like you know, out of our precision regulation of AI.
Speaker 1: That's where our recommendation is that regulation should be risk based,
Speaker 1: it should be context specific. It should look at and
Speaker 1: allocate responsibility on the party that's closest to the risk,
Speaker 1: and that may be different at different times in the
Speaker 1: life cycle of an AI system. So we deploy some
Speaker 1: general purpose technologies and then our clients train those over time,
Speaker 1: so you know, bearing the risk it should sit with
Speaker 1: the party that's closest to the risk at the different
Speaker 1: points in time in the AI life cycle. You know,
Speaker 1: one of the interesting things about this issue today, we're
Speaker 1: now in a situation where someone like IBM, I'm guessing
Speaker 1: is that it would be as sensitive to public reaction
Speaker 1: to the uses of AI as they would be to
Speaker 1: government reaction to the uses of AI. And I wanted
Speaker 1: to just way those you know, is that this is
Speaker 1: a this kind of fascinating development in our age that
Speaker 1: all of a sudden it almost seems like whatever form
Speaker 1: public reaction takes can be a more powerful lever of
Speaker 1: of in moving changing corporate behavior. Then what government are saying?
Speaker 1: And do you do you think this is true in
Speaker 1: this AI space? I think the government regulation that we're
Speaker 1: seeing is responding to public sentiment. So I agree with
Speaker 1: you a hundred percent that that this is being moved
Speaker 1: by the public. And you know, oftentimes when we have
Speaker 1: conversations at the ethics board, Okay Christina and the lawyers
Speaker 1: say okay, this is not a legal issue, then the
Speaker 1: next conversation is what happens if this story shows up
Speaker 1: on the front page of the New York Times of
Speaker 1: the Wall Street Journal. So so absolutely we consider that.
Speaker 1: So I would also I would add to that, like
Speaker 1: we've been well, probably think the oldest technology company, we're
Speaker 1: over a hundred years old, and our clients have looked
Speaker 1: to us for that hundred plus years to responsibly usher
Speaker 1: in new technologies right and to manage their data, their
Speaker 1: most sensitive data, in a trusted way. So for us,
Speaker 1: it's it's not just about the the headline risk. It's
Speaker 1: about ensuring that we have of business going forward because
Speaker 1: our clients trust us UM and society trusts US. So
Speaker 1: the guardrails we put in place, particularly around the trust
Speaker 1: and Transparency principles, or the guard rails we put in
Speaker 1: place around responsible data use. In the COVID pandemic, there
Speaker 1: was nothing that from a legal perspective, said we couldn't
Speaker 1: do more. There was nothing that said in the US
Speaker 1: we can't use facial recognition technology and our sites. But
Speaker 1: we made principal decisions, and we made those decisions because
Speaker 1: we think they're the right decisions to make. And when
Speaker 1: I look back at the Ethics Board and the analysis
Speaker 1: and the use cases that have come forward over the
Speaker 1: course of the last two years, I can think of
Speaker 1: very few where we said we're not going to do
Speaker 1: this because we're afraid of regulatory repercussions. UM. In fact,
Speaker 1: I can't think of any where because it wouldn't have
Speaker 1: come to the board if it was a league all.
Speaker 1: But yet we did refine in some cases stop I
Speaker 1: actual transactions right and solutions because we felt they were
Speaker 1: not the right thing to do. Yeah, yeah, A question
Speaker 1: for either of you, can you can you dig a
Speaker 1: little more into this, into the real world applications of this.
Speaker 1: What are some of the very kind of concrete kinds
Speaker 1: of things that come out of this focus on untrust.
Speaker 1: So so you know, some some real world examples of
Speaker 1: how trust plays into what we're doing. Is gets back
Speaker 1: to a couple of things Christina said earlier around how
Speaker 1: we're open sourcing a lot of what we do. So
Speaker 1: our research division builds a lot of the technology that
Speaker 1: winds up in our products um uh. And then, particularly
Speaker 1: related to this topic of AI ethics and trustworthy AI
Speaker 1: are the fall is to open source the base of
Speaker 1: the technology. So we have a whole bunch of open
Speaker 1: source tool kits um that anyone can use. In fact,
Speaker 1: some of our competitors use them as much as we
Speaker 1: do in their products. And then we build value adds
Speaker 1: on top of those and so that is something that
Speaker 1: we advocate strongly for in the Ethics Board helps support
Speaker 1: us with that, as do you know, our our product teams,
Speaker 1: because the value is you know, AI is one of
Speaker 1: those spaces where when something goes wrong, it affects everyone, right,
Speaker 1: so if if there's a big issue with AI, everyone's
Speaker 1: going to be concerned about all AI, and so we
Speaker 1: want to make sure that the technology behind AI is
Speaker 1: as fair as possible, is as explainable as possible, is
Speaker 1: as robust as possible, and is as privacy preserving as possible.
Speaker 1: So tool kits that address those are all publicly available,
Speaker 1: and then we build value added capabilities on top of
Speaker 1: that when we set when we bring those things to
Speaker 1: our customers in the form of an integrated platform that
Speaker 1: helps manage the whole life cycle of an AI. Because
Speaker 1: AI is different than software in that the technology under
Speaker 1: AI is machine learning. What that means is that the
Speaker 1: machine keeps learning over time and adjusting the model over time.
Speaker 1: Once you write a piece of software, it's done, it
Speaker 1: doesn't change. And so you need to figure out how
Speaker 1: do you continuously monitor your your AI over time for
Speaker 1: those things I just described and integrate them into your
Speaker 1: security and privacy by design practices so that they're continuously
Speaker 1: updating and aligned to your company's principles as well as
Speaker 1: societal principles as well as any relevant regulations. Yeah, when
Speaker 1: this question, give me one suggestion prediction about what AI
Speaker 1: looks like five years or ten years for now. Yeah, So,
Speaker 1: so that is a really really good question. And you know,
Speaker 1: when we when we look at what AI does today, AI,
Speaker 1: while it's very insightful, and it helps us realize things
Speaker 1: that as humans we may not have picked up on
Speaker 1: our own. And so to augment our intelligence, A surfaces
Speaker 1: insights and maybe reduce as a complexity from almost infinite
Speaker 1: and comprehensible to humans. Two, I have five choices now
Speaker 1: that I can make based on the output of an AI.
Speaker 1: There's there's AI is unable for the most part today
Speaker 1: to provide context or reasoning. Right, So AI provides an answer,
Speaker 1: but there's no reasoning as we think about it as
Speaker 1: humans associated with it. There's a new technology that's coming up,
Speaker 1: that's all. There's a bunch of them that are lumped
Speaker 1: under something called neurosymbolic reasoning. And what neurosymbolic reasoning means,
Speaker 1: it's using mathematical equations. So AI algorithms to reason similarly
Speaker 1: to a human does. So, for instance, you know, the
Speaker 1: Internet contains all sorts of things good and bad, and
Speaker 1: and let's let's look at something that's relevant to to
Speaker 1: me at least being of Jewish background. Right, you want
Speaker 1: you want algorithms to know about the Nazi regime, But
Speaker 1: you don't want algorithms spewing rhetoric about the Nazi regime. Today,
Speaker 1: when we build an AI, it's almost impossible for us
Speaker 1: to get the algorithm to differentiate those two things. With
Speaker 1: a tool like reasoning around it, you could exclude prevent
Speaker 1: an algorithm from saying from learning rhetoric that is, you know,
Speaker 1: not conducive to norms. It's just you know, an example.
Speaker 1: So those are the kinds of things you'll see over
Speaker 1: the next three to five years. I think we'll see
Speaker 1: a lot more explainableity and transparency around AI. So for example,
Speaker 1: whether it may be you're seeing this ad because you
Speaker 1: you know, went on and searched for X, Y and Z,
Speaker 1: You're seeing a shoe ad because you visited this site,
Speaker 1: you know to extent it's it's that, or there'll be
Speaker 1: more transparency you're dealing with a chatbot, you know, just
Speaker 1: when AI is being applied to you. I think you'll
Speaker 1: see a lot more transparency and disclosure around that. And
Speaker 1: then the sort of uh answer, less practical, more aspirational
Speaker 1: answer I think is you know, we know AI is
Speaker 1: changing jobs, it's eliminating some, it's creating new jobs, and
Speaker 1: I think hopefully right with principles around AI. That it
Speaker 1: be used to augment to help humans, that it be
Speaker 1: human centered, that it put people first at the heart
Speaker 1: of the technology. UH, that it will make people better
Speaker 1: and smarter at what they do and they'll be more
Speaker 1: interesting work. Right. So I'm hoping that that will ultimately
Speaker 1: be something that will come out of AI as there's
Speaker 1: more awareness around where it's being used in your life
Speaker 1: already day to day, more transparency around that, more explainability
Speaker 1: around that, and then ultimately more trust. Um, we'll wonderful.
Speaker 1: I think that covers our basis. This has been really
Speaker 1: really fascinating. Thank you for joining me for this, and
Speaker 1: I expect that we will be having both as a
Speaker 1: company inside IBM and as a society many many, many
Speaker 1: many more conversations about AI in the coming years. So
Speaker 1: I'm glad to be on the early end of that
Speaker 1: process because we're not done with this one, are we not?
Speaker 1: By a long shot? The beginning, guess, just the beginning.
Speaker 1: Thank you again, Yeah, thanks for having Thank you. Thank
Speaker 1: you again to Christina Montgomery and Sethburn for the discussion
Speaker 1: about trust and transparency around AI and for their insights
Speaker 1: about what may be possible in the future. It will
Speaker 1: be fascinating to see how IBM can help foster positive
Speaker 1: change in the industry. Smart Talks with IBM is produced
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