Speaker 1: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
Speaker 1: we're diving back into the world of artificial intelligence, but
Speaker 1: with a focus on the powerful concept of open its possibilities, implications,
Speaker 1: and misconceptions. We'll look at openness from a variety of
Speaker 1: angles and explore how the concept is already reshaping industries,
Speaker 1: ways of doing business and our very notion of what's possible.
Speaker 1: In today's episode, Jacob Goldstein sits down with Rebecca Finley,
Speaker 1: the CEO of the Partnership on Ai, a nonprofit group
Speaker 1: grappling with important questions around the future of AI. Their
Speaker 1: conversation focuses on Rebecca's work bringing together a community of
Speaker 1: diverse stakeholders to help shape the conversation around accountable AI governance.
Speaker 1: Rebecca explains why transparency is so crucial for scaling the
Speaker 1: technology responsibly, and she highlights how working with groups like
Speaker 1: the AI Alliance can provide valuable insights in order to
Speaker 1: build the resources, infrastructure, and community around releasing open source models. So,
Speaker 1: without further ado, let's get to that conversation.
Speaker 2: Can you just say your name? And your job.
Speaker 3: My name is Rebecca Finley. I am the CEO of
Speaker 3: the Partnership on AI to Benefit People and Society, often
Speaker 3: referred to as PAI.
Speaker 2: How did you get here? What was your job before
Speaker 2: you have the job that you have now?
Speaker 3: I came to PAI about three years ago, having had
Speaker 3: the opportunity to work for the Canadian Institute for Advance Research,
Speaker 3: developing and deploying all of their programs related to the
Speaker 3: intersection of technology and society. And one of the areas
Speaker 3: that the Canadian Institute had been funding since nineteen eighty
Speaker 3: two was research into artificial intelligence.
Speaker 2: Wow early, they were early.
Speaker 3: It was a very early commitment and an ongoing commitment
Speaker 3: at the Institute to fund long term fundamental questions of
Speaker 3: scientific importance in interdisciplinary research programs that were often committed
Speaker 3: and funded to for well over a decade. The AI
Speaker 3: Robotics and Society program that kicked off the work at
Speaker 3: the Institute eventually became a program very much focused on
Speaker 3: deep learning and reinforcement learning, neural networks. All of the
Speaker 3: current iteration of AI, or certainly the pregenerative of AI
Speaker 3: iteration of AI that led to this transformation that we've
Speaker 3: seen in terms of online search and all sorts of
Speaker 3: ways in which predictive AI has been deployed. So I
Speaker 3: had the opportunity to see the very early days of
Speaker 3: that research coming together, and when in the early sort
Speaker 3: of two thousand, twenty and tens, when compute capability came
Speaker 3: together with data capability through some of the Internet companies
Speaker 3: and otherwise, and we really saw this technology start to
Speaker 3: take off. I had the opportunity to start up a
Speaker 3: program specifically focused on the impacts of AI in society.
Speaker 3: There was, as you know, at that time, some concerns
Speaker 3: both about the potential for the technology, but also in
Speaker 3: terms of what we were seeing around data sets and
Speaker 3: bias and discrimination and potential impact on future jobs. And
Speaker 3: so bringing a whole group of experts, whether they were
Speaker 3: ethicists or lawyers or economists sociologists into the discussion about
Speaker 3: AI was core to that new program and continues to
Speaker 3: be core to my commitment to bringing diverse perspectives together
Speaker 3: to solve the challenges and opportunities that AI offers today.
Speaker 2: So specifically, what is your job now? What is the
Speaker 2: work you do? What is the work that PAI does?
Speaker 3: I like to answer that question by asking two questions,
Speaker 3: First and foremost, do you believe that the world is
Speaker 3: more divided today than it ever has been in recent history?
Speaker 3: And do you believe that if we don't create spaces
Speaker 3: for very different perspectives to come together, we won't be
Speaker 3: able to solve the challenges that are in front of
Speaker 3: the world today. My answer to both of those questions is, yes,
Speaker 3: we're more divided, and two, we need to seek out
Speaker 3: those spaces where those very different perspectives can come together
Speaker 3: to solve those great challenges. And that's what I get
Speaker 3: to do as CEO of the Partnership on AI. We
Speaker 3: were begun in twenty sixteen with a fundamental commitment to
Speaker 3: bringing together experts, whether they were in industry, academia, civil society,
Speaker 3: or philanthropy, coming together to identify what are the most
Speaker 3: important questions when we think about developing AI centered on
Speaker 3: people and communities, and then how do we begin to
Speaker 3: develop the solutions to make sure we benefit appropriately.
Speaker 2: So that's a very big picture set of ideas. I'm
Speaker 2: curious on a sort of more day to day level.
Speaker 2: I mean, you talk about collaborating with all these different
Speaker 2: kinds of people, all these different groups, what does that
Speaker 2: actually look like, what are some specific examples of how
Speaker 2: you do this work?
Speaker 3: So right now we have about one hundred and twenty
Speaker 3: partners in sixteen countries. They come together through working groups
Speaker 3: that we look at through a variety of different perspectives.
Speaker 3: It could be AI, labor and the economy. It could
Speaker 3: be how do you build a healthy information ecosystem. It
Speaker 3: could be how do you bring more diverse perspectives into
Speaker 3: the inclusive and equitable development of AI. It could be
Speaker 3: what are the emerging opportunities with these very very large
Speaker 3: foundation model applications and how do you deploy those safely?
Speaker 3: And these groups come together most importantly to say what
Speaker 3: are the questions we need to answer collectively, So they
Speaker 3: come together in working groups. I have an amazing staff
Speaker 3: team who hold the pen on synthesizing research and data
Speaker 3: and evidence, developing frameworks, best practices, resources, all sorts of
Speaker 3: things that we can offer up to the community, be
Speaker 3: they in industry or in policy, to say this is
Speaker 3: how we can well, this is what good looks like,
Speaker 3: and this is how we can do it on a
Speaker 3: day to day basis. So that's what we do, and
Speaker 3: then we publish our materials. It's all open. We make
Speaker 3: sure that we get them into the hands of those
Speaker 3: communities that can use them, and then we drive and
Speaker 3: work with those communities to put them into practice.
Speaker 2: You used the word to open there and describing your publications.
Speaker 2: I know, in the world of AI, on the sort
Speaker 2: of technical side, there's a debate, say, or discussion about
Speaker 2: kind of open versus closed AI, And I'm curious how
Speaker 2: you kind of encounter that particular discussion. What is your
Speaker 2: view on open versus closed AI.
Speaker 3: So the current discussion between open and closed release of
Speaker 3: AI models came once we saw chat, GPT and other
Speaker 3: very large generative AI systems being deployed out into the
Speaker 3: hands of consumers around the world, and there emerged some
Speaker 3: fear about the potential of these models to act in
Speaker 3: all sorts of catastrophic ways. So there were concerns that
Speaker 3: the models could be deployed with regard to different development
Speaker 3: of viruses or biomedical weapons or even nuclear weapons, or
Speaker 3: through manipulation or otherwise. So this are emerged about over
Speaker 3: the last eighteen months, this real concern that these models,
Speaker 3: if deployed openly, could lead to some level of truly
Speaker 3: catastrophic risk. And what emerged is actually that we discovered
Speaker 3: that through a whole bunch of work that's been done
Speaker 3: over the last little while, that releasing them openly has
Speaker 3: not led and doesn't appear to be leading in any
Speaker 3: way to catastrophic risk. In facts, releasing them openly allows
Speaker 3: for much more greater scrutiny and understanding of the safety
Speaker 3: measures that have been put into place, And so what
Speaker 3: happened was sort of the pendulum swamp very much towards
Speaker 3: concerned about really catastrophic risk and safety over the last year,
Speaker 3: and over the last year we've seen it swing back
Speaker 3: as we learn more and more about how these models
Speaker 3: are being used and how they are being deployed into
Speaker 3: the world. My feeling is we must approach this work openly,
Speaker 3: and it's not just open release of models or what
Speaker 3: we think of as traditional open source forms of model
Speaker 3: development or otherwise, but we really need to think about
Speaker 3: how do we build an open innovation ecosystem that fundamentally
Speaker 3: allows both for the innovation to be shared with many people,
Speaker 3: but also for safety and security to be rigorously upheld.
Speaker 2: So when you talk about this kind of broader idea
Speaker 2: of open innovation beyond open source or you know, transparency
Speaker 2: in models like what do you mean sort of specifically,
Speaker 2: how does that look in the world.
Speaker 3: So I have three particular points view when it comes
Speaker 3: to open innovation, because I think we need to think
Speaker 3: both upstream around the research that is driving these models,
Speaker 3: and downstream in terms of the benefits of these models
Speaker 3: to others. So first and foremost, what we have known
Speaker 3: in terms of how AI has been developed, and yes,
Speaker 3: I had an opportunity to see it when I was
Speaker 3: at the Canadian Institute for Advanced Research is a very
Speaker 3: open form of scientific publication and rigorous peer review. And
Speaker 3: what happens when we release openly is you have an
Speaker 3: opportunity for the research to be interrogated to determine the
Speaker 3: quality and significance of that, but then also for it
Speaker 3: to be picked up by many others. And then secondly,
Speaker 3: openness for me is about transparency. We released a set
Speaker 3: of very strong recommendations last year around the way in
Speaker 3: which these very large foundation models could be deployed safely.
Speaker 3: They're all about disclosure. They're all about disclosure and documentation
Speaker 3: right from the early days pre R and D development
Speaker 3: of these systems, right in terms of thinking about what's
Speaker 3: in the training data and how is it being used
Speaker 3: all the way through to post deployment monitoring and disclosure.
Speaker 3: So I really think that this is important transparency through it.
Speaker 3: And then the third piece is openness in terms of
Speaker 3: who is around the table to benefit from this technology.
Speaker 3: We know that if we're really going to see these
Speaker 3: new models having being successful deployed into education or healthcare
Speaker 3: or climate and sustainability, we need to have those experts
Speaker 3: in those communities at the table charting this and making
Speaker 3: sure that the technology is working for them. So those
Speaker 3: are the three ways I think about openness.
Speaker 2: Is there like a particular project that you've worked on
Speaker 2: that you feel like you know reflects your approach to
Speaker 2: responsible AI.
Speaker 3: So there's a really interesting project that we have underway
Speaker 3: at PAI that is looking at responsible practices squarely when
Speaker 3: it comes to the use of synthetic media. And what
Speaker 3: we heard from our community was that they were looking
Speaker 3: for a clear code of conduct about what does it
Speaker 3: mean to be responsible in this space. And so what
Speaker 3: happened is we pulled together a number of working groups
Speaker 3: to come together. They included industry representatives. They also included
Speaker 3: civil society organizations like WITNESS, a number of academic institutions
Speaker 3: and otherwise, And what we heard was that there were
Speaker 3: clear requirements that creators could take, that developers of the
Speaker 3: technology could take, and then also distributors. So when we
Speaker 3: think about those generative AI systems being deployed across platforms
Speaker 3: and otherwise, and we came up with a framework for
Speaker 3: what responsibility looks like. What does it mean to have consent,
Speaker 3: what does it mean to disclose responsibly, what does it
Speaker 3: mean to embed technology into it? So, for example, we've
Speaker 3: heard many people talk about the importance of water marking
Speaker 3: systems right and making sure that we have a way
Speaker 3: to water mark them. But what we know from the
Speaker 3: technology is that is a very very complex and complicated problem,
Speaker 3: and what might work on a technical level certainly hits
Speaker 3: a whole new set of complications when we start labeling
Speaker 3: and disclosing out to the public about what that technology
Speaker 3: actually means. All of these, I believe are solvable problems,
Speaker 3: but they all needed to have a clear code underneath
Speaker 3: them that was saying this is what we will commit to.
Speaker 3: And we now have a number of organizations, many many
Speaker 3: of the large technology companies, but also many of the
Speaker 3: small startups who are operating in this based civil society
Speaker 3: and media organizations like the BBC and the CBC who's
Speaker 3: have signed on. And one of the really exciting pieces
Speaker 3: of that is that we're now seeing how it's changing practice.
Speaker 3: So a year in we asked each of our partners
Speaker 3: to come up with a clear case study about how
Speaker 3: that work has changed the way they are making decisions,
Speaker 3: deploying technology and ensuring that they're being responsible in their use.
Speaker 3: And that is creating now a whole resource online that
Speaker 3: we're able to share with others about what does it
Speaker 3: mean to be responsible in this place. There's so much
Speaker 3: more work to be done, and the exciting thing is
Speaker 3: once you have a foundation like this in place, we
Speaker 3: can continue to build on it. So much interest now
Speaker 3: in the policy space, for example, about this work as well.
Speaker 2: Are there any specific examples of those sort of case
Speaker 2: studies or the real world experiences that say media organizations
Speaker 2: had that are interesting that are illuminating. Yes.
Speaker 3: So, for example, what we saw with the BBC is
Speaker 3: that they're developing a lot of content as a public broadcaster,
Speaker 3: both in terms of their news coverage but also in
Speaker 3: terms of some of the resources that they are developing
Speaker 3: for the British public as well. And what they talked
Speaker 3: about was the way in which they had used synthetic
Speaker 3: meat in a very very sensitive environment where they were
Speaker 3: hearing from individuals talk about personal experiences, but wanted to
Speaker 3: have some way to change the face entirely in terms
Speaker 3: of the individuals who were speaking. So that's a very
Speaker 3: complicated ethical question, right, how do you do that responsibily
Speaker 3: and what is the way in which you use that technology,
Speaker 3: and most importantly, how do you disclose it? So their
Speaker 3: case study looked at that in some real detail about
Speaker 3: the process they went through to make the decision responsibly
Speaker 3: to do what they chose, how they intended to use
Speaker 3: the technology in that space.
Speaker 2: As you describe your work in some of these studies,
Speaker 2: the idea of transparency seems to be a theme. Talk
Speaker 2: about the importance of transparency in this kind of work.
Speaker 3: Yeah, transparency is fundamental to responsibility. I always like to
Speaker 3: say it's not accountability in a complete sense, but it
Speaker 3: is a first step to driving accountability more fully, so,
Speaker 3: when we think about how these systems are developed, they're
Speaker 3: often developed behind closed doors inside companies who are making
Speaker 3: decisions about what and how these products will work from
Speaker 3: a business perspective, and what disclosure and transparency can provide
Speaker 3: is some sense of the decisions that were made leading
Speaker 3: up to the way in which those models were deployed.
Speaker 3: So this could be ensuring that individual's private information was
Speaker 3: protected through the process and won't be inadvertently disclosed, or otherwise,
Speaker 3: it could be providing some sense of how well the
Speaker 3: system performs against a whole level of quality measures. So
Speaker 3: we have all of these different types of evaluations and
Speaker 3: a measures that are emerging about the quality of these
Speaker 3: systems as they're deployed. Being transparent about how they perform
Speaker 3: against these systems is really crucial to that as well.
Speaker 3: We have a whole ecosis that's starting to emerge around
Speaker 3: auditing of these systems. So what does that look like
Speaker 3: we think about auditors and all sorts of other sectors
Speaker 3: of the economy. What does it look like to be
Speaker 3: auditing these systems to ensure that they're meeting all of
Speaker 3: those both legal but additional ethical requirements that we want
Speaker 3: to make sure that are in place.
Speaker 2: What are some of the hardest ethical dilemmas you've come
Speaker 2: up against in AI policy.
Speaker 3: Well, the interesting thing about AI policy right is what
Speaker 3: it works very simply in one setting can be highly
Speaker 3: complicated in another setting. And so, for example, I have
Speaker 3: an app that I adore. It's an app on my
Speaker 3: phone that allows me to take a photo of a
Speaker 3: bird and it will help me to better understand what
Speaker 3: that bird is and give me all sorts of information
Speaker 3: about that bird. Now, it's probably right most of the time,
Speaker 3: and it's certainly right enough of the time to give
Speaker 3: me great pleasure and delight when I'm out walking. You
Speaker 3: could think about that exact same technology applied. So for example,
Speaker 3: now you're a security guard and you're working in a
Speaker 3: shopping plaza, and you're able to take photos of individuals
Speaker 3: who you may think are acting suspiciously in some way
Speaker 3: and match that photo up with some sort of a
Speaker 3: database of individuals that may have been found, you know,
Speaker 3: to have some sort of connection to other criminal behavior
Speaker 3: in the past. Right, So what goes from being a
Speaker 3: delightful Oh, isn't this an interesting bird? To a very
Speaker 3: very creepy What does this say about surveillance and privacy
Speaker 3: and access to public spaces? And that is the nature
Speaker 3: of AI. So much of the concern about the ethical
Speaker 3: use and deployment of AI is how an organization is
Speaker 3: making the choices within the social and systemic structure they sit.
Speaker 3: So so much about the ethics of AI is understanding
Speaker 3: what is the use case, how is it being used,
Speaker 3: how is it being constrained? How does it start to
Speaker 3: infringe upon what we think of as the human rights
Speaker 3: of an individual to privacy? And so you have to
Speaker 3: constantly be thinking about ethics. What could work very well
Speaker 3: in one situation absolutely doesn't work in another. We often
Speaker 3: talk about these as socio technical questions. Right, just because
Speaker 3: the technology works doesn't actually mean that it should be
Speaker 3: used and deployed.
Speaker 2: What's an example of where the partnership on AI influence
Speaker 2: changes either in policy or in industry practice.
Speaker 3: We talked a little bit about the Framework for Synthetic
Speaker 3: Media and how that has allowed companies and media organizations
Speaker 3: and civil society organizations to really think deeply about the
Speaker 3: way in which they're using this. Another area that we
Speaker 3: focused on has been around responsible deployment of foundation on
Speaker 3: large scale models. I said, we issued a set of
Speaker 3: recommendations last year that really laid out for these very
Speaker 3: large developers and deployers of foundation and frontier models were
Speaker 3: what does good look like? Right from R and D
Speaker 3: through to deployment monitoring, and it has been very encouraging
Speaker 3: to see that that work has been picked up by
Speaker 3: companies and really articulated as part of the fabric of
Speaker 3: the deployment of their foundation models and systems moving forward.
Speaker 3: So much of this work is around creating clear definitions
Speaker 3: of what we're meaning as the technology evolves and clear
Speaker 3: sets of responsibilities. So it's great to see that work
Speaker 3: getting picked up. The NTIA in the United States just
Speaker 3: released a report on open models and the release of
Speaker 3: open models. Great to see our work cited there as
Speaker 3: contributing to that analysis. Great to see some of our
Speaker 3: definitions and synthetic media getting picked up by legislators in
Speaker 3: different countries. It's important, i think, for us to build capacity,
Speaker 3: knowledge and understanding and our policy makers in this moment
Speaker 3: as the technology is evolving and accelerating in its development.
Speaker 2: What's the AI Alliance and why did Partnership on AI
Speaker 2: decide to join?
Speaker 3: So you had asked about the debate between open versus
Speaker 3: closed models and how that has evolved over the last year,
Speaker 3: and the AI Alliance was a community of organizations that
Speaker 3: came together to really think about, okay, if we support
Speaker 3: open release of models what does that look like and
Speaker 3: what does the community need? And so that's about one
Speaker 3: hundred organizations. IBM, one of our founding partners, is also
Speaker 3: one of the founding partners of the AI Alliance. It's
Speaker 3: a community that brings together a number of academic institutions
Speaker 3: many countries around the world, and they're really focused on
Speaker 3: how do you build the resource is an infrastructure and
Speaker 3: community around what open source in these large scale models
Speaker 3: really mean. So that could be open data sets, that
Speaker 3: could be open technology development. Really building on that understanding
Speaker 3: that we need an infrastructure in place and a community
Speaker 3: engaged in thinking about safety and innovation through the open lens.
Speaker 1: This approach brings together organizations and experts from around the
Speaker 1: globe with different backgrounds, experiences, and perspectives to transparently and
Speaker 1: openly address the challenges and opportunities today. I poses the
Speaker 1: collaborative nature of the AI Alliance encourages discussion, debate, and innovation.
Speaker 1: Through these efforts, IBM is helping to build a community
Speaker 1: around transparent open technology.
Speaker 2: So I want to talk about the future for a minute.
Speaker 2: I'm true, is what you see as the biggest obstacles
Speaker 2: to why spread adoption of responsible AI practices.
Speaker 3: One of the biggest obstacles today is an inability and
Speaker 3: really a lack of understanding about how to use these
Speaker 3: models and how they can most effectively drive forward a
Speaker 3: company's commitment to whatever products and services it might be deploying.
Speaker 3: So I always recommend a couple of things for companies
Speaker 3: really to think about this and to get started. One
Speaker 3: is think about how you are already using AI across
Speaker 3: all of your business products and services, because already AI
Speaker 3: is integrated into our workforces and into our workstreams, and
Speaker 3: into the way in which companies are communicating with their
Speaker 3: clients every day. So understand how you are already using
Speaker 3: it and understand how you are integrating oversight and monitoring
Speaker 3: into those One of the best and clearest ways in
Speaker 3: which a company can really understand how to use this
Speaker 3: response is through documentation. It's one of the areas where
Speaker 3: there's a clear consensus in the community. So how do
Speaker 3: you document the models that you are using, making sure
Speaker 3: that you've got a registry in place. How do you
Speaker 3: document the data that you are using and where that
Speaker 3: data comes from. This is sort of the first system,
Speaker 3: first line of defense in terms of understanding both what
Speaker 3: is in place and what you need to do in
Speaker 3: order to monitor it moving forward. And then secondly, once
Speaker 3: you've got an understanding of how you're already using the system,
Speaker 3: look at ways in which you could begin to pilot
Speaker 3: or iterate in a low risk way using these systems
Speaker 3: to really begin to see how and what structures you
Speaker 3: need to have in place to use it moving forward.
Speaker 3: And then thirdly, make sure that you structure a team
Speaker 3: in place internally that's able to do some of this
Speaker 3: cross departmental monitoring, knowledge sharing and learning boards are very
Speaker 3: very interested in this technology, So thinking about how you
Speaker 3: can have a system or a team in place internally
Speaker 3: that's reporting to your board, giving them a sense of
Speaker 3: both the opportunities that it identifies for you and the
Speaker 3: additional risk mitigation and management you might be putting into place.
Speaker 3: And then you know, once you have those things into place,
Speaker 3: you're really going to need to understand how you work
Speaker 3: with the most valuable asset you have, which is your people.
Speaker 3: How do you make sure that AI systems are working
Speaker 3: for the workers, making sure that they're going into place.
Speaker 3: The most important and impressive implementations we see are those
Speaker 3: where you have the workers who are going to be
Speaker 3: engaged in this process central to figuring out how to
Speaker 3: develop and deploy it in order to really enhance their work.
Speaker 3: It's a core part of a set of Shared Prosperity
Speaker 3: guidelines that we issued last year.
Speaker 2: And then, from the side of policy makers, how should
Speaker 2: policy makers think about the balance between innovation and regulation.
Speaker 3: Yeah, it's so interesting, isn't it that we always think of,
Speaker 3: you know, innovation and regulation as being two sides of
Speaker 3: a coin, when in fact, so much innovation comes from
Speaker 3: having a clear set of guardrails and regulation in place.
Speaker 3: We think about all of the innovation that's happened in
Speaker 3: the automotive industry, right we can drive faster because we
Speaker 3: have breaks, we can drive faster because we have seat
Speaker 3: belts in place. So I think it's often interesting to
Speaker 3: me that we think about the two as being on
Speaker 3: either side of the coin, but an actual fact, you
Speaker 3: can't be innovative without being responsible as well. And so
Speaker 3: I think from a policy maker perspective, what we have
Speaker 3: been really encouraging them to do is to understand that
Speaker 3: you've got foundational regulation in place that works for you. Nationally,
Speaker 3: this could be ensuring that you have strong privacy protections
Speaker 3: in place. It could be ensuring that you are understanding
Speaker 3: pential online harms, particularly to vulnerable communities, and then look
Speaker 3: at what you need to be doing internationally to being
Speaker 3: both competitive and sustainable. There's all sorts of mechanisms that
Speaker 3: are in place right now at the international level to
Speaker 3: think about how do we build an interoperable space for
Speaker 3: these technologies moving forward.
Speaker 2: We've been talking in various ways about what it means
Speaker 2: to responsibly develop AI, and if you're going to boil
Speaker 2: that down, you know the essential concerns that people should
Speaker 2: be thinking about, like what are the key things to
Speaker 2: think about in responsible AI?
Speaker 3: So if you are a company, if we're talking specifically
Speaker 3: through the company lens, when we're thinking about responsible use
Speaker 3: of AI, the most important difference between this form of
Speaker 3: AI technologies and other forms of technologies that we have
Speaker 3: used previously is the integration of data and the training
Speaker 3: models that go on top of that data. So when
Speaker 3: we think about responsibility, first and foremost, you need to
Speaker 3: think about your data. Where did it come from, What
Speaker 3: consent and disclosure requirements do you have on it? Are
Speaker 3: you privacy protecting? You can't be thinking about AI within
Speaker 3: your company without thinking about data, and that's both your
Speaker 3: training data. But then once you're using your systems and
Speaker 3: integrating and interacting with your consumers, how are you protecting
Speaker 3: the data that's coming out of those systems as well?
Speaker 3: And then secondly is when you're thinking about how to
Speaker 3: deploy that AI system, the most important thing you want
Speaker 3: to think about is are we being transparent about how
Speaker 3: it's being used with our clients and our partners. So
Speaker 3: you know the idea that if I'm a customer, I
Speaker 3: should know when I'm interacting with an AI system, I
Speaker 3: should know when I'm interacting with a human. So I
Speaker 3: think those two pieces are the fundamentals. And then of
Speaker 3: course you want to be thinking carefully about making sure
Speaker 3: that whatever jurisdiction you're operating in, you're meeting all of
Speaker 3: the legal requirements with regard to the services and products
Speaker 3: that you're offering.
Speaker 2: Let's finish with the speed round, complete the sentence. In
Speaker 2: five years, AI will will.
Speaker 3: Drive equity, justice, and shared prosperity if we choose to
Speaker 3: set that future trajectory for this technology.
Speaker 2: What is the number one thing that people misunderstand about AI.
Speaker 3: AI is not good, and AI is not bad, but
Speaker 3: AI is also not neutral. It is a product of
Speaker 3: the choices we make as humans about how we deploy
Speaker 3: it in the world.
Speaker 2: What advice would you give yourself ten years ago to
Speaker 2: better prepare yourself for today?
Speaker 3: Ten years ago, I wish that I had known just
Speaker 3: how fundamental the enduring questions of ethics and responsibility would
Speaker 3: be as we developed this technology moving forward, So many
Speaker 3: of the questions that we ask about AI are questions
Speaker 3: about ourselves and the way in which we use technology,
Speaker 3: and the way in which technology can advance the work
Speaker 3: we're doing.
Speaker 2: How do you use AI in your day to day
Speaker 2: life today?
Speaker 3: I use AI all day every day. So whether it's
Speaker 3: my bird app when I go out for my morning walk,
Speaker 3: helping me to better identify birds that I see, or
Speaker 3: whether it is my mapping app that's helping me to
Speaker 3: get more speedily through traffic to whatever meeting I need
Speaker 3: to go to, I use AI all the time. I
Speaker 3: really enjoy using some of the generative AI chatbots more
Speaker 3: for fun than for anything else. As a creative partner
Speaker 3: in thinking through ideas and integrating it into all aspects
Speaker 3: of our lives. Is just so much about the way
Speaker 3: in which we live today.
Speaker 2: So people use the word open to mean different things,
Speaker 2: even just in the context of technology. How do you
Speaker 2: define open in the context of your work.
Speaker 3: So there is the question of open as it is
Speaker 3: deployed to technology, which we've talked a lot about. But
Speaker 3: I do think a big piece of PAI is open minded.
Speaker 3: We need to be open minded truly to listen to,
Speaker 3: for example, what a civil society advocate might say about
Speaker 3: what they're seeing in terms of the way in which
Speaker 3: AI is interacting in a particular community. Or we need
Speaker 3: to be open minded to hear from a technologist about
Speaker 3: their hopes and dreams of where this technology you might
Speaker 3: go moving forward. And we need to have those conversations
Speaker 3: listening to each other to really identify how we're going
Speaker 3: to meet the challenge and opportunity of AI today. So
Speaker 3: open is just fundamental to the partnership on AI. I
Speaker 3: often call it an experiment in open innovation.
Speaker 2: Rebecca, thank you so much for your time.
Speaker 3: It is my pleasure. Thank you for having me.
Speaker 1: Thank you to Rebecca and Jacob for that engaging discussion
Speaker 1: about some of the most pressing issues facing the future
Speaker 1: of AI. As Rebecca emphasized, whether you're thinking about data
Speaker 1: privacy or disclosure, transparency and openness are key to solving
Speaker 1: challenges and capitalizing on new opportunities by developing best practices
Speaker 1: and resources. Partnership on AI is building out the guardrails
Speaker 1: to support the release of open source models the practice
Speaker 1: of post deployment monitoring. By sharing their work with the
Speaker 1: broader community, Rebecca and Pai are demonstrating how working responsibly,
Speaker 1: ethically and openly can help drive innovation. Smart Talks with
Speaker 1: IBM is produced by Matt Ramano, Joey Fishground, Amy Gaines McQuaid,
Speaker 1: and Jacob Goldstein. We're edited by Lydia jen Kott. Our
Speaker 1: engineers are Sarah Brugaer and Ben Tolliday. Theme song by Gramoscope.
Speaker 1: Special thanks to 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.
Speaker 1: To find more Pushkin podcasts, listen on the iHeartRadio app,
Speaker 1: Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Glabo.
Speaker 1: This is a paid advertised span from IBM. The conversations
Speaker 1: on this podcast don't necessarily represent IBM's positions, strategies or opinions,