Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. This season
Speaker 1: on smart Talks with IBM, Malcolm Gladwell and team are
Speaker 1: diving into the transformative world of artificial intelligence with a
Speaker 1: fresh perspective on the concept of open What does open
Speaker 1: really mean in the context of AI. It can mean
Speaker 1: open source code or open data, but it also encompasses
Speaker 1: fostering an ecosystem of ideas, ensuring diverse perspectives are heard,
Speaker 1: and enabling new levels of transparency. Join hosts from your
Speaker 1: favorite Pushkin podcasts as they explore how openness in AI
Speaker 1: is reshaping industries, driving innovation, and redefining what's possible. You'll
Speaker 1: hear from industry experts and leaders about the implications and
Speaker 1: possibilities of open AI, and of course, Malcolm Gladwell will
Speaker 1: be there to guide you through the season with his
Speaker 1: unique insights. Look out for new episodes of Smart Talks
Speaker 1: every other week on the iHeartRadio app, Apple Podcasts, or
Speaker 1: wherever you get your podcasts, and learn more at IBM
Speaker 1: dot com, slash smart Talks.
Speaker 2: Pushkin Hello, Hello, Welcome to Smart Talks with IBM, a
Speaker 2: podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell.
Speaker 2: This season, we're diving back into the world of artificial intelligence,
Speaker 2: but with a focus on the powerful concept of open
Speaker 2: its possibilities, implications, and misconceptions. We'll look at openness from
Speaker 2: a variety of angles and explore how the concept is
Speaker 2: already reshaping industries, ways of doing business, and our very
Speaker 2: notion of what's possible. In today's episode, Jacob Goldstein sits
Speaker 2: down with Rebecca Finley, the CEO of the Partnership on AI,
Speaker 2: a nonprofit group grappling with important questions around the future
Speaker 2: of AI. Their conversation focuses on Rebecca's work bringing together
Speaker 2: a community of diverse stakeholders to help shape the conversation
Speaker 2: around accountable AI governance. Rebecca explains why transparency is so
Speaker 2: crucial for scaling the technology responsibly, and she highlights how
Speaker 2: working with groups like the AI Alliance can provide valuable
Speaker 2: insights in order to build the resources, infrastructure, and community
Speaker 2: around releasing open source models. So, without further ado, let's
Speaker 2: get to that conversation.
Speaker 3: Can you say your name and your job?
Speaker 4: My name is Rebecca Finley. I am the CEO of
Speaker 4: the Partnership on AI to benefit people and society. Often
Speaker 4: referred to as PAI.
Speaker 3: How did you get here? What was your job before
Speaker 3: you had the job that you have now.
Speaker 4: I came to PAI about three years ago, having had
Speaker 4: the opportunity to work for the Canadian Institute for Advance Research,
Speaker 4: developing and deploying all of their programs related to the
Speaker 4: intersection of technology and society. And one of the areas
Speaker 4: that the Canadian Institute had been funding since nineteen eighty
Speaker 4: two was research into artificial intelligence.
Speaker 3: Wow, early, they were early.
Speaker 4: It was a very early commitment and an ongoing commitment
Speaker 4: at the Institute to fund long term fundamental questions of
Speaker 4: scientific importance in interdisciplinary research programs that were often committed
Speaker 4: and funded to for well over a decade. The AI,
Speaker 4: Robotics and Society program that kicked off the work at
Speaker 4: the Institute eventually became a program very much focused on
Speaker 4: deep learning and reinforcement learning, neural networks, all of the
Speaker 4: current iteration of AI, or certainly the pregenerative AI iteration
Speaker 4: of AI that led to this transformation that we've seen
Speaker 4: in terms of online search and all sorts of ways
Speaker 4: in which predictive AI has been deployed. So I had
Speaker 4: the opportunity to see the very early days of that
Speaker 4: research coming together, and when in the early sort of
Speaker 4: two thousand, twenty and tens, when compute capability came together
Speaker 4: with data capability through some of the Internet companies and otherwise,
Speaker 4: and we really saw this technology start to take off.
Speaker 4: I had the opportunity to start up a program specifically
Speaker 4: focused on the impacts of AI in society. There was,
Speaker 4: as you know, at that time, some concerns both about
Speaker 4: the potential for the technology, but also in terms of
Speaker 4: what we were seeing around data sets and bias and
Speaker 4: discrimination and potential impact on future jobs. And so bringing
Speaker 4: a whole group of experts, whether they were ethicists or
Speaker 4: lawyers or economists, sociologists into the discussion about AI was
Speaker 4: core to that new program and continues to be core
Speaker 4: to my commitment to bringing diverse perspectives together to solve
Speaker 4: the challenges and opportunities that AI offers today.
Speaker 3: So specifically, what is your job now? What is the
Speaker 3: work you do? What is the work that PAI does?
Speaker 4: I like to answer that question by asking two questions.
Speaker 4: First and foremost, do you believe that the world is
Speaker 4: more divided today than it ever has been in recent history?
Speaker 4: And do you believe that if we don't create spaces
Speaker 4: for very different perspectives to come together, we won't be
Speaker 4: able to solve the challenges that are in front of
Speaker 4: the world today. My answer to both of those questions is, yes,
Speaker 4: we're more divided, and two, we need to seek out
Speaker 4: those spaces where those very different perspectives can come together
Speaker 4: to solve those great challenges. And that's what I get
Speaker 4: to do as CEO of the Partnership on AI. We
Speaker 4: were begun in twenty sixteen with a fundamental commitment to
Speaker 4: bringing together experts, whether they were in industry, academia, civil society,
Speaker 4: or philanthropy, coming together to identify what are the most
Speaker 4: important questions when we think about developing AI centered on
Speaker 4: people and communities, and then how do we begin to
Speaker 4: develop the solutions to make sure we benefit appropriately.
Speaker 3: So that's a very big picture set of ideas. I'm
Speaker 3: curious on a sort of more day to day level.
Speaker 3: I mean, you talk about collaborating with all these different
Speaker 3: kinds of people, all these different groups, what does that
Speaker 3: actually look like. What are some specific examples of how
Speaker 3: you do this work?
Speaker 4: So right now we have about one hundred and twenty
Speaker 4: partners in sixteen countries. They come together through working groups
Speaker 4: that we look at through a variety of different perspectives.
Speaker 4: It could be AI, labor and the economy. It could
Speaker 4: be how do you build a healthy information ecosystem. It
Speaker 4: could be how do you bring more diverse perspectives into
Speaker 4: the inclusive and equitable development of AI. It could be
Speaker 4: what are the emerging opportunities with these very very large
Speaker 4: foundation model applications and how do you deploy those safely?
Speaker 4: And these groups come together most importantly to say, what
Speaker 4: are the questions we need to answer collectively, So they
Speaker 4: come together in working groups. I have an amazing staff
Speaker 4: team who hold the pen on synthesizing research and data
Speaker 4: and evidence, developing frameworks, best practices, resources, all sorts of
Speaker 4: things that we can offer up to the community, be
Speaker 4: they in industry or in policy, to say this is
Speaker 4: how we can well, this is what good looks like,
Speaker 4: and this is how we can do it on a
Speaker 4: day to day basis. So that's what we do, and
Speaker 4: then we publish our materials. It's all open. We make
Speaker 4: sure that we get them into the hands of those
Speaker 4: communities that can use them, and then we drive and
Speaker 4: work with those communities to put them into practice.
Speaker 3: You use the word open there and describing your publications.
Speaker 3: I know, in the world of AI, on the sort
Speaker 3: of technical side, there's a lot of debate, say, or
Speaker 3: discussion about kind of open versus closed AI, And I'm
Speaker 3: curious how you kind of encounter that particular discussion. What
Speaker 3: is your view on open versus closed AI.
Speaker 4: So the current discussion between open and closed release of
Speaker 4: AI models came once we saw chat, GPT and other
Speaker 4: very large generative AI systems being deployed out into the
Speaker 4: hands of consumers around the world, and there emerged some
Speaker 4: fear about the potential of these models to act in
Speaker 4: all sorts of catastrophic ways. So there were concerns that
Speaker 4: the models could be deployed with regard to different development
Speaker 4: of viruses or biomedical weapons, or even nuclear weapons, or
Speaker 4: through manipulation or otherwise. So this are emerged about over
Speaker 4: the last eighteen months, this real concern that these models,
Speaker 4: if deployed openly, could lead to some level of truly
Speaker 4: catastrophic risk. And what emerged is actually that we discovered
Speaker 4: that through a whole bunch of work that's been done
Speaker 4: over the last little while, that releasing them openly has
Speaker 4: not led and doesn't appear to be leading in any
Speaker 4: way to catastrophic risk. In facts, releasing them openly allows
Speaker 4: for much more greater scrutiny and understanding of the safety
Speaker 4: measures that have been put into place. And so what
Speaker 4: happened was sort of the pendulum swung very much towards
Speaker 4: concerned about really catastrophic risk and safety over the last year,
Speaker 4: and over the last year we've seen it swing back
Speaker 4: as we learn more and more about how these models
Speaker 4: are being used and how they are being deployed into
Speaker 4: the world. My feeling is we must approach this work openly,
Speaker 4: and it's not just open release of models or what
Speaker 4: we think of as traditional open source forms of model
Speaker 4: development or otherwise, but we really need to think about
Speaker 4: how do we build an open innovation ecosystem that fundamentally
Speaker 4: allows both for the innovation to be shared with many people,
Speaker 4: but also for safety and security to be rigorously upheld.
Speaker 3: So when you talk about this kind of broader idea
Speaker 3: of open innovation beyond open source or you know, transparency
Speaker 3: and models, like, what do you mean sort of specifically
Speaker 3: how does that look in the world.
Speaker 4: So I have three particular points of view when it
Speaker 4: comes to open innovation because I think we need to
Speaker 4: think both both upstream around the research that is driving
Speaker 4: these models and downstream in terms of the benefits of
Speaker 4: these models to others. So, first and foremost, what we
Speaker 4: have known in terms of how AI has been developed,
Speaker 4: and yes, I had an opportunity to see it when
Speaker 4: I was at the Canadian Institute for Advanced Research is
Speaker 4: a very open form of scientific publication and rigorous peer review.
Speaker 4: And what happens when we release openly is you have
Speaker 4: an opportunity for the research to be interrogated to determine
Speaker 4: the quality and significance of that, but then also for
Speaker 4: it to be picked up by many others. And then secondly,
Speaker 4: openness for me is about transparency. We released a set
Speaker 4: of very strong recommendations last year around the way in
Speaker 4: which these very large foundation models could be deployed safely.
Speaker 4: They're all about disclosure. They're all about disclosure and documentation
Speaker 4: right from the early days pre R and D development
Speaker 4: of these systems, right in terms of thinking about what's
Speaker 4: in the training data and how is it being used,
Speaker 4: all the way through to post deployment monitoring and disclosure.
Speaker 4: So I really think that this is important transparency through it.
Speaker 4: And then the third piece is openness in terms of
Speaker 4: who was around the table to benefit from this technology.
Speaker 4: We know that if we're really going to see these
Speaker 4: new models having being successful deployed into education or healthcare
Speaker 4: or climate and sustainability, we need to have those experts
Speaker 4: in those communities at the table charting this and making
Speaker 4: sure that the technology is working for them. Those are
Speaker 4: the free ways I think about openness.
Speaker 3: Is there like a particular project that you've worked on
Speaker 3: that you feel like, you know reflects your approach to
Speaker 3: responsible AI.
Speaker 4: So there's a really interesting project that we have underway
Speaker 4: at PAI that is looking at responsible practices squarely when
Speaker 4: it comes to the use of synthetic media. And what
Speaker 4: we heard from our community was that they were looking
Speaker 4: for a clear code of conduct about what does it
Speaker 4: mean to be responsible in this space. And so what
Speaker 4: happened is we pulled together a number of working groups
Speaker 4: to come together. They included industry representatives, They also included
Speaker 4: civil society organizations like WITNESS, a number of academic institutions
Speaker 4: and otherwise. And what we heard was that there were
Speaker 4: clear requirements that creators could take, that developers of the
Speaker 4: technology could take. And then also distributors. So when we
Speaker 4: think about those generative AI systems being deployed across platforms
Speaker 4: and otherwise, and we came up with a framework for
Speaker 4: what responsibility looks like. What does it mean to have consent,
Speaker 4: what does it mean to disclose responsibly, what does it
Speaker 4: mean to embed technology into it? So, for example, we've
Speaker 4: heard many people talk about the importance of water marking
Speaker 4: systems right and making sure that we have a way
Speaker 4: to water mark them. But what we know from the
Speaker 4: technology is that is a very very complex and complicated problem,
Speaker 4: and what might work on a technical level certainly hits
Speaker 4: a whole new set of complications when we start labeling
Speaker 4: and disclosing out to the public about what that technology
Speaker 4: actually means. All of these I believe are solvable problems,
Speaker 4: but they all needed to have a clear code underneath
Speaker 4: them that was saying this is what we will commit to.
Speaker 4: And we now have a number of organizations, many many
Speaker 4: of the large technology companies, but also many of the
Speaker 4: small startups who are operating in this base, civil society,
Speaker 4: media organizations like the BBC and the CBC who's have
Speaker 4: signed on. And one of the really exciting pieces of
Speaker 4: that is that we're now seeing how it's changing practice.
Speaker 4: So a year in we asked each of our partners
Speaker 4: to come up with a clear case study about how
Speaker 4: that work has changed the way they are making decisions,
Speaker 4: deploying technology and ensuring that they're being responsible in their use.
Speaker 4: And that is creating now a whole resource online that
Speaker 4: we're able to share with others about what does it
Speaker 4: mean to be responsible in this place. There's so much
Speaker 4: more work to be done, and the exciting thing is
Speaker 4: once you have a foundation like this in place, we
Speaker 4: can continue to build on it. So much interest now
Speaker 4: in the policy space, for example, about this work as well.
Speaker 3: Are there any specific examples of those sort of case
Speaker 3: studies or the real world experiences that say media organizations
Speaker 3: had that are interesting that are illuminating.
Speaker 4: Yes. So, for example, what we saw with the BBC
Speaker 4: is that they're developing a lot of content as a
Speaker 4: public broadcaster, both in terms of their news coverage but
Speaker 4: also in terms of some of the resources that they
Speaker 4: are developing for the British public as well. And what
Speaker 4: they talked about was the way in which they had
Speaker 4: used synthetic media in a very very sensitive environment where
Speaker 4: they were hearing from individuals talk about personal experiences, but
Speaker 4: wanted to have some way to change the face entirely
Speaker 4: in terms of the individuals who were speaking. So that's
Speaker 4: a very complicated ethical question, right, how do you do
Speaker 4: that responsibly? And what is the way in which you
Speaker 4: use that technology, and most importantly, how do you disclose it?
Speaker 4: So their case study looked at that in some real
Speaker 4: detail about the process they went through to make the
Speaker 4: decision responsibly to do what they chose, how they intended
Speaker 4: to use the technology in that space.
Speaker 3: Describe your work and some of these studies, the idea
Speaker 3: of transparency seems to be a theme. Talk about the
Speaker 3: importance of transparency in this kind of work.
Speaker 4: Yeah, transparency is fundamental to responsibility. I always like to
Speaker 4: say it's not accountability in a complete sense, but it
Speaker 4: is a first step to driving accountability more fully. So,
Speaker 4: when we think about how these systems are developed, they're
Speaker 4: often developed behind closed doors inside companies who are making
Speaker 4: decisions about what and how these products will work from
Speaker 4: a business perspective. And what disclosure and transparency can provide
Speaker 4: is some sense of the decisions that were made leading
Speaker 4: up to the way in which those models were deployed.
Speaker 4: So This could be ensuring that individual's private information was
Speaker 4: protected through the process and won't be inadvertently disclosed, or otherwise,
Speaker 4: it could be providing some sense of how well the
Speaker 4: system performs against a whole level of quality measures. So
Speaker 4: we have all of these different types of evaluations and
Speaker 4: a measures that are emerging about the quality of these
Speaker 4: systems as they're deployed. Being transparent about how they perform
Speaker 4: against these systems is really crucial to that as well.
Speaker 4: We have a whole ecosystem that's starting to emerge around
Speaker 4: auditing of these systems. So what does that look like
Speaker 4: we think about auditors and all sorts of other sectors
Speaker 4: of the economy. What does it look like to be
Speaker 4: auditing these systems to ensure that they're meeting all of
Speaker 4: those both legal but additional ethical requirements that we want
Speaker 4: to make sure that are in place.
Speaker 3: What are some of the hardest ethical dilemmas you've come
Speaker 3: up against in AI policy?
Speaker 4: Well, the interesting thing about AI policy right is what
Speaker 4: it works very simply in one setting, can be highly
Speaker 4: complicated in another setting. And so, for example, I have
Speaker 4: an app that I adore. It's an app on my
Speaker 4: phone that allows me to take a photo of a bird,
Speaker 4: and it will help me to better understand, you know,
Speaker 4: what that bird is, and give me all sorts of
Speaker 4: information about that bird. Now, it's probably right most of
Speaker 4: the time, and it's certainly right enough of the time
Speaker 4: to give me great pleasure and delight when I'm out walking.
Speaker 4: You could think about that exact same technology applied. So,
Speaker 4: for example, now you're a security guard and you're working
Speaker 4: in a shopping plaza, and you're able to take photos
Speaker 4: of individuals who you may think are acting suspiciously in
Speaker 4: some way and match that photo up with some sort
Speaker 4: of a database of individuals that may have been found,
Speaker 4: you know, to have some sort of connection to other
Speaker 4: criminal behavior in the past. Right, So what goes from
Speaker 4: being a delightful Oh, isn't this an interesting bird? To
Speaker 4: a very very creepy What is this say about surveillance
Speaker 4: and privacy and access to public spaces? And that is
Speaker 4: the nature of AI. So much of the concern about
Speaker 4: the ethical use and deployment of AI is how an
Speaker 4: organization is making the choices within the social and systemic
Speaker 4: structure they sit. So so much about the ethics of
Speaker 4: AI is understanding what is the use case how is
Speaker 4: it being used, how is it being constrained? How does
Speaker 4: it start to infringe upon what we think of as
Speaker 4: the human rights of an individual to privacy, And so
Speaker 4: you have to constantly be thinking about ethics. What could
Speaker 4: work very well in one situation absolutely doesn't work in another.
Speaker 4: We often talk about these as socio technical questions. Right,
Speaker 4: just because the technology works doesn't actually mean that it
Speaker 4: should be used and deployed.
Speaker 3: What's an example of where the partnership on AI influence
Speaker 3: changes either in policy or in industry practice.
Speaker 4: We talked a little bit about the Framework for Synthetic
Speaker 4: Media and how that has allowed companies and media organizations
Speaker 4: and civil society organizations to really think deeply about the
Speaker 4: way in which they're using this. Another area that we
Speaker 4: focused on has been around responsible deployment of foundation and
Speaker 4: large scale models. So, as I said, we issued a
Speaker 4: set of recommendations last year that really laid out for
Speaker 4: these very large developers and deployers of foundation and frontier models,
Speaker 4: what does good look like right from R and D
Speaker 4: through to deployment monitoring. And it has been very encouraging
Speaker 4: to see that that work has been picked up by
Speaker 4: companies and really articulated as part of the fabric of
Speaker 4: the deployment of their foundation models and systems moving forward.
Speaker 4: So much of this work is around creating clear definitions
Speaker 4: of what we're meaning as the technology evolves and clear
Speaker 4: sets of responsibilities. So it's great to see that work
Speaker 4: getting picked up. The NTIA in the United States just
Speaker 4: released a report on open models and the release of
Speaker 4: open models. Great to see our work sited there as
Speaker 4: contributing to that analysis. Great to see some of our
Speaker 4: definitions and synthetic media getting picked up by legislators in
Speaker 4: different countries. Really just it's important, I think, for us
Speaker 4: to build capacity, knowledge and understanding and our policy makers
Speaker 4: in this moment as the technology is evolving and accelerating
Speaker 4: in its development.
Speaker 3: What's the AI Alliance and why did Partnership on AI
Speaker 3: decide to join?
Speaker 4: So you had asked about the debate between open versus
Speaker 4: closed models and how that has evolved over the last year,
Speaker 4: and the AI Alliance was a community of organizations that
Speaker 4: came together to really think about, Okay, if we support
Speaker 4: open release of models, what does that look like and
Speaker 4: what does the community the need. And so that's about
Speaker 4: one hundred organizations IBM one of our founding partners is
Speaker 4: also one of the founding partners of the AI Alliance.
Speaker 4: It's a community that brings together a number of academic
Speaker 4: institutions many countries around the world, and they're really focused
Speaker 4: on how do you build the resources and infrastructure and
Speaker 4: community around what open source in these large scale models
Speaker 4: really mean. So that could be open data sets, that
Speaker 4: could be open technology development. Really building on that understanding
Speaker 4: that we need an infrastructure in place and a community
Speaker 4: engaged in thinking about safety and innovation through the open lens.
Speaker 2: This approach brings together organizations and experts from around the
Speaker 2: globe with different backgrounds, experiences, and perspectives to transparently and
Speaker 2: openly address the challenges and opportunities today. I poses the
Speaker 2: collaborative nature of the AI Alliance encourages discussion, debate, and innovation.
Speaker 2: Through these efforts, IBM is helping to build a community
Speaker 2: around transparent open technology.
Speaker 3: So I want to talk about the future for a minute.
Speaker 3: I'm trures what you see as the biggest obstacles to
Speaker 3: widespread adoption of responsible AI practices.
Speaker 4: One of the biggest obstacles today is an inability and
Speaker 4: a really a lack of understanding about how to use
Speaker 4: these models and how they can most effectively drive forward
Speaker 4: a company's commitment to whatever products and services it might
Speaker 4: be deploying. So I always recommend a couple of things
Speaker 4: for companies to really to think about this and to
Speaker 4: get started. One is think about how you are already
Speaker 4: using AI across all of your business products and services.
Speaker 4: Because already AI is integri into our workforces and into
Speaker 4: our workstreams and into the way in which companies are
Speaker 4: communicating with their clients every day. So understand how you
Speaker 4: are already using it and understand how you are integrating
Speaker 4: oversight and monitoring into those One of the best and
Speaker 4: clearest ways in which a company can really understand how
Speaker 4: to use this responsibly is through documentation. It's one of
Speaker 4: the areas where there's a clear consensus in the community.
Speaker 4: So how do you document the models that you are using,
Speaker 4: making sure that you've got a registry in place. How
Speaker 4: do you document the data that you are using and
Speaker 4: where that data comes from. This is sort of the
Speaker 4: first system, first line of defense in terms of understanding
Speaker 4: both what is in place and what you need to
Speaker 4: do in order to monitor it moving forward. And then secondly,
Speaker 4: once you've got an understanding of how you're already using
Speaker 4: the system, look at ways in which you could begin
Speaker 4: to pilot or iterate in a low risk way using
Speaker 4: these systems to really begin to see how and what
Speaker 4: structures you need to have in place to use it
Speaker 4: moving forward. And then thirdly, make sure that you structure
Speaker 4: a team in place internally that's able to do some
Speaker 4: of this. Cross departmental monitoring, knowledge sharing and learning boards
Speaker 4: are very very interested in this technology, So thinking about
Speaker 4: how you can have a system or a team in
Speaker 4: place internally that's reporting to your board, giving them a
Speaker 4: sense of both the opportunities that it is identifies for
Speaker 4: you and the additional risk mitigation and management you might
Speaker 4: be putting into place. And then you know, once you
Speaker 4: have those things into place, you're really going to need
Speaker 4: to understand how you work with the most valuable asset
Speaker 4: you have, which is your people. How do you make
Speaker 4: sure that AI systems are working for the workers, making
Speaker 4: sure that they're going into place. The most important and
Speaker 4: impressive implementations we see are those where you have the
Speaker 4: workers who are going to be engaged in this process.
Speaker 4: Central to figuring out how to develop and deploy it
Speaker 4: in order to really enhance their work, gets a core
Speaker 4: part of a set of shared Prosperity guidelines that we
Speaker 4: issued last year.
Speaker 3: And then from the side of policy makers, how should
Speaker 3: policy makers think about the balance between innovation and regulation.
Speaker 4: Yeah, it's so interesting, isn't it that we always think of,
Speaker 4: you know, innovation and regulation as being two sides of
Speaker 4: a coin, when in fact, so much innovation comes from
Speaker 4: having a clear set of guardrails and regulation in place.
Speaker 4: We think about all of the innovation that's happened in
Speaker 4: the automotive industry, right we can drive faster because we
Speaker 4: have breaks, we can drive faster because we have seat
Speaker 4: belts in place. So I think it's often interesting to
Speaker 4: me that we think about the two as being on
Speaker 4: either side of the coin, but in actual fact, you
Speaker 4: can't be innovative without being responsible as well. So I
Speaker 4: think from a policy maker perspective, what we have been
Speaker 4: really encouraging them to do is to understand that you've
Speaker 4: got foundational regulation in place that works for you nationally.
Speaker 4: This could be ensuring that you have strong privacy protections
Speaker 4: in place. It could be ensuring that you are understanding
Speaker 4: potential online harms, particularly to vulnerable communities and then look
Speaker 4: at what you need to be doing internationally to being
Speaker 4: both competitive and sustainable. There's all sorts of mechanisms that
Speaker 4: are in place right now at the international level to
Speaker 4: think about how do we build an interoperable space for
Speaker 4: these technologies moving forward.
Speaker 3: We've been talking in various ways about what it means
Speaker 3: to responsibly develop AI, and if you're going to boil
Speaker 3: that down, you know the essential concerns that people should
Speaker 3: be thinking about, like what are the key things to
Speaker 3: think about in responsible AI?
Speaker 4: So if you are a company, if we're talking specifically
Speaker 4: through the company lens, when we're thinking about responsible use
Speaker 4: of AI, the most important difference between this form of
Speaker 4: AI technologies and other forms of technologies that we have
Speaker 4: used previously is the integration of data and the training
Speaker 4: models that go on top of that data. So when
Speaker 4: we think about responsibility, first and foremost, you need to
Speaker 4: think about your data. Where did it come from, What
Speaker 4: consent and disclosure requirements do you have on it? Are
Speaker 4: you privacy protecting? You can't be thinking about AI within
Speaker 4: your company without thinking about data, and that's both your
Speaker 4: training data. But then once you're using your systems and
Speaker 4: integrating and interacting with your consumers, how are you protecting
Speaker 4: the data that's coming out of those systems as well?
Speaker 4: And then secondly is when you're thinking about how to
Speaker 4: deploy that AA system, the most important thing you want
Speaker 4: to think about is are we being transparent about how
Speaker 4: it's being used with our clients and our partners. So
Speaker 4: you know, the idea that if I'm a customer, I
Speaker 4: should know when I'm interacting with an AI system, I
Speaker 4: should know when I'm interacting with a human. So I
Speaker 4: think those two pieces are the fundamentals. And then of
Speaker 4: course you want to be thinking carefully about, you know,
Speaker 4: making sure that whatever jurisdiction you're operating in, you're meeting
Speaker 4: all of the legal requirements with regard to the services
Speaker 4: and products that you're offering.
Speaker 3: Let's finish with the speed round, complete the sentence. In
Speaker 3: five years, AI will will.
Speaker 4: Drive equity, justice and shared prosperity if we choose to
Speaker 4: set that future trajectory for this technology.
Speaker 3: What is the number one thing that people misunderstand about AI?
Speaker 4: AI is not good, and AI is not bad, but
Speaker 4: AI is also not neutral. It is a product of
Speaker 4: the choices we make as humans about how we deploy it.
Speaker 4: In the world.
Speaker 3: What advice would you give yourself ten years ago to
Speaker 3: better prepare yourself for today?
Speaker 4: Ten years ago, I wish that I had known just
Speaker 4: how fundamental the enduring questions of ethics and responsibility would
Speaker 4: be as we developed this technology moving forward, So many
Speaker 4: of the questions that we ask about AI are questions
Speaker 4: about ourselves and the way in which we use technology
Speaker 4: and the way in which technology can advance the work
Speaker 4: we're doing.
Speaker 3: How do you use AI in your day to day life?
Speaker 4: I use AI all day every day, So whether it's
Speaker 4: my bird app when I go out for my morning walk,
Speaker 4: helping me to better identify birds that I see, or
Speaker 4: whether it is my mapping app that's helping me to
Speaker 4: get more speedily through traffic to whatever meeting I need
Speaker 4: to go to. I use AI all the time. I
Speaker 4: really enjoy using some of the generative AI chatbots more
Speaker 4: for fun than for anything else. As a creative partner
Speaker 4: in thinking through ideas and integrating it into all aspects
Speaker 4: of our lives. Is just so much about the way
Speaker 4: in which we live today.
Speaker 3: So people use the word open to mean different things,
Speaker 3: even just in the context of technology. How do you
Speaker 3: define open in the context of your work.
Speaker 4: So there is the question of open as it is
Speaker 4: deployed to technology, which we've talked a lot about. But
Speaker 4: I do think a big piece of PAI is open minded.
Speaker 4: We need to be open minded truly to listen to,
Speaker 4: for example, what a civil society advocate might say about
Speaker 4: what they're seeing in terms of the way in which
Speaker 4: AI is interacting in a particular community. Or we need
Speaker 4: to be open minded to hear from a technologist about
Speaker 4: their hopes and dreams of where this technology might go
Speaker 4: moving forward. And we need to have those conversations listening
Speaker 4: to each other to really identify how we're going to
Speaker 4: meet the challenge and opportunity of AI today. So open
Speaker 4: is just fundamental to the partnership on AI. I often
Speaker 4: call it an experiment in open innovation.
Speaker 3: Rebecca, thank you so much for your time.
Speaker 4: It is my pleasure. Thank you for having me.
Speaker 2: Thank you to Rebecca and Jacob for that engaging discussion
Speaker 2: about some of the most pressing issues facing the future
Speaker 2: of AI. As Rebecca emphasized, whether you're thinking about data
Speaker 2: privacy or disclosure, transparency and openness are key to solving
Speaker 2: challenges and capitalizing on new opportunities by developing best practices
Speaker 2: and resources Partnership on AI is building out the guardrails
Speaker 2: to support the release of open source models and the
Speaker 2: practice of post deployment monitoring. By sharing their work with
Speaker 2: the broader community, Rebecca and Pai are demonstrating how working responsibly,
Speaker 2: ethically and openly can help drive innovation. Smart Talks with
Speaker 2: IBM is produced by Matt Romano, Joey Fishground, Amy Gaines McQuaid,
Speaker 2: and Jacob Goldstein. We're edited by Lydia gene Kott. Our
Speaker 2: engineers are Sarah Bugaier and Ben Holliday. Theme song by Gramoscope.
Speaker 2: Special thanks to the eight Bar and IBM teams, as
Speaker 2: well as the Pushkin marketing team. Smart Talks with IBM
Speaker 2: is a production of Pushkin Industries and Ruby Studio at iHeartMedia.
Speaker 2: To find more Pushkin podcasts, listen on the iHeartRadio app,
Speaker 2: Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Glapwell.
Speaker 2: This is a paid advertisement from IBM. The conversations on
Speaker 2: this podcast don't necessarily represent IBM's positions, strategies or opinions.