Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got
Speaker 1: something a little different to share with you. It's a
Speaker 1: new season of the Smart Talks with IBM podcast series.
Speaker 2: This season, on smart Talks, Malcolm Gladwell and team are
Speaker 2: diving into the transformative world of artificial intelligence with a
Speaker 2: fresh perspective on the concept of open What does open
Speaker 2: really mean in the context of AI. It can mean
Speaker 2: open source code or open data, but it also encompasses
Speaker 2: fostering an ecosystem of ideas, ensuring diverse perspectives are heard,
Speaker 2: and enabling new levels of transparency.
Speaker 1: Join hosts from your favorite pushkin podcasts as they explore
Speaker 1: how openness and AI is reshaping industries, driving innovation, and
Speaker 1: redefining what's possible. You'll hear from industry experts and leaders
Speaker 1: about the implication and possibilities of open AI, and of course,
Speaker 1: Malcolm Gladwell will be there to guide you through the
Speaker 1: season with his unique insights.
Speaker 2: Look out for new episodes of Smart Talks every other
Speaker 2: week on the iHeartRadio app, Apple Podcasts, or wherever you
Speaker 2: get your podcast and learn more at IBM dot com,
Speaker 2: Slash smart Talks.
Speaker 3: Pushkin Hello, Hello, Welcome to Smart Talks with IBM, a
Speaker 3: podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell.
Speaker 3: This season, we're diving back into the world of artificial intelligence,
Speaker 3: but with a focus on the powerful concept of open
Speaker 3: its possibilities, implications, and misconceptions. We'll look at openness from
Speaker 3: a variety of angles and explore how the concept is
Speaker 3: already reshaping industries, ways of doing business and our very
Speaker 3: notion of what's possible. In today's episode, Jacob Goldstein sits
Speaker 3: down with Rebecca Finley, the CEO of the Partnership on Ai,
Speaker 3: a nonprofit group grappling with important questions around the future
Speaker 3: of AI. Their conversation focuses on Rebecca's work bringing together
Speaker 3: a community of diverse stakeholders to help shape the conversation
Speaker 3: around accountable AI governance. Rebecca explains why transparency is so
Speaker 3: crucial for scaling the technology responsibly, and she highlights how
Speaker 3: working with groups like the AI Alliance can provide valuable
Speaker 3: insights in order to build the resources, infrastructure, and community
Speaker 3: around releasing open source models. So, without further ado, let's
Speaker 3: get to that conversation.
Speaker 4: Can you just say your name and your job?
Speaker 5: My name is Rebecca Finley. I am the CEO of
Speaker 5: the Partnership on AI to Benefit People and Society, often
Speaker 5: referred to as PAI.
Speaker 4: How did you get here? What was your job before
Speaker 4: you have the job that you have now?
Speaker 5: I came to about three years ago having had the
Speaker 5: opportunity to work for the Canadian Institute for Advance Research,
Speaker 5: developing and deploying all of their programs related to the
Speaker 5: intersection of technology and society, and one of the areas
Speaker 5: that the Canadian Institute had been funding since nineteen eighty
Speaker 5: two was research into artificial intelligence.
Speaker 4: Wow, early, they were early.
Speaker 5: It was a very early commitment and an ongoing commitment
Speaker 5: at the Institute to fund long term fundamental questions of
Speaker 5: scientific importance in interdisciplinary research programs that were often committed
Speaker 5: and funded to for well over a decade. The AI
Speaker 5: Robotics and Society program that kicked off the work at
Speaker 5: the Institute eventually became a program very much focused on
Speaker 5: deep learning and reinforcement learning, neural networks. All of the
Speaker 5: current iteration of AI, or certainly the pregenerative AI iteration
Speaker 5: of AI that led to this transformation that we've seen
Speaker 5: in terms of online search and all sorts of ways
Speaker 5: in which predictive AI has been deployed. So I had
Speaker 5: the opportunity to see the very early days of that
Speaker 5: research coming together, and when in the early sort of
Speaker 5: two thousand and twenty and tens, when compute capability came
Speaker 5: together with data capability through some of the Internet companies
Speaker 5: and otherwise, and we really saw this technology start to
Speaker 5: take off, I had the opportunity to start up a
Speaker 5: program specifically focused on the impacts of AI in society.
Speaker 5: There was, as you know, at that time, some concerns
Speaker 5: both about the potential for the technology, but also in
Speaker 5: terms of what we were seeing around data sets and
Speaker 5: bias and discrimination and potential impact on future jobs. And
Speaker 5: so bringing a whole group of experts, whether they were
Speaker 5: ethicists or lawyers or economists sociologists into the discussion about
Speaker 5: AI was core to that new program and continues to
Speaker 5: be core to my commitment to bringing diverse perspectives together
Speaker 5: to solve the challenges and opportunities that AI offers today.
Speaker 4: So specifically, what is your job now? What is the
Speaker 4: work you do? What is the work that PAI does.
Speaker 5: I like to answer that question by asking two questions.
Speaker 5: First and foremost, do you believe that the world is
Speaker 5: more divided today than it ever has been in recent history.
Speaker 5: And do you believe that if we don't create spaces
Speaker 5: for very different perspectives to come together, we won't be
Speaker 5: able to solve the challenges that are in front of
Speaker 5: the world today. My answer to both of those questions is, yes,
Speaker 5: we're more divided, and two, we need to seek out
Speaker 5: those spaces where those very different perspectives can come together
Speaker 5: to solve those great challenges. And that's what I get
Speaker 5: to do as CEO of the Partnership on AI. We
Speaker 5: were begun in twenty sixteen with a fundamental commitment to
Speaker 5: bringing together experts, whether they were in industry, academia, civil society,
Speaker 5: or philanthropy, coming together to identify what are the most
Speaker 5: important questions when we think about developing AI centered on
Speaker 5: people and communities, and then how do we begin to
Speaker 5: develop the solutions to make sure we benefit appropriately.
Speaker 4: So that's a very big picture set of ideas. I'm
Speaker 4: curious on a sort of more day to day level.
Speaker 4: I mean, you talk about collaborating with all these different
Speaker 4: kinds of people, all these different groups, what does that
Speaker 4: actually look like. What are some specific examples of how
Speaker 4: you do this work.
Speaker 5: So right now we have about one hundred and twenty
Speaker 5: partners in sixteen countries. They come together through working groups
Speaker 5: that we look at through a variety of different perspectives.
Speaker 5: It could be AI, labor and the economy. It could
Speaker 5: be how do you build a healthy information ecosystem. It
Speaker 5: could be how do you bring more diverse perspectives into
Speaker 5: the inclusive and equitable development of AI. It could be
Speaker 5: what are the emerging opportunities with these very very large
Speaker 5: foundation model applications and how do you deploy those safely?
Speaker 5: And these groups come together most importantly to say what
Speaker 5: are the questions we need to answer collectively, So they
Speaker 5: come together in working groups. I have an amazing staff
Speaker 5: team who hold the pen on synthesizing research and data
Speaker 5: and evidence, developing frameworks, best practice resources, all sorts of
Speaker 5: things that we can offer up to the community, be
Speaker 5: they in industry or in policy, to say this is
Speaker 5: how we can well, this is what good looks like,
Speaker 5: and this is how we can do it on a
Speaker 5: day to day basis. So that's what we do, and
Speaker 5: then we publish our materials. It's all open. We make
Speaker 5: sure that we get them into the hands of those
Speaker 5: communities that can use them, and then we drive and
Speaker 5: work with those communities to put them into practice.
Speaker 4: You use the word open there and describing your publications.
Speaker 4: I know, in the world of AI on the sort
Speaker 4: of technical side, there's a lot of debate, say, or
Speaker 4: discussion about kind of open versus closed AI, And I'm
Speaker 4: curious how you kind of encounter that particular discussion. What
Speaker 4: is your view on open versus closed AI?
Speaker 5: So the current discussion between open and closed release of
Speaker 5: AI models came once we saw a CHAT, GPT and
Speaker 5: other very large generative AI systems being deployed out into
Speaker 5: the hands of consumers around the world, and there emerged
Speaker 5: some fear about the potential of these models to act
Speaker 5: in all sorts of catastrophic ways. So there were concerns
Speaker 5: that the models could be deployed with regard to different
Speaker 5: development of viruses or biomedical weapons or even nuclear weapons,
Speaker 5: or through manipulation or otherwise. So this are emerged about
Speaker 5: over the last eighteen months, this real concern that these models,
Speaker 5: if deployed openly, could lead to some level of truly
Speaker 5: catastrophic risk. And what emerged is actually that we discovered
Speaker 5: that through a whole bunch of work that's been done
Speaker 5: over the last little while that releasing them openly has
Speaker 5: not led and doesn't appear to be leading in any
Speaker 5: way to catastrophic risk. In facts, releasing them openly allows
Speaker 5: for much more greater scrutiny and understanding of the safety
Speaker 5: measures that have been put into place, And so what
Speaker 5: happened was sort of the pendulum swung very much towards
Speaker 5: concerned about really catastrophic risk and safety over the last year,
Speaker 5: and over the last year we've seen it swing back
Speaker 5: as we learn more and more about how these models
Speaker 5: are being used and how they are being deployed into
Speaker 5: the world. My feeling is we must approach this work openly,
Speaker 5: and it's not just open release of models or what
Speaker 5: we think of as traditional open source forms of model
Speaker 5: development or otherwise, but we really need to think about
Speaker 5: how do we build an open innovation ecosystem that fundamentally
Speaker 5: allows both for the innovation to be shared with many people,
Speaker 5: but also for safety and security to be rigorously upheld.
Speaker 4: So when you talk about this kind of broader idea
Speaker 4: of open innovation beyond open source or you know, transparency
Speaker 4: and models, like, what do you mean sort of specifically
Speaker 4: how does that look in the world.
Speaker 5: So I have three particular points of view when it
Speaker 5: comes to open innovation, because I think we need to
Speaker 5: think both both upstream around the research that is driving
Speaker 5: these models and downstream in terms of the benefits of
Speaker 5: these models to others. So, first and foremost, what we
Speaker 5: have known in terms of how AI has been developed,
Speaker 5: and yes, I had an opportunity to see it when
Speaker 5: I was at the Canadian Institute for Advanced Research is
Speaker 5: a very open form of scientific publication and rigorous peer review.
Speaker 5: And what happens when we release openly is you have
Speaker 5: an opportunity for the research to be interrogated to determine
Speaker 5: the quality and significance of that, but then also for
Speaker 5: it to be picked up by many others. And then secondly,
Speaker 5: openness for me is about transparency. We released a set
Speaker 5: of very strong recommendations last year around the way in
Speaker 5: which these very large foundation models could be deployed safely.
Speaker 5: They're all about disclosure. They're all about disclosure and documentation
Speaker 5: right from the early days pre R and D development
Speaker 5: of these systems, right in terms of thinking about what's
Speaker 5: in the training data and how is it being used?
Speaker 5: All the way through to post deployment monitoring and disclosure.
Speaker 5: So I really think that this is important transparency through it.
Speaker 5: And then the third piece is openness in terms of
Speaker 5: who was around the table to benefit from this technology.
Speaker 5: We know that if we're really going to see these
Speaker 5: new models being successful deployed into education or healthcare or
Speaker 5: climate and sustainability, we need to have those experts in
Speaker 5: those communities at the table charting this and making sure
Speaker 5: that the technology is working for them. So those are
Speaker 5: the three ways I think about openness.
Speaker 4: Is there like a particular project that you've worked on
Speaker 4: that you feel like you know reflects your approach to
Speaker 4: responsible AI.
Speaker 5: So there's a really interesting project that we have underway
Speaker 5: at PAI that is looking at responsible practices squarely when
Speaker 5: it comes to the use of synthetic media. And what
Speaker 5: we heard from our community was that they were looking
Speaker 5: for a clear code of conduct about what does it
Speaker 5: mean to be responsible in this space? And so what
Speaker 5: happened is we pulled together a number of working groups
Speaker 5: to come together. They included industry representatives, They also included
Speaker 5: civil society organizations like WITNESS, a number of academic institutions
Speaker 5: and otherwise, and what we heard was that there were
Speaker 5: clear requirements that creators could take, that developers of the
Speaker 5: technology could take, and then also distributors. So when we
Speaker 5: think about those generative AI systems being deployed across platforms
Speaker 5: and otherwise, and we came up with a framework for
Speaker 5: what responsibility looks like. What does it mean to have consent,
Speaker 5: what does it mean to disclose responsibly, what does it
Speaker 5: mean to embed technology into it? So, for example, we've
Speaker 5: heard many people talk about the importance of water marking
Speaker 5: systems right and making sure that we have a way
Speaker 5: to water mark them. But what we know from the
Speaker 5: technology is that is a very very complex and complicated problem,
Speaker 5: and what might work on a technical level certainly hits
Speaker 5: a whole new set of complications when we start labeling
Speaker 5: and disclosing out to the public about what that technology
Speaker 5: actually means. All of these, I believe are solvable problems,
Speaker 5: but they all needed to have a clear code underneath
Speaker 5: them that was saying this is what we will commit to.
Speaker 5: And we now have a number of organizations, many many
Speaker 5: of the large technology companies but also many of the
Speaker 5: small startups who are operating in this based civil society
Speaker 5: and media organizations like the BBC and the CBC who's
Speaker 5: have signed on. And one of the really exciting pieces
Speaker 5: of that is that we're now seeing how it's changing practice.
Speaker 5: So a year in we asked each of our partners
Speaker 5: to come up with a clear case study about how
Speaker 5: that work has changed the way they are making decisions,
Speaker 5: deploying technology and ensuring that they're being responsible in their use.
Speaker 5: And that is creating now a whole resource online that
Speaker 5: we're able to share with others about what does it
Speaker 5: mean to be responsible in this place. There's so much
Speaker 5: more work to be done, and the exciting thing is
Speaker 5: once you have a foundation like this in place, we
Speaker 5: can continue to build on it. So much interest now
Speaker 5: in the policy space, for example, about this work as well.
Speaker 4: Are there any specific examples of those sort of case
Speaker 4: studies or the real world experiences that say media organizations
Speaker 4: had that are interesting that are illuminating.
Speaker 5: Yes. So, for example, what we saw with the BBC
Speaker 5: is that they're developing a lot of content as a
Speaker 5: public broadcast are both in terms of their news coverage
Speaker 5: but also in terms of some of the resources that
Speaker 5: they are developing for the British public as well. And
Speaker 5: what they talked about was the way in which they
Speaker 5: had used synthetic media in a very very sensitive environment
Speaker 5: where they were hearing from individuals talk about personal experiences,
Speaker 5: but wanted to have some way to change the face
Speaker 5: entirely in terms of the individuals who were speaking. So
Speaker 5: that's a very complicated ethical question, right, how do you
Speaker 5: do that responsibly? And what is the way in which
Speaker 5: you use that technology, and most importantly, how do you
Speaker 5: disclose it? So their case study looked at that in
Speaker 5: some real detail about the process they went through to
Speaker 5: make the decision responsibly to do what they chose, how
Speaker 5: they intended to use the technology in that space.
Speaker 4: As you describe your work and some of these studies,
Speaker 4: the idea of transparency seems to be a theme. Talk
Speaker 4: about the importance of transparency in this kind of work.
Speaker 5: Yeah, transparency is fundamental to responsibility. I always like to
Speaker 5: say it's not accountability in a complete sense, but it
Speaker 5: is a first step to driving accountability more fully, so,
Speaker 5: when we think about how these systems are developed, they're
Speaker 5: often developed behind closed doors inside companies who are making
Speaker 5: decisions about what and how these products will work from
Speaker 5: a business perspective, and what disclosure and transparency can provide
Speaker 5: is some sense of the decisions that were made leading
Speaker 5: up to the way in which those models were deployed.
Speaker 5: So this could be ensuring that individual's private information was
Speaker 5: protected through the process and won't be inadvertently disclosed or otherwise.
Speaker 5: It could be providing some sense of how well the
Speaker 5: system performs against a whole level of quality measures. So
Speaker 5: we have all of these different types of evaluations and
Speaker 5: a measures that are emerging about the quality of these
Speaker 5: systems as they're deployed. Being transparent about how they perform
Speaker 5: against these systems is really crucial to that as well.
Speaker 5: We have a whole ecosystem that's starting to emerge around
Speaker 5: auditing of these systems. So what does that look like
Speaker 5: we think about auditors and all sorts of other sectors
Speaker 5: of the economy. What does it look like to be
Speaker 5: auditing these systems to ensure that they're meeting all of
Speaker 5: those both legal but additional ethical requirements that we want
Speaker 5: to make sure that are in place.
Speaker 4: What are some of the hardest ethical dilemmas you've come
Speaker 4: up against in AI policy.
Speaker 5: Well, the interesting thing about AI policy right is what
Speaker 5: it works very simply in one setting, can be highly
Speaker 5: complicated in another setting. And so, for example, I have
Speaker 5: an app that I adore. It's an app on my
Speaker 5: phone that allows me to take a photo of a bird,
Speaker 5: and it will help me to better understand and you know,
Speaker 5: what that bird is, and give me all sorts of
Speaker 5: information about that bird. Now, it's probably right most of
Speaker 5: the time, and it's certainly right enough of the time
Speaker 5: to give me great pleasure and delight when I'm out walking.
Speaker 5: You could think about that exact same technology applied. So
Speaker 5: for example, now you're a security guard and you're working
Speaker 5: in a shopping plaza, and you're able to take photos
Speaker 5: of individuals who you may think are acting suspiciously in
Speaker 5: some way and match that photo up with some sort
Speaker 5: of a database of individuals that may have been found,
Speaker 5: you know, to have some sort of connection to other
Speaker 5: criminal behavior in the past. Right, So what goes from
Speaker 5: being a delightful Oh, isn't this an interesting bird? To
Speaker 5: a very very creepy What is this say about surveillance
Speaker 5: and privacy and access to public spaces? And that is
Speaker 5: the nature of AI. So much of the concern about
Speaker 5: the ethical use and deployment of AI is how organization
Speaker 5: is making the choices within the social and systemic structure
Speaker 5: they sit. So so much about the ethics of AI
Speaker 5: is understanding what is the use case, how is it
Speaker 5: being used, how is it being constrained? How does it
Speaker 5: start to infringe upon what we think of as the
Speaker 5: human rights of an individual to privacy? And so you
Speaker 5: have to constantly be thinking about ethics. What could work
Speaker 5: very well in one situation absolutely doesn't work in another.
Speaker 5: We often talk about these as socio technical questions. Right,
Speaker 5: just because the technology works doesn't actually mean that it
Speaker 5: should be used and deployed.
Speaker 4: What's an example of where the partnership on AI influence
Speaker 4: changes either in policy or in industry practice.
Speaker 5: We talked a little bit about the framework for Synthetic
Speaker 5: Media and how that has allowed companies and media organizations
Speaker 5: and civil society or organizations to really think deeply about
Speaker 5: the way in which they're using this. Another area that
Speaker 5: we focused on has been around responsible deployment of foundation
Speaker 5: and large scale models. So, as I said, we issued
Speaker 5: a set of recommendations last year that really laid out
Speaker 5: for these very large developers and deployers of foundation and
Speaker 5: frontier models, what does good look like right from R
Speaker 5: and D through to deployment monitoring, and it has been
Speaker 5: very encouraging to see that that work has been picked
Speaker 5: up by companies and really articulated as part of the
Speaker 5: fabric of the deployment of their foundation models and systems
Speaker 5: moving forward. So much of this work is around creating
Speaker 5: clear definitions of what we're meaning as the technology evolves
Speaker 5: and clear sets of responsibility. So it's great to see
Speaker 5: that work getting picked up. The NTIA in the United
Speaker 5: States just released a report on open models and the
Speaker 5: release of open models. Great to see our work sited
Speaker 5: there as contributing to that analysis. Great to see some
Speaker 5: of our definitions and synthetic media getting picked up by
Speaker 5: legislators in different countries. Really just it's important, I think,
Speaker 5: for us to build capacity, knowledge and understanding and our
Speaker 5: policy makers in this moment as the technology is evolving
Speaker 5: and accelerating in its development.
Speaker 4: What's the AI Alliance and why did Partnership on AI
Speaker 4: decide to join?
Speaker 5: So you had asked about the debate between open versus
Speaker 5: closed models and how that has evolved over the last year,
Speaker 5: and the AI Alliance was a community of organizations that
Speaker 5: came together to really think about, okay, if we support
Speaker 5: open release of models, what does that look like and
Speaker 5: what does the community need? And so that's about one
Speaker 5: hundred organizations. IBM, one of our founding partners, is also
Speaker 5: one of the founding partner of the AI Alliance. It's
Speaker 5: a community that brings together a number of academic institutions
Speaker 5: many countries around the world, and they're really focused on
Speaker 5: how do you build the resources and infrastructure and community
Speaker 5: around what open source in these large scale models really mean.
Speaker 5: So that could be open data sets, that could be
Speaker 5: open technology development. Really building on that understanding that we
Speaker 5: need an infrastructure in place and a community engaged in
Speaker 5: thinking about safety and innovation through the open lens.
Speaker 3: This approach brings together organizations and experts from around the
Speaker 3: globe with different backgrounds, experiences, and perspectives to transparently and
Speaker 3: openly address the challenges and opportunities today. I poses the
Speaker 3: collaborative nature of the AI Alliance encourages discussion, debate, and innovation.
Speaker 3: Through these efforts, IBM is helping to build the community
Speaker 3: around transparent open technology.
Speaker 4: So I want to talk about the future for a minute.
Speaker 4: I'm sure is what you see as the biggest obstacles
Speaker 4: to widespread adoption of responsible AI practices.
Speaker 5: One of the biggest obstacles today is an inability and
Speaker 5: really a lack of understanding about how to use these
Speaker 5: models and how they can most effectively drive forward a
Speaker 5: company's commitment to whatever products and services it might be deploying.
Speaker 5: So I always recommend a couple of things for companies
Speaker 5: really to think about this and to get started. One
Speaker 5: is think about how you are already using AI across
Speaker 5: all of your business products and services, Because already AI
Speaker 5: is integrated into our workforces and into our workstreams, and
Speaker 5: into the way in which companies are communicating with their
Speaker 5: clients every day. So understand how you are already using
Speaker 5: it and understand how you are integrating oversight and monitoring
Speaker 5: into those One of the best and clearest ways in
Speaker 5: which a company can really understand how to use this
Speaker 5: responsibly is through documentation. It's one of the areas where
Speaker 5: there's a clear consensus in the community. So how do
Speaker 5: you document the models that you are using, making sure
Speaker 5: that you've got a registry in place. How do you
Speaker 5: document the data that you are using and where that
Speaker 5: data comes from. This is sort of the first system,
Speaker 5: first line of defense in terms of understanding both what
Speaker 5: is in place and what you need to do in
Speaker 5: order to monitor it moving forward. And then secondly, once
Speaker 5: you've got an understanding of how you're already using the system,
Speaker 5: look at ways in which you could begin to pilot
Speaker 5: or iterate in a low risk way using these systems
Speaker 5: to really begin to see how and what structures you
Speaker 5: need to have in place to use it moving forward.
Speaker 5: And then thirdly, make sure that you structure a team
Speaker 5: in place internally that's able to do some of this
Speaker 5: cross departmental monitoring, Knowledge sharing and learning boards are very
Speaker 5: very interested in this technology, So thinking about how you
Speaker 5: can have a system or a team in place internally
Speaker 5: that's reporting to your board, giving them a sense of
Speaker 5: both the opportunities that it identifies for you and the
Speaker 5: additional risk mitigation and management you might be putting into place.
Speaker 5: And then once you have those things into place, you're
Speaker 5: really going to need to understand how you work with
Speaker 5: the most valuable asset you have, which is your people.
Speaker 5: How do you make sure that AI systems are working
Speaker 5: for the workers, making sure that they're going into place.
Speaker 5: The most important and impressive implementations we see are those
Speaker 5: where you have the workers who are going to be
Speaker 5: engaged in this process central to figuring out how to
Speaker 5: develop and deploy it in order to really enhance their work.
Speaker 5: It's a core part of a set of Shared Prosperity
Speaker 5: guidelines that we issued last year.
Speaker 4: And then, from the side of policy makers, how should
Speaker 4: policy makers think about the balance between innovation and regulation.
Speaker 5: Yeah, it's so interesting, isn't it that we always think of,
Speaker 5: you know, innovation and regulation as being two sides of
Speaker 5: a coin, when in fact, so much innovation comes from
Speaker 5: having a clear set of guardrails and regulation in place.
Speaker 5: We think about all of the innovation that's happened in
Speaker 5: the automotive industry, right we can drive faster because we
Speaker 5: have breaks, we can drive faster because we have seat
Speaker 5: belts in place. So I think it's often interesting to
Speaker 5: me that we think about the two as being on
Speaker 5: either side of the coin, but an actual fact, you
Speaker 5: can't be innovative without being responsible as well. And so
Speaker 5: I think from a policy maker perspective, what we have
Speaker 5: been really encouraging them to do is to understand that
Speaker 5: you've got foundational regulation in place that works for you. Nationally,
Speaker 5: this could be ensuring that you have strong privacy protections
Speaker 5: in place. It could be ensuring that you are understanding
Speaker 5: potential online harms, particularly to vulnerable communities, and then look
Speaker 5: at what you need to be doing internationally to being
Speaker 5: both competitive and sustainable. There's all sorts of mechanisms that
Speaker 5: are in place right now at the international level to
Speaker 5: think about how do we build an interoperable space for
Speaker 5: these technologies moving forward.
Speaker 4: We've been talking in various ways about what it means
Speaker 4: to responsibly develop AI, and if you're going to boil
Speaker 4: that down, you know the essential concerns that people should
Speaker 4: be thinking about, like what are the key things to
Speaker 4: think about in responsible AI?
Speaker 5: So if you are a company, if we're talking specifically
Speaker 5: through the company lens, when we're thinking about responsese of AI,
Speaker 5: the most important difference between this form of AI technologies
Speaker 5: and other forms of technologies that we have used previously
Speaker 5: is the integration of data and the training models that
Speaker 5: go on top of that data. So when we think
Speaker 5: about responsibility, first and foremost, you need to think about
Speaker 5: your data. Where did it come from? What consent and
Speaker 5: disclosure requirements do you have on it? Are you privacy protecting?
Speaker 5: You can't be thinking about AI within your company without
Speaker 5: thinking about data, and that's both your training data. But
Speaker 5: then once you're using your systems and integrating and interacting
Speaker 5: with your consumers, how are you protecting the data that's
Speaker 5: coming out of those systems as well? And then secondly
Speaker 5: is when you're thinking about how to deploy that AI system,
Speaker 5: the most important thing you want to think about is
Speaker 5: are we being transparent about how it's being you with
Speaker 5: our clients and our partners. So you know, the idea
Speaker 5: that if I'm a customer, I should know when I'm
Speaker 5: interacting with an AI system, I should know when I'm
Speaker 5: interacting with a human. So I think those two pieces
Speaker 5: are the fundamentals. And then of course you want to
Speaker 5: be thinking carefully about, you know, making sure that whatever
Speaker 5: jurisdiction you're operating in, you're meeting all of the legal
Speaker 5: requirements with regard to the services and products that you're offering.
Speaker 4: Let's finish with the speed round, complete the sentence. In
Speaker 4: five years, AI will.
Speaker 5: Will drive equity, justice, and shared prosperity if we choose
Speaker 5: to set that future trajectory for this technology.
Speaker 4: What is the number one thing that people misunderstand about AI.
Speaker 5: AI is not good, and AI is not bad, But
Speaker 5: AI is also not neutral. It is a product of
Speaker 5: the choices we make as humans about how we deploy
Speaker 5: it in the world.
Speaker 4: What advice would you give yourself ten years ago to
Speaker 4: better prepare yourself for today?
Speaker 5: Ten years ago, I wish that I had known just
Speaker 5: how fundamental the enduring questions of ethics and responsibility would
Speaker 5: be as we developed this technology moving forward, So many
Speaker 5: of the questions that we ask about AI are questions
Speaker 5: about ourselves and the way in which we use technology,
Speaker 5: and the way in which technology can advance the work
Speaker 5: we're doing.
Speaker 4: How do you use AI in your day to day
Speaker 4: life today?
Speaker 5: I use AI all day every day. So whether it's
Speaker 5: my bird app when I go out for my learning
Speaker 5: walk helping me to better identify birds that I see,
Speaker 5: or whether it is my mapping app that's helping me
Speaker 5: to get more speedily through traffic to whatever meeting I
Speaker 5: need to go to, I use AI all the time.
Speaker 5: I really enjoy using some of the generative AI chatbots
Speaker 5: more for fun than for anything else. As a creative
Speaker 5: partner in thinking through ideas and integrating it into all
Speaker 5: aspects of our lives. Is just so much about the
Speaker 5: way in which we live today.
Speaker 4: So people use the word open to mean different things,
Speaker 4: even just in the context of technology. How do you
Speaker 4: define open in the context.
Speaker 2: Of your work.
Speaker 5: So there is the question of open as it is
Speaker 5: deployed to technology, which we've talked a lot about. But
Speaker 5: I do think a big piece of PAI is open minded.
Speaker 5: We need to be open minded truly to listen to,
Speaker 5: for example, what a civil society advocate might say about
Speaker 5: what they're seeing in terms of the way in which
Speaker 5: AI is interacting in a particular community. Or we need
Speaker 5: to be open minded to hear from a technologist about
Speaker 5: their hopes and dreams of where this technology might go
Speaker 5: moving forward. And we need to have those conversations listening
Speaker 5: to each other to really identify how we're going to
Speaker 5: meet the challenge and opportunity of AI today. So open
Speaker 5: is just fundamental to the partnership on AI. I often
Speaker 5: call it an experiment in open innovation.
Speaker 4: Rebecca, thank you so much for your time.
Speaker 5: It is my pleasure. Thank you for having me.
Speaker 3: Thank you to Rebecca and Jacob for that engaging discussion
Speaker 3: about some of the most pressing issues facing the future
Speaker 3: of AI. As Rebecca emphasized, whether you're thinking about data
Speaker 3: privacy or disclosure, transparency and openness are key to solving
Speaker 3: challenges and capitalizing on new opportunities by developing best practices
Speaker 3: and resources. Partnership on AI is building out the guardrails
Speaker 3: to support the release of open source models and the
Speaker 3: practice of post deployment monitoring. By sharing their work with
Speaker 3: the broader community, Rebecca and Pai are demonstrating how working responsibly,
Speaker 3: ethically and openly can help drive innovation. Smart Talks with
Speaker 3: IBM is produced by Matt Romano, Joey Fishground, Amy Gaines McQuaid,
Speaker 3: and Jacob Goldstein. We're edited by Lydia jen Kott. Our
Speaker 3: engineers are Sarah Brugaer and Ben Holliday. Theme song by Gramoscope.
Speaker 3: Special thanks to the eight Bar and IBM teams, as
Speaker 3: well as the Pushkin marketing team. Smart Talks with IBM
Speaker 3: is a production of Pushkin Industries and Ruby Studio at iHeartMedia.
Speaker 3: To find more Pushkin podcasts, listen on the iHeartRadio app,
Speaker 3: Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Glapwell.
Speaker 3: This is a paid advertisement from IBM. The conversations on
Speaker 3: this podcast don't necessarily represent IBM's positions, strategies or opinions,