WEBVTT - Smart Talks with IBM: Responsible AI - Why Businesses Need Reliable AI Governance 0:00:04.440 --> 0:00:12.639 Welcome to tech Stuff, a production from iHeartRadio. This season 0:00:12.720 --> 0:00:16.200 on smart Talks with IBM, Malcolm Gladwell and team are 0:00:16.239 --> 0:00:19.479 diving into the transformative world of artificial intelligence with a 0:00:19.520 --> 0:00:24.120 fresh perspective on the concept of open What does open 0:00:24.560 --> 0:00:27.920 really mean in the context of AI. It can mean 0:00:28.000 --> 0:00:31.600 open source code or open data, but it also encompasses 0:00:31.720 --> 0:00:36.040 fostering an ecosystem of ideas, ensuring diverse perspectives are heard, 0:00:36.159 --> 0:00:39.919 and enabling new levels of transparency. Join hosts from your 0:00:39.960 --> 0:00:43.560 favorite pushkin podcasts as they explore how openness and AI 0:00:43.800 --> 0:00:49.040 is reshaping industries, driving innovation, and redefining what's possible. You'll 0:00:49.080 --> 0:00:52.360 hear from industry experts and leaders about the implications and 0:00:52.400 --> 0:00:56.160 possibilities of open AI, and of course, Malcolm Gladwell will 0:00:56.200 --> 0:00:58.200 be there to guide you through the season with his 0:00:58.360 --> 0:01:01.840 unique insights. Look out for new episodes of Smart Talks 0:01:01.920 --> 0:01:05.360 every other week on the iHeartRadio app, Apple Podcasts, or 0:01:05.360 --> 0:01:08.920 wherever you get your podcasts, and learn more at IBM 0:01:09.080 --> 0:01:11.360 dot com slash smart Talks. 0:01:12.520 --> 0:01:15.559 Hey Malcolm Glabell, here, I'm back in your feed today 0:01:15.600 --> 0:01:18.399 because we are re releasing an episode of Smart Talks 0:01:18.440 --> 0:01:23.520 with IBM on a very timely topic, AI governance and 0:01:23.600 --> 0:01:28.360 why regulation is critical to building responsible and accountable AI. 0:01:29.000 --> 0:01:34.840 I hope you enjoy it. Hello, Hello, Welcome to Smart 0:01:34.840 --> 0:01:40.480 Talks with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM. 0:01:40.840 --> 0:01:45.200 I'm Malcolm Glabwell. This season, we're continuing our conversation with 0:01:45.400 --> 0:01:49.800 new creators visionaries who are creatively applying technology in business 0:01:49.840 --> 0:01:53.600 to drive change, but with a focus on the transformative 0:01:53.600 --> 0:01:57.560 power of artificial intelligence and what it means to leverage 0:01:57.600 --> 0:02:02.440 AI as a game changing multiple for your business. Our 0:02:02.440 --> 0:02:07.640 guest today is Christina Montgomery, IBM's Chief Privacy and Trust Officer. 0:02:08.240 --> 0:02:12.240 She's also chair of IBM's AI Ethics Board. In addition 0:02:12.320 --> 0:02:16.680 to overseeing IBM's privacy policy, A core part of Christina's 0:02:16.760 --> 0:02:21.120 job involves AI governance, making sure the way AI is 0:02:21.280 --> 0:02:27.520 used complies with the international legal regulations customized for each industry. 0:02:28.200 --> 0:02:33.399 In today's episode, Christina will explain why businesses need foundational 0:02:33.440 --> 0:02:38.000 principles when it comes to using technology, why AI regulation 0:02:38.320 --> 0:02:42.680 should focus on specific use cases over the technology itself, 0:02:43.160 --> 0:02:47.320 and share a little bit about her landmark congressional testimony. 0:02:47.720 --> 0:02:51.960 Last May, Christina spoke with doctor Lori Santos, host of 0:02:52.000 --> 0:02:56.760 the Pushkin podcast The Happiness Lab, a cognitive scientist and 0:02:56.960 --> 0:03:01.240 psychology professor at Yale University, Laurie is an expert on 0:03:01.480 --> 0:03:06.520 human happiness and cognition. Okay, let's get to the interview. 0:03:09.080 --> 0:03:11.160 So Christina, I'm so excited to talk to you today. 0:03:11.320 --> 0:03:13.560 So let's start by talking a little bit about your 0:03:13.639 --> 0:03:16.320 role at IBM. What does a Chief Privacy and Trust 0:03:16.360 --> 0:03:17.400 Officer actually do. 0:03:18.200 --> 0:03:21.960 It's a really dynamic profession and it's not a new profession, 0:03:22.520 --> 0:03:25.360 but the role has really changed. I mean, my role 0:03:25.480 --> 0:03:29.200 today is broader than just helping to ensure compliance with 0:03:29.360 --> 0:03:33.959 data protection laws globally. I'm also responsible for AI governance 0:03:34.040 --> 0:03:36.840 I co chair or AI Ethics Board here at IBM, 0:03:37.280 --> 0:03:40.360 and for data clearance in data governance as well for 0:03:40.440 --> 0:03:43.960 the company. So I have both a compliance aspect to 0:03:44.000 --> 0:03:46.760 my role, really important on a global basis, but also 0:03:47.280 --> 0:03:52.160 help the business to competitively differentiate, because really trust is 0:03:52.200 --> 0:03:55.880 a strategic advantage for IBM and a competitive differentiator as 0:03:55.920 --> 0:03:59.760 a company that's been responsibly managing the most sensitive data 0:03:59.760 --> 0:04:02.480 for our clients for more than a century now and 0:04:02.600 --> 0:04:05.320 helping to usher new technologies into the world with trust 0:04:05.360 --> 0:04:08.760 and transparency, and so that's also a key aspect of 0:04:08.760 --> 0:04:09.200 my role. 0:04:09.680 --> 0:04:11.880 And so you joined us here on smart Talks back 0:04:11.920 --> 0:04:14.120 in twenty twenty one and you chatted with us about 0:04:14.160 --> 0:04:17.800 IBM's approach of building trust and transparency with AI, and 0:04:17.960 --> 0:04:20.000 that was only two years ago. But it almost feels 0:04:20.000 --> 0:04:22.320 like an eternity has happened in the field of AI 0:04:22.480 --> 0:04:25.520 since then, And so I'm curious how much has changed 0:04:25.560 --> 0:04:28.000 since you were here last time. Were the things you 0:04:28.080 --> 0:04:30.760 told us before, you know, are they still true? How 0:04:30.760 --> 0:04:31.560 are things changing? 0:04:32.240 --> 0:04:35.719 You're absolutely right, it feels like the world has changed 0:04:35.800 --> 0:04:39.520 really in the last two years. But the same fundamental 0:04:39.520 --> 0:04:44.440 principles and the same overall governance applied to IBM's program 0:04:45.040 --> 0:04:49.359 for data protection and responsible AI that we talked about 0:04:49.440 --> 0:04:52.080 two years ago, and not much has changed there from 0:04:52.160 --> 0:04:55.279 our perspective. And the good thing is we've put these 0:04:55.320 --> 0:04:59.840 practices and this governance approach into place, and we've had 0:05:00.080 --> 0:05:03.680 an established way of looking at these emerging technologies. As 0:05:03.720 --> 0:05:07.120 the technology evolves, the tech is more powerful, for sure, 0:05:07.240 --> 0:05:11.320 foundation models are vastly larger and more capable and are 0:05:11.360 --> 0:05:14.760 creating in some respects new issues. But that just makes 0:05:14.800 --> 0:05:16.640 it all the more urgent to do what we've been 0:05:16.680 --> 0:05:20.240 doing and to put trust and transparency into place across 0:05:20.279 --> 0:05:22.920 the business to be accountable to those principles. 0:05:23.880 --> 0:05:26.039 And so our conversation today is really centered around this 0:05:26.160 --> 0:05:29.400 need for new AI regulation and part of that regulation 0:05:29.520 --> 0:05:32.440 involves the mitigation of bias. And this is something I 0:05:32.440 --> 0:05:34.800 think about a ton as a psychologist, right, you know, 0:05:34.839 --> 0:05:37.599 I know, like my students and everyone who's interacting with 0:05:37.640 --> 0:05:40.760 AI is assuming that the kind of knowledge that they're 0:05:40.800 --> 0:05:43.800 getting from this kind of learning is accurate, right, But 0:05:43.880 --> 0:05:45.880 of course AI is only as good as the knowledge 0:05:45.920 --> 0:05:48.200 that's going in. And so talk to me a little 0:05:48.200 --> 0:05:51.479 bit about like why bias occurs in AI and the 0:05:51.560 --> 0:05:53.440 level of the problem that we're really dealing with. 0:05:54.520 --> 0:05:57.560 Yeah, well, obviously AI is based on data, right, It's 0:05:57.920 --> 0:06:02.920 trained with data, and that data could be biased in 0:06:02.960 --> 0:06:05.400 and of itself, and that's where issues could come up. 0:06:05.440 --> 0:06:07.360 They come up in the data, they could also come 0:06:07.440 --> 0:06:10.960 up in the output of the models themselves. So it's 0:06:11.080 --> 0:06:15.799 really important that you build bias consideration and bias testing 0:06:15.960 --> 0:06:19.320 into your product development cycle. And so what we've been 0:06:19.400 --> 0:06:21.919 thinking about here at IBM and doing we had some 0:06:22.000 --> 0:06:24.960 of our research teams delivered some of the very first 0:06:24.960 --> 0:06:28.320 toolkits to help detect bias years ago now right and 0:06:28.360 --> 0:06:31.960 deploy them to open source, and we have put into 0:06:32.040 --> 0:06:35.360 place for our developers here at IBM and Ethics by 0:06:35.400 --> 0:06:38.640 Design playbook that's sort of a step by step approach 0:06:39.360 --> 0:06:45.920 which also addresses very fully biased considerations, and we provide 0:06:45.960 --> 0:06:49.120 not only like here's a point when you should test 0:06:49.200 --> 0:06:51.520 for it and you consider it in the data, you 0:06:51.600 --> 0:06:53.360 have to measure it both at the data and the 0:06:53.360 --> 0:06:56.880 model level or the outcome level, and we provide guidance 0:06:56.920 --> 0:06:59.520 with respect to what tools can best be used to 0:06:59.520 --> 0:07:03.080 accomplish that. So it's a really important issue. It's one 0:07:03.120 --> 0:07:06.160 you can't just talk about. You have to provide essentially 0:07:06.200 --> 0:07:09.480 the technology and the capabilities and the guidance to enable 0:07:09.520 --> 0:07:10.640 people to test for it. 0:07:11.080 --> 0:07:13.800 Recently, you had this wonderful opportunity to head to Congress 0:07:13.800 --> 0:07:16.880 to talk about AI, and in your testimony before Congress 0:07:16.880 --> 0:07:19.880 you mentioned that it's often said that innovation moves too 0:07:19.960 --> 0:07:22.520 fast for government to keep up. And this is something 0:07:22.560 --> 0:07:24.720 that I also worry about as a psychologist. Right our 0:07:24.720 --> 0:07:27.640 policy makers really understanding the issues that they're dealing with, 0:07:28.080 --> 0:07:30.360 and so I'm curious how you're approaching this challenge of 0:07:30.400 --> 0:07:32.800 adapting AI policies to keep up with the sort of 0:07:32.920 --> 0:07:35.400 rapid pace of all the advancements we're seeing in the 0:07:35.400 --> 0:07:36.800 AI technology itself. 0:07:38.040 --> 0:07:41.240 I think it's really critically important that you have foundational 0:07:41.280 --> 0:07:46.760 principles that applied to not only how you use technology, 0:07:46.800 --> 0:07:48.280 but whether you're going to use it in the first 0:07:48.320 --> 0:07:49.920 place and where you're going to use and apply it 0:07:49.960 --> 0:07:54.040 across your company. And then your program from a governance perspective, 0:07:54.120 --> 0:07:56.640 has to be agile. It has to be able to 0:07:56.720 --> 0:08:02.400 address emerging capabilities, new training methods, etc. And part of 0:08:02.440 --> 0:08:07.000 that involves helping to educate and instill and empower a 0:08:07.000 --> 0:08:10.800 trustworthy culture at a company so you can spot those 0:08:10.840 --> 0:08:13.120 issues so you can ask the right questions at the 0:08:13.160 --> 0:08:16.360 right time if you try. We talked about during the 0:08:16.400 --> 0:08:20.640 Senate hearing, and IBM's been talking for years about regulating 0:08:20.960 --> 0:08:24.080 the use, not the technology itself, because if you try 0:08:24.120 --> 0:08:27.880 to regulate technology, you're very quickly going to find out 0:08:28.320 --> 0:08:31.240 regulation will absolutely never keep up with that. 0:08:31.520 --> 0:08:33.720 And so in your testimony to Congress, you also talked 0:08:33.720 --> 0:08:36.959 about this idea of a precision regulation approach for AI. 0:08:37.559 --> 0:08:40.240 Tell me more about this. What is a precision regulation approach, 0:08:40.400 --> 0:08:42.240 and why could that be so important. 0:08:42.400 --> 0:08:45.360 It's funny because I was able to share with Congress 0:08:46.040 --> 0:08:49.800 our precision regulation point of view in twenty twenty three, 0:08:50.160 --> 0:08:53.000 but that precision regulation point of view was published by 0:08:53.080 --> 0:08:57.760 IBM in twenty twenty. So we have not changed our 0:08:57.920 --> 0:09:02.800 position that you should apply the tightest controls, the strictest 0:09:02.840 --> 0:09:07.120 regulatory requirements to the technology where the end use and 0:09:07.240 --> 0:09:11.199 risk of societal harm is the greatest. So that's essentially 0:09:11.200 --> 0:09:14.160 what it is. There's lots of AI technology that's used 0:09:14.160 --> 0:09:17.840 today that doesn't touch people, that's very low risk in nature. 0:09:18.320 --> 0:09:21.760 And even when you think about AI that delivers a 0:09:21.880 --> 0:09:27.320 movie recommendation versus AI that is used to diagnose cancer, right, 0:09:27.360 --> 0:09:30.880 there's very different implications associated with those two uses of 0:09:30.920 --> 0:09:35.719 the technology. And so essentially what precision regulation is is 0:09:35.760 --> 0:09:39.160 apply different rules to different risks, right, more stringent regulation 0:09:39.400 --> 0:09:42.520 to the use cases with the greatest risk. And then 0:09:42.679 --> 0:09:46.839 also we build that out calling for things like transparency. 0:09:47.480 --> 0:09:51.040 You see it today with content right, misinformation and the like. 0:09:51.600 --> 0:09:55.559 We believe that consumers should always know when they're interacting 0:09:55.600 --> 0:09:58.280 with an AI system, So be transparent, don't hyd your 0:09:58.320 --> 0:10:03.240 AI define the risks. So as a country, we need 0:10:03.280 --> 0:10:06.319 to have some clear guidance right and globally as well 0:10:06.679 --> 0:10:10.240 in terms of which uses of AI or higher risk 0:10:10.360 --> 0:10:14.680 where we'll apply higher and stricter regulation and have sort 0:10:14.679 --> 0:10:17.600 of a common understanding of what those high risk uses 0:10:17.640 --> 0:10:21.240 are and then demonstrate the impact in the cases of 0:10:21.280 --> 0:10:26.040 those higher risk uses. So companies who are using AI 0:10:26.200 --> 0:10:30.559 in spaces where they can impact people's legal rights, for example, 0:10:31.160 --> 0:10:35.400 should have to conduct an impact assessment that demonstrates that 0:10:35.480 --> 0:10:38.680 the technology isn't biased. So we've been pretty clear about 0:10:39.080 --> 0:10:42.800 apply the most stringent regulation to the highest risk uses 0:10:42.840 --> 0:10:43.199 of AI. 0:10:44.679 --> 0:10:46.920 And so so far we've been talking about your congressional 0:10:46.960 --> 0:10:49.800 testimony in terms of, you know, the specific content that 0:10:49.840 --> 0:10:52.320 you talked about, But I'm just curious on a personal level, 0:10:52.640 --> 0:10:54.760 you know, what was that like right like right now 0:10:54.760 --> 0:10:57.040 it feels like at a policy level, like there's a 0:10:57.120 --> 0:10:59.440 kind of fever pitch going on with AI right now. 0:10:59.840 --> 0:11:01.600 What did that feel like to kind of really have 0:11:01.679 --> 0:11:03.960 the opportunity to talk to policy makers and sort of 0:11:03.960 --> 0:11:07.000 influence what they're thinking about AI technologies like in the 0:11:07.000 --> 0:11:07.760 coming century. 0:11:07.800 --> 0:11:11.000 Perhaps I was really an honor to be able to 0:11:11.040 --> 0:11:14.040 do that and to be one of the first set 0:11:14.040 --> 0:11:18.160 of invitees to the first hearing, and what I learned 0:11:18.160 --> 0:11:21.320 from it essentially is, you know, really two things. The 0:11:21.360 --> 0:11:25.240 first is really the value of authenticity. So both as 0:11:25.400 --> 0:11:29.360 an individual and as a company, I was able to 0:11:29.440 --> 0:11:31.880 talk about what I do. You know, I did need 0:11:31.920 --> 0:11:35.160 a lot of advanced prep, right. I talked about what 0:11:35.320 --> 0:11:38.800 my job is, what IBM has been putting in place 0:11:38.840 --> 0:11:43.080 for years now. So this isn't about creating something. This 0:11:43.240 --> 0:11:45.840 was just about showing up and being authentic. And we 0:11:45.840 --> 0:11:48.960 were invited for a reason. We were invited because we 0:11:48.960 --> 0:11:52.840 were one of the earliest companies in the AI technology space. 0:11:53.520 --> 0:11:58.640 We're the oldest technology company and we are trusted and 0:11:58.960 --> 0:12:01.360 that's an honor. And then the second thing I came 0:12:01.400 --> 0:12:04.120 away with was really how important this issue is to society. 0:12:04.160 --> 0:12:08.640 I don't think I appreciated it as much until following 0:12:08.760 --> 0:12:13.120 that experience. I had outreached from colleagues I hadn't worked 0:12:13.120 --> 0:12:16.040 with for years. I had an outreach from family members 0:12:16.040 --> 0:12:18.680 who heard me on the radio. You know, my mother 0:12:18.880 --> 0:12:21.679 and my mother in law, and my nieces and nephews 0:12:21.760 --> 0:12:24.160 and my friends of my kids were all like, oh, 0:12:24.160 --> 0:12:26.120 I get it. I get what you do. Now, Wow, 0:12:26.200 --> 0:12:29.400 that's pretty cool, you know So that was really probably 0:12:29.480 --> 0:12:32.200 the best and most impactful takeaway that I had. 0:12:32.480 --> 0:12:36.080 The mass adoption of generative AI, happening at breakneck speed, 0:12:36.480 --> 0:12:40.480 has spurred societies and governments around the world to get 0:12:40.559 --> 0:12:46.880 serious about regulating AI. For businesses, compliance is complex enough already, 0:12:47.040 --> 0:12:50.360 but throw anever involving technology like AI into the mix, 0:12:50.800 --> 0:12:57.040 and compliance itself becomes an exercise in adaptability. As regulators 0:12:57.080 --> 0:13:01.600 seek greater accountability in how AI is used, businesses need 0:13:01.679 --> 0:13:06.600 help creating governance processes comprehensive enough to comply with the law, 0:13:06.840 --> 0:13:09.959 but agile enough to keep up with the rapid rate 0:13:10.000 --> 0:13:15.360 of change in AI development. Regulatory scrutiny isn't the only 0:13:15.440 --> 0:13:20.600 consideration either responsible AI governance. A business's ability to prove 0:13:20.640 --> 0:13:25.440 its AI models are transparent and explainable is also key 0:13:25.520 --> 0:13:30.520 to building trust with customers, regardless of industry. In the 0:13:30.559 --> 0:13:34.800 next part of their conversation, Laurie asked Christina what businesses 0:13:34.840 --> 0:13:40.320 should consider when approaching AI governance. Let's listen, So. 0:13:40.240 --> 0:13:43.600 What's a particular role that businesses are playing in AI governance? Like, 0:13:43.640 --> 0:13:46.120 why is it so critical for businesses to be part 0:13:46.120 --> 0:13:46.400 of this. 0:13:47.240 --> 0:13:51.719 So I think it's really critically important that businesses understand 0:13:52.240 --> 0:13:55.080 the impacts that technology can have, both in making them 0:13:55.120 --> 0:13:58.960 better businesses, but the impacts that those technologies can have 0:13:59.440 --> 0:14:04.559 on the consumers that they are supporting. You know, businesses 0:14:04.679 --> 0:14:09.320 need to be deploying AI technology that is in alignment 0:14:09.480 --> 0:14:11.360 with the goals that they set for it, and that 0:14:11.440 --> 0:14:14.319 can be trusted. I think for us and for our clients, 0:14:14.640 --> 0:14:17.560 a lot of this comes back to trust in tech. 0:14:18.000 --> 0:14:23.720 If you deploy something that doesn't work, that hallucinates, that discriminates, 0:14:24.200 --> 0:14:28.280 that isn't transparent, where decisions can't be explained, then you 0:14:28.440 --> 0:14:32.080 are going to very rapidly erode the trust at best 0:14:32.240 --> 0:14:35.560 right of your clients and at worst for yourself. You're 0:14:35.600 --> 0:14:38.280 going to create legal and regulatory issues for yourself as well. 0:14:38.360 --> 0:14:42.240 So trusted technology is really important, and I think there's 0:14:42.280 --> 0:14:44.480 a lot of pressure on businesses today to move very 0:14:44.560 --> 0:14:47.280 rapidly and adopt technology. But if you do it without 0:14:47.320 --> 0:14:50.840 having a program of governance in place, you're really risking 0:14:51.000 --> 0:14:52.080 eroding that trust. 0:14:52.440 --> 0:14:54.440 And so this is really where I think a strong 0:14:54.560 --> 0:14:57.160 AI governance comes in. You know, talk about from your 0:14:57.200 --> 0:15:00.880 perspective how this really contributes to me containing the trust 0:15:00.880 --> 0:15:03.600 that customers and stakeholders have in these technologies. 0:15:03.840 --> 0:15:06.360 Yeah. Absolutely. I mean you need to have a governance 0:15:06.400 --> 0:15:10.400 program because you need to understand that the technology, particularly 0:15:10.440 --> 0:15:15.000 in the AI space, that you are deploying, is explainable. 0:15:15.080 --> 0:15:19.400 You need to understand why it's making decisions and recommendations 0:15:19.480 --> 0:15:20.920 that it's making, and you need to be able to 0:15:20.960 --> 0:15:23.320 explain that to your consumers. I mean, you can't do 0:15:23.400 --> 0:15:25.480 that if you don't know where your data is coming from, 0:15:25.520 --> 0:15:27.960 what data are you using to train those models, if 0:15:27.960 --> 0:15:31.800 you don't have a program that manages the alignment of 0:15:31.840 --> 0:15:35.400 your AI models over time to make sure as AI 0:15:35.600 --> 0:15:40.000 learns and evolves over uses, which is in large part 0:15:40.640 --> 0:15:44.440 what makes it so beneficial that it stays in alignment 0:15:44.440 --> 0:15:47.920 with the objectives that you set for the technology over time. 0:15:48.560 --> 0:15:52.400 So you can't do that without a robust governance process 0:15:52.440 --> 0:15:55.880 in place. So we work with clients to share our 0:15:55.920 --> 0:15:58.440 own story here at IBM in terms of how we 0:15:58.480 --> 0:16:02.880 put that in place also in our consulting practice to 0:16:03.040 --> 0:16:07.760 help clients work with these new generative capabilities and foundation 0:16:07.880 --> 0:16:10.400 models and the like in order to put them to 0:16:10.440 --> 0:16:12.440 work for their business in a way that's going to 0:16:12.480 --> 0:16:15.360 be impactful to that business, but at the same time 0:16:15.520 --> 0:16:16.120 be trusted. 0:16:16.320 --> 0:16:18.120 So now I wanted to turn a little bit towards 0:16:18.160 --> 0:16:22.040 Watson x governance, and so IBM recently announced their AI platform, 0:16:22.080 --> 0:16:25.800 Watson X, which will include a governance component. Could you 0:16:25.800 --> 0:16:28.440 tell us a little more about watsonx dot governance. 0:16:29.040 --> 0:16:31.280 Yeah, I mean before I do that, I'll just back 0:16:31.360 --> 0:16:35.000 up and talk about the full platform and then lean 0:16:35.040 --> 0:16:37.880 into Watson X because I think it's important to understand 0:16:38.160 --> 0:16:44.120 the delivery of a full suite of capabilities to get data, 0:16:44.200 --> 0:16:47.080 to train models, and then to govern them over their 0:16:47.120 --> 0:16:52.600 life cycle. All of these things are really important. From 0:16:52.680 --> 0:16:55.560 the onset you need to make sure that you have. 0:16:56.480 --> 0:17:01.040 For our watsonex dot AI for example, that's the studio 0:17:01.120 --> 0:17:05.560 to train new foundation models and generative AI and machine 0:17:05.600 --> 0:17:11.399 learning capabilities. And we are populating that studio with some 0:17:11.880 --> 0:17:17.400 IBM trained foundation models which we're curating and tailoring more 0:17:17.440 --> 0:17:20.680 specifically for enterprises. So that's really important. It comes back 0:17:20.720 --> 0:17:23.800 to the point I made earlier about business trust and 0:17:23.920 --> 0:17:30.320 the need to have enterprise ready technologies in the AI space. 0:17:30.640 --> 0:17:34.199 And then the watsonex dot data is a fit for 0:17:34.280 --> 0:17:37.800 purpose data store or a data Lake and then watsonex 0:17:37.840 --> 0:17:42.520 dot gov. So that's a particular component of the platform 0:17:42.920 --> 0:17:46.679 that my team and the AI Ethics Board has really 0:17:46.720 --> 0:17:49.920 worked closely with the product team on developing, and we're 0:17:50.119 --> 0:17:52.919 using it internally here in the Chief Privacy Office as 0:17:52.960 --> 0:17:57.359 well to help us govern our own uses of AI 0:17:57.480 --> 0:18:03.440 technology and our compliance program here, and it essentially helps 0:18:03.480 --> 0:18:08.120 to notify you if a model becomes biased or gets 0:18:08.160 --> 0:18:11.280 out of alignment as you're using it over time. So 0:18:11.359 --> 0:18:14.000 companies are going to need these capabilities. I mean they 0:18:14.040 --> 0:18:18.240 need them today to deliver technologies with trust. They'll need 0:18:18.280 --> 0:18:21.960 them tomorrow to comply with regulation which is on the horizon. 0:18:22.040 --> 0:18:24.840 And I think compliance becomes even more complex when you 0:18:24.920 --> 0:18:29.040 consider international data protection laws and regulations. Honestly, I don't 0:18:29.040 --> 0:18:31.640 know how anyone on any company's legal team is keeping 0:18:31.680 --> 0:18:33.959 up with us these days. But my question for you 0:18:34.040 --> 0:18:37.680 is really how can businesses develop a strategy to maintain 0:18:37.720 --> 0:18:40.720 compliance and to deal with it in this ever changing landscape. 0:18:40.800 --> 0:18:44.800 It's increasingly more challenging. In fact, I saw statistic just 0:18:44.840 --> 0:18:49.440 this morning that the regulatory obligations on companies have increased 0:18:49.480 --> 0:18:53.119 something like seven hundred times in the last twenty years. 0:18:53.160 --> 0:18:57.760 So it really is a huge focus area for companies. 0:18:57.920 --> 0:19:00.760 You have to have a process in place in order 0:19:00.840 --> 0:19:03.399 to do that, and it's not easy, particularly for a 0:19:03.400 --> 0:19:07.280 company like IBM that it has a presence in over 0:19:07.320 --> 0:19:10.320 one hundred and seventy countries around the world. There is 0:19:10.359 --> 0:19:15.240 more than one hundred and fifty comprehensive privacy regulations, there 0:19:15.280 --> 0:19:19.800 are regulations of non personal data, there are AI regulations emerging, 0:19:20.800 --> 0:19:24.840 So you really need an operational approach to it in 0:19:24.960 --> 0:19:27.000 order to stay compliant. But one of the things we 0:19:27.040 --> 0:19:29.199 do is we set a baseline. And a lot of 0:19:29.200 --> 0:19:32.679 companies do this as well. So we define a privacy baseline, 0:19:32.720 --> 0:19:37.520 we define an AI baseline, and we ensure then as 0:19:37.520 --> 0:19:39.960 a result of that that there are very few deviances 0:19:40.000 --> 0:19:43.080 because it incorporates in that baseline. So that's one of 0:19:43.119 --> 0:19:45.320 the ways we do it. Other companies, I think are 0:19:45.359 --> 0:19:50.560 similarly situated in terms of doing that. But again, it 0:19:51.160 --> 0:19:53.399 is a real challenge for global companies. It's one of 0:19:53.440 --> 0:19:57.400 the reasons why we advocate for as much alignment as 0:19:57.440 --> 0:20:02.840 possible on the international realm as well as nationally here 0:20:02.840 --> 0:20:06.640 in the US, as much alignment as possible to make 0:20:06.880 --> 0:20:11.800 compliance easier for easier and not just because companies want 0:20:11.840 --> 0:20:15.000 an easy way to comply. But the harder it is, 0:20:15.280 --> 0:20:19.159 the less likely there will be compliance. And it's not 0:20:19.240 --> 0:20:25.320 the objective of anybody, governments, companies, consumers to have to 0:20:25.520 --> 0:20:29.040 set legal obligations that companies simply can't meet. 0:20:29.480 --> 0:20:31.479 So what advice would you give to other companies who 0:20:31.520 --> 0:20:34.480 are looking to rethink or strengthen their approach to AI government. 0:20:34.520 --> 0:20:37.760 Think you need to start with, as we did, foundational principles, 0:20:38.400 --> 0:20:41.840 and you need to start making decisions about what technology 0:20:41.880 --> 0:20:44.199 you're going to deploy and what technology you're not, What 0:20:44.240 --> 0:20:45.320 are you going to use it for, and what aren't 0:20:45.359 --> 0:20:46.720 you going to use it for? And then when you 0:20:46.760 --> 0:20:51.080 do use it, align to those principles. That's really important. 0:20:51.200 --> 0:20:55.720 Formalize a program, have someone within the organization, whether it's 0:20:55.760 --> 0:21:00.400 the chief privacy officer, whether it's some other role, chief 0:21:00.440 --> 0:21:06.040 AI ethics officer, but have an accountable individual and accountable organization. 0:21:06.720 --> 0:21:09.159 Do a maturity assessment, figure out where you are and 0:21:09.160 --> 0:21:12.200 where you need to be, and really start, you know, 0:21:12.720 --> 0:21:17.199 putting it into place today. Don't wait for regulation to 0:21:17.280 --> 0:21:19.960 apply directly to your business because it'll be too late. 0:21:20.920 --> 0:21:24.280 So Smart Talks features new creators these visionaries like yourself 0:21:24.320 --> 0:21:27.840 who are creatively applying technology in business to drive change. 0:21:28.080 --> 0:21:30.720 I'm curious if you see yourself as creative. 0:21:31.200 --> 0:21:34.520 You know, I definitely do. I mean, you need to 0:21:34.600 --> 0:21:39.240 be creative when you're working in an industry that evolves 0:21:39.320 --> 0:21:43.800 so very quickly. So you know, I started with IBM 0:21:44.040 --> 0:21:47.160 when we were primarily a hardware company, right, and we've 0:21:47.280 --> 0:21:50.679 changed our business so significantly over the years. And the 0:21:50.760 --> 0:21:55.240 issues that are raised with respect to each new technology, 0:21:55.240 --> 0:21:58.880 whether it be cloud, whether it be AI now where 0:21:58.920 --> 0:22:00.479 we're seeing a ton of issues, or you look at 0:22:00.480 --> 0:22:04.639 emergent issues in the space of things like neurotechnologies and 0:22:04.720 --> 0:22:11.320 quantum computers. You have to be strategic and you have 0:22:11.440 --> 0:22:14.720 to be creative and thinking about how you can adapt 0:22:15.320 --> 0:22:20.560 agilely quickly a company to an environment that is changing 0:22:20.600 --> 0:22:22.360 so quickly and. 0:22:22.359 --> 0:22:25.399 With this transformation happening at such a rapid pace. Do 0:22:25.440 --> 0:22:27.520 you think creativity plays a role in how you think 0:22:27.520 --> 0:22:30.840 about and implement, specifically a trustworthy AI strategy. 0:22:33.320 --> 0:22:37.359 Yeah, I absolutely think it does, because again, it comes 0:22:37.400 --> 0:22:40.560 back to these capabilities, and there are ways, I guess 0:22:40.760 --> 0:22:44.520 how you define creativity could be different, right, But I'm 0:22:44.560 --> 0:22:47.760 thinking of creativity in the sense of sort of agility 0:22:47.800 --> 0:22:51.920 and strategic vision and creative problem solving. I think that's 0:22:52.160 --> 0:22:55.280 really important in the world that we're in right now, 0:22:55.320 --> 0:22:59.800 being able to creatively problem solve with new issues that 0:22:59.880 --> 0:23:03.080 are rising sort of every day. 0:23:03.440 --> 0:23:05.000 And so, how do you see the role of chief 0:23:05.040 --> 0:23:08.680 privacy officer evolving in the future as AI technology continues 0:23:08.680 --> 0:23:11.600 to advance, Like what stuff should CPOs take to stay 0:23:11.600 --> 0:23:13.520 ahead of all these changes that are come in their way? 0:23:15.080 --> 0:23:18.960 So the role is evolving in most companies, I would 0:23:19.040 --> 0:23:23.960 say pretty rapidly. Many companies are looking to chief privacy 0:23:24.000 --> 0:23:27.560 officers who are ready understand the data that's being used 0:23:27.560 --> 0:23:31.040 in the organization and have programs to ensure compliance with 0:23:31.160 --> 0:23:34.960 laws that require you to manage that data in accordance 0:23:35.000 --> 0:23:37.600 with data protection laws and the like. It's a natural 0:23:37.640 --> 0:23:43.640 place and position for AI responsibility. And so I think 0:23:43.680 --> 0:23:46.320 what's happening to a lot of chief privacy officers is 0:23:46.359 --> 0:23:49.880 they're being asked to take on this AI governance responsibility 0:23:49.880 --> 0:23:53.399 for companies and if not take it on, at least 0:23:53.440 --> 0:23:56.879 play a very key role working with other parts of 0:23:56.920 --> 0:24:00.520 the business in AI governance. So that really is changing. 0:24:00.760 --> 0:24:04.840 And if chief privacy officers are in companies who maybe 0:24:04.840 --> 0:24:08.639 haven't started thinking about AI yet, they should, so I 0:24:08.640 --> 0:24:12.520 would encourage them to look at different resources that are 0:24:12.520 --> 0:24:16.879 available already in AI governance space. For example, the International 0:24:16.960 --> 0:24:21.360