Episode Categories:
Resources
Join the discussion: Ask Jordan and Diya questions on AI
Related Episodes: Ep 323: How AI Is Changing Workplace Productivity
Ep 302: 5 Laws for Success in the AI Era
Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Connect with Jordan Wilson: LinkedIn Profile
Try Our Free AI Prompting Course: Register for our free Prime, Prompt, and Polish AI Course!
The Future of AI: Responsible Implementation and Upskilling the Workforce
The role of emerging technologies in shaping societies can be profound, particularly in accessibility and opportunities for populations that may otherwise be underserved. It's indicative that disparities in access can largely be a factor of resource allocation. An interesting example is healthcare, where those dependent on social health insurance may not be privy to certain services, leading to a relative divide.
One of the promising technologies stepping up and striving to level the playing field is Artificial Intelligence (AI). With use cases ranging from education and banking services to hiring processes and video production, AI is making inroads everywhere. Furthermore, AI can be pivotal in system testing for fairness and bias, thereby taking significant strides towards a more equitable world.
Driving Efficiency with AI
AI's potential productivity and efficiency gains are not a point of discussion anymore, they are proven facts. Businesses are already leveraging AI for a host of applications, revamping their employee roles, and redefining their market positions. Responsible AI implementation is key as the implications of AI functionalities and their affect on employees have to be monitored meticulously.
There is considerable emphasis on understanding how AI impacts companies and employees, a conversation that is critical to having a responsible AI strategy in place. Just like every form of advancement, AI too has a dual effect -- on one hand, there could be job displacement, but on the other, a significant number of new roles could emerge.
Riding the Wave of Change: Upskilling and Reskilling
The shift brought in by AI is no different from the cloud movement that revolutionized the IT sector. It is a transformation that requires adaptation and continual learning. The emphasis is on education, training, and skills development to keep pace with this fast-evolving landscape. The shift towards skills-based education is a prerequisite to meet the demands of technological advancements. The traditional paradigms need to be rethought and reframed to accommodate the changes brought about by AI.
Harnessing the Power of AI: Maximizing Human Potential
AI is not just about automating tasks, it is a tool to augment human capabilities, reduce work hours, and boost creativity. However, like all tools, responsible usage is critical. It is essential to consider biases, promoting fairness, and addressing concerns about development and advancement to ensure AI is used to enrich human capabilities.
Education, upskilling, and reskilling are crucial in preparing the workforce for new opportunities and changes brought about by technological advancements.
Responsibility in AI: A Shared Task
The organizational structure plays a critical role in implementation and oversight of AI. Integrating people, technology, and processes is the best way to maximize benefits and reduce risks associated with AI. Establishing responsible management with clear leadership commitment can provide a strong foundation for productive AI applications. Public and internal communication of AI strategy, too, is significant for ensuring shared responsibility and transparency.
Technological advancements are undoubtedly powerful tools that can shape societies and economies alike. As we step deeper into an era heavily shaped by AI, it is crucial for businesses, governments and individuals to imbibe practices that drive an equitable society. It rests in the collective hands of society to harness the transformative potential of AI, while also continually preparing for a better future through education, reskilling, and responsible usage.
Open dialogue and sharing of experiences are necessary to ensure that we all progress toward a future that leverages AI responsibly and equitably. An opportunity, no doubt, exciting partner in our journey towards a more enriched and equitable future.
Topics Covered in This Episode
1. Allocation of resources and the role of AI
2. Responsible and intentional AI practices
3. Complexities of upskilling and reskilling
4. Implications of AI on a grand scale
Podcast Transcript
Jordan Wilson [00:00:16]:
What happens when AI works? Right? We spend so much time and and thought and energy into implementing generative AI and large language models into our companies, chasing these these golden claims of being more productive, 30%, 50%, 70%. But then what happens when we see those productivity gains gains or those efficiency gains? What happens with the humans? Do we sit around and wait for a new project? Do we start working in other departments? I don't know those things, but it's questions that I think about all the time. Luckily, on today's episode of everyday AI, we have an expert joining us from AWS who that's her role. And, I think she is one of the smartest people out there when it comes to responsible AI. So we're gonna be tackling that together today on Everyday AI. What's What's going on y'all? My name is Jordan Wilson, and I'm the host. And everyday AI, it's for you. It's a daily livestream podcast and free daily newsletter, helping us all learn and leverage generative AI to grow our companies and to grow our careers.
Jordan Wilson [00:01:21]:
So if that's you, maybe you're listening for the first time. Thank you for joining us. If you're on the podcast, make sure to check out your show notes for a link to our website, a link to the livestream where you can come in and ask questions after the fact. So make sure to go to your everyday ai.com. Sign up for that free daily newsletter. We will be recapping today's interview and a lot more in our newsletter that goes out as well as make sure to check out that thanks a million giveaway, campaign we have going on celebrating a 1000000 downloads here at everydayai. Alright. Before we get into it, let's quickly tackle what's going on in the world of AI news.
Jordan Wilson [00:01:53]:
So a federal judge has allowed a key copyright claim against AI image developers to proceed. So a federal judge has advanced the copyright infringement case against AI image developers like Stability AI, Midjourney, and the online art community DeviantArt, marking a significant step in a high profile legal battle. So US district judge William Oryk has allowed the copyright and trademark infringement claims to move forward while dismissing claims under the Digital Millennium Copyright Act, DMCA, and unjust enrichment. So the lawsuit was filed by artists Sarah Anderson, Kelly McKernan, and, Karla Ortiz who alleged their work was used without permission to train AI models. Stability AI, DeviantArt, Runway, Midjourneys, and others have yet to comment on the ongoing case. So pretty pretty big deal there that this, a judge, a federal judge, is allowing this case to move forward. Alright. Next, a generative AI usage in math prep, study is showing that using AI, generative AI, in math prep is actually leading to lower exam scores.
Jordan Wilson [00:03:01]:
So a new study from the Wharton School reveals that high school students using generative AI for math exam preparation perform actually worse on actual tests compared to those who didn't use the tools while prepping. So here's the significance. You know, AI optimists, you know, are obviously envisioning a personal tutor for every student, but the study highlights that that might not be the case as there's challenges and potential drawbacks for AI driven learning. So some schools have banned generative AI tools and large language models, while others are really pushing them and permitting their use with disclosure. So as an example, Khan Academy's, Sol Khan piloted a generative AI tutor last year, called KhanMigo, super popular in the educational system, aiming to help students solve problems rather than just providing answers. So, yeah, that one's an interesting one, and we'll be keeping an eye on that. Last but not least, World Labs has reached unicorn status in just 4 months. So World Labs, a startup founded by Stanford AI professor, Fifi Lee, has achieved a valuation of over $1,000,000,000 within 4 months of its founding.
Jordan Wilson [00:04:12]:
That's wild, y'all. So, Fifi Lee has been given the name the godmother of AI. So according to a report from TechCrunch, the latest financing route, led by by NEA raised a $100,000,000 and significantly increased the company's valuation from $200,000,000 in April to now $1,000,000,000. So the startup aims to develop AI models capable of accurately estimating the 3 dimensional the 3 dimensional physicality of real world objects and environments, potentially revolutionizing industries like gaming and robotics. Phoebe Lee is often referred to as the godmother of AI. She highlighted the importance of developing machines with human like spatial intelligence in a TED Talk earlier this year. According to an investor familiar with World Labs, the company's approach could reduce the need for extensive and expensive data collection, which is currently a significant hurdle for many AI applications. Alright.
Jordan Wilson [00:05:09]:
We're gonna be having a lot more on those stories inside of our newsletter, so make sure you go check this out. But, let's talk now about the topic for today, which is one I'm constantly thinking about because if I'm telling you the truth, I'm very lucky. I get to talk to the smartest people in the world, and this is one of those issues that I think is hard to tackle. Right? What happens when AI works? How can you responsibly, you know, not just roll it out in your organization, but responsibly handle everything that comes with it? So I'm not by myself today. Luckily, I have a guest. So, I'm excited for this one. Please help me. Welcome to the show.
Jordan Wilson [00:05:44]:
There we go. We have her, Dia Wynne, who is the responsible lead at AWS, Amazon Web Services. Dia, thank you so much for joining the Everyday AI Show.
Diya Wynn [00:05:54]:
Thank you for having me, Jordan. It's good to see you.
Jordan Wilson [00:05:57]:
Oh, I'm excited for this one, y'all. I've been wanting to get, Dia on the show for a hot minute or a hot couple of months. Dia, can you just tell everyone a little bit about what your role entails at AWS as the responsible AI lead?
Diya Wynn [00:06:10]:
Sure. Well, I'll tell you all something interesting. It actually is just a couple of weeks ago. I am in a new role, so just moved over into responsible AI policy. But, which which will which will look a little different in that, you know, I get to work with folks that are, in legislation or working on legislation around responsible AI. So it's an expansion of the work that I was doing previously. But, you know, I I had the opportunity to start our customer facing work on responsible AI. So, essentially, one of our first internal practices solely focused on thinking about the risks and applications of AI and doing what we do well at AWS in terms of helping guide our customers and support them, partner with them as they develop and build on top of our services.
Diya Wynn [00:06:58]:
And we use the very similar model to what we do with, like, the cloud in that, we provide a structure to be able to identify best practices, support them in terms of understanding and growing in their ability to be able to leverage the cloud well and maximize their benefit? Well, we use that same sort of model in in terms of thinking about responsible AI. How do we support them given the 100 or maybe even 1,000 of, a 1,000, data scientists that we have internally throughout the business, working in various areas, the folks that we have in policy, the work that we're doing in standards, you know, the experience that we have working with our customers who are building, as well as building for, you know, our platform or our, company Amazon, all bringing all that to bear, as well as the ongoing research that we've been doing to be able to help them think through the areas of risk and put in place kind of best practices that would help them address those risks, minimizing the area potential for impact or negative impact and maximize the benefit and, hopefully, the benefit to not just their bottom line, but to to all.
Jordan Wilson [00:08:07]:
And and and, dear, I I really do wanna get back to what your role looks like a little bit more at AWS because I'm curious, and I think that, you know, our listeners can probably gain a lot from from understanding how a huge company like AWS handles responsible AI. But I actually just wanna take a second and fast forward to the end and ask the big questions. So, you know, what happens when AI works? What happens when, you know, companies are maybe finally realizing those 20%, 30%, 50%, you know, increases in productivity and, efficiency? What happens then? What happens with, you know, employees that maybe have way more on their plate than they or sorry, way less on their plate than they did before?
Diya Wynn [00:08:49]:
Yeah. Well, no. I actually and I and I said this to you. I love the I love the topic because, you know, oftentimes in my work, I'm I'm focused on, like, the risk and helping people understand the risk and understand the unintended impact, ultimately, in order to see the good when it works. But but I have the opportunity to look at this, much more critically from an area of risk and elevating those risks as well. So when it works, I think it's it's ultimately, you know, a a bridge, not a barrier for people to be able to reach their full potential and, for us to drive towards more equitable outcomes with the technology and build a more equitable world. Right? That's that's sort of my envisioning of what this looks like, you know, when AI works. And that means that, we have, you know, systems that are not necessarily replacing people, but are working and augmenting human capabilities so that we can be our best versions of ourselves.
Diya Wynn [00:09:45]:
As you mentioned, when we are, you know, I I just understand and know that there are many of us that are overworked, and and but but have systems to benefit, you know, and and to leverage in terms of, you know, being able to do everyday sort of tasks, but also allow us the space to be able to you remember that there was a book that was like the, what was it? The I think the the what was it? It wasn't the 4 hour work work day or so. The 4:4 week 4 day work week or something like that. I forget the book. Someone bought it for me and clearly by me forgetting the title, you know, that I haven't mastered that. But the idea was that you could do 4 hour work week.
Jordan Wilson [00:10:27]:
You were 4 hour work week.
Diya Wynn [00:10:28]:
Okay. So that's what it was. Right? But, but the idea is like, imagine actually being a little more of us to being able to live into that and, and be less stressed and to be able to maximize our creativity and capability. Imagine a world where we have, you know, folks that, you know, typically would be underserved and and marginalized and underrepresented, actually lowering various entries so that they can, you know, be part of and included in systems. Right? I think, you know, systems and technology that address some of the, biases and promote fairness and decision making processes is whether that's in hiring or criminal justice or where resources are allocated. When it works, it's, you know, we have the right kind of inclusion in place so that all are being considered kind of the same ways that we thought about, in earlier days of thinking about disability and ensuring that we're building, you know, for accessibility that benefits us all. Right? All of that, I think, is, when AI works and when we're doing the intentional things, to, minimize the areas of risk and impact or the potential impacts that the systems can have and that we're we're all very familiar about and concerned about in terms of the, you know, its development and advancement.
Jordan Wilson [00:11:52]:
Yeah. And and, Dale, I'm I'm I'm also curious. What about on the flip side? Right? Because you just painted out the, utopian side of the coin. Right? When we have the more, you know, 4 hour work week and we have the ability to to work on, you know, in driving equitable outcomes. And, yeah, that's a 100% possible in a future with generative AI. But what about the more dystopian side of that coin when we say what happens when AI works? Because, you know, a lot of companies, you know, what they're doing is, you know, we saw it from, you know, I think IBM, Intel, you know, laying off a lot of employees and saying, hey. We're gonna focus on AI. It's gonna drive efficiency.
Jordan Wilson [00:12:27]:
So what about the more dystopian side of that coin of what happens when AI works? Because a lot of people are scared of that when AI works.
Diya Wynn [00:12:36]:
Right. Right. So so it's interesting. Right? Because I looked at this. What happens when it works, when we're doing the things, right, to build into, and when responsible AI, the area of focus that I have, is part of the way that we work. So because part of my envision or part and those that are in spaces like mine, or roles like mine, think about responsible AI being the way in which we build, that we ultimately want to move to a place where we think about like, and are intentional about who we include and whether there's value alignment and what we're doing from transparency and fairness. That is the world when in my mind where AI works, where we're building with that kind of intentionality, you know, across the life cycle. So what you're talking about, so when it works, the, the reverse of this when it works, right, and the ways that we're envisioning being able to do new things, have greater efficiencies and, you know, businesses are looking at what if I have greater efficiencies and I can, I need less people or I need, you know, I need, or or we we we spend less in certain areas? That means that we have the opportunity to be able to be optimized and then can invest in other other things.
Diya Wynn [00:13:47]:
I think that what what we talk about when when we work with companies, and even internally is is 1, you know, the focus on human beings. Right? And and not necessarily looking to replace human beings, but that we're augmenting capability that we look for opportunity for humans to work with systems and technology, to be more efficient, to remove the undifferentiated heavy lifting kind of the way we talk about the cloud as well. And then we get to sort of optimize or be focused in the areas that really bring out our unique value. But that again, doesn't happen without intentionality. So that means that we have to be thinking about the ways in which, if we're replacing work or that people are, we're not working with systems that we actually have to focus on the kind of education, upskilling and reskilling that is necessary, to be able to one, help people understand how their work shifts as a result of that, and then prepare people for pathways to be able to move into new opportunities and new areas of work or new roles that are being created that weren't previously existed. All of that has to be part of the intentional focus that that, companies, you know, embrace the technology with this sort of mindset, this organizational change, this structure in place to be considering that. And I think it's not just a responsibility of, you know, technology companies or those that are employing, AI, but that's also, you know, part of what we expect in government as we look out and NGOs and others that are looking at workforce programs that we're thinking about that as well. Because with any technological advancement, any major era we see an e shift from the kind of work that we had, right, to the work that we're now doing today.
Diya Wynn [00:15:31]:
I heard someone mention this the other day, and they likened this move with, with AI and generative AI being similar to like the way that automobiles changed the face of everything, right? Going from, you know, horse and buggy to having, like, automobiles that changed our supply chains, that changed, you know, how we, get out that mail that changed the work that we do, that changed industry, right? It wasn't just that one area. It trains how we, gather with individuals, right? Like all of this was a massive adjustment and it did mean that we didn't have people that were driving horse and, and buggy or, or like the elevators of all that we don't have people that are helping us, you know, that were elevator operators. They're now different responsibilities that we have. And we have to understand that with all of that technological change, there will be differences in terms of the work, but then we have to prepare people that will, for the work. The World Economic Forum said that I think it was what, 70,000,000 jobs would be displaced, but 80,000,000 would be created. And it's not necessarily to say that the people that were in the 70 automatically moved to the 80, which is why that intentionality around reskilling and upskilling is essential, right, to be able to hopefully create pathways for people to be able to move into the work of the new, and to the new opportunities that we're seeing, in the future.
Jordan Wilson [00:16:54]:
Yeah. And I think that's that's what I think when we talk or at least when I think about responsible AI in the future, that's one of the places my mind ultimately goes. And and, hey, as a reminder for our for our livestream audience, if you have a question, for Dia, you know, it's not every day you can ask a question for the responsible AI lead at Amazon Web Services. Look at your questions in now. But let's focus on that a a little bit here, Dia, in this concept of, yes, there's going to be, I think, massive job loss, but there's also gonna be massive job creation. Right? You just mentioned the study there from the World Economic Forum, that 70,000,000 jobs would be impacted, but potentially 80,000,000 jobs created. So, you know, how are you even approaching this at AWS, and how should companies be looking at this process of upskilling and reskilling? Because from my perspective, I think it's personally hard because in some of these instances, it might not be a a a one degree or a 5 degree shift in terms of where you might be upscaling and reskilling someone too, but it could be a 90 degree or a 180 degree change. How can companies be doing this responsibly?
Diya Wynn [00:19:08]:
Yeah. I think I think that that is a great point. And, you know, I'd say this, like, upscaling, resale scaling, like, it's easy and it's not. And I even remember, when we were having conversations again, I liken this to the cloud, right? We talked about like the cloud shifting the work. We have people in data centers that were focused on, managing infrastructure, right, that now perhaps we're going to have a different responsibility and wasn't going to be the same when we have systems in the cloud and not, necessarily in the data center that they were physically responsible for. And that was a shift of work. And we had folks that were willing to make that shift and others that were not right. And so that's part of some of the complication with this as well.
Diya Wynn [00:19:50]:
There's going to be a degree of resistance. There will be some degree of failure, all of what's to have to be worked through in order for us to shape and reshape like the opportunity for people in the future. But I think that's part of the work. And again, not just for those that are intact or those that are, you know, building products, but also for sort of community organizations, other institutions, as well as government to have a role in the place that I play in. But like, what are, what are the things that we do? I think one of the things is one educating people, on, you know, the value that the organization is placing in AI and what their commitment is in terms of using AI and being transparent about their policy as far as responsible development. I think that's one thing that's critical. And we were talking about this, in, in the way that we think about some of the most organizations have compliance training that happens every year, right? Like you have to understand security and then we need to understand like data classification and the way that data is used. And we need to other we'll also do some other sort of, tests like a requirement required test to understand, like what is sexual harassment, etcetera.
Diya Wynn [00:20:58]:
Like, those kinds of annual compliance required training could be an opportunity. And we've we're certainly seeing some that are using that model to, to roll out, like, awareness and commitment to and understanding of our use of AI, where it gets applied, what our commitments to to to the development of that is. We we started last year through Amazon wide, initiative, under our Inclusive Tech team, like actually training sort of broadly on response on AI and responsible AI. And then making sure that we also have like role based or AI education and training that is aligned to people at different stages of their life cycle. So what is necessary and essential for someone to understand if they're building and responsible, like, from a data perspective, their data scientists or an engineer is different than what we might want, you know, someone to consider as a product manager or an IT. And so having role based or or, that kind of training that's aligned across the board. And then we've done things to make, AI education sort of broadly available to, to our employees as well as externally. So, our our, skill builder platform is one of the learning and development platforms that is also open to the public.
Diya Wynn [00:22:19]:
And on that platform, we have a lot of low costs as well as free training and education that will provide AI education, to, to folks like thinking about it and strategy and business, as well as, you know, specific skills in terms of those that are looking at Python development or you know, using one of our services. And so that's made available to our resources internally as well. And so I think that we're thinking about, you know, critically thinking about, like, what does that mean as we look at teams? And that has to be ongoing, or look at leveraging and adopting the technology throughout our organization. But that also has to be has to be an ongoing work. Right? Because what we're looking at today, in terms of roles and where AI is being used will very likely be a little different than in tomorrow. And and having that sort of constant iteration, and reviews that we can make the adjustments necessary, pivot, and, you know, meet the demand if necessary. And one other thing I'll talk about this in terms of skill, and like reskilling. I think it's important important as well in education context, right, that we are making the shift.
Diya Wynn [00:23:27]:
Right? There was a study, I believe IBM was one of the recent ones that talked about the, half half life of skills. And in the last sort of evaluation, there was this notion that the half life of skills was somewhere around the, around 2 years. I would venture to say that that is decreasing as well with the rapid advancement of technology, which means that we have to be ongoing and constant learners. And then that requires as well, the way that we look at skills and learning to be different, both in sort of broader education context, like traditional education, as well as other programming, because we need to be able to meet the demand as we are shifting with technology. Now, new skills are required and what other way that we look at education has to shift to be able to more of a skills based sort of, understanding. And we've been talking about that for a while, but I think now with what's happening with AI and the shift in roles and the shift in work, right? This is elevating that need and demand, but also means that we have to shift the way that we look for people skills and resources as well, in our companies, right? Not just, do you have a 4 year degree and while there's necessity for specialization, right? Because AI can do some of the lower level things and now we need greater specialization. I think that there is still also a need to look at this skills shift that's required and looking at transferable skills and where we can get skills attainment and other, opportunities outside of traditional school and elevating that as a means for bringing in resources, that are necessary, you know, throughout the organization, all of that full ecosystem shift I think is necessary and all of it hasn't progressed, as quickly as the technology is. So we're in some ways we're playing catch up to, to shift our paradigm.
Diya Wynn [00:25:18]:
And I think near around this.
Jordan Wilson [00:25:20]:
Yeah. Dee, I think that's a great point. The the concept of the half life of skills or when skills start to use their value because, yeah, historically, it's been going down. Right? And it's at the lowest point ever, which I think is why, you know, some of those points that you just made about, you know, companies providing education, ongoing training, etcetera, is extremely important. This question from Cecilia here, I think is great. And I'm going to add on to it as well. So she's asking, how are you communicating the concept of responsible AI within AWS? But then I'll also add on top of that, Dia, how are you not just at communicating, responsible AI, but also how are you communicating, upskilling and reskilling? Because I think how companies talk about this, right, especially when they're implementing AI for the first time on a large scale is extremely important. So how are you doing those things?
Diya Wynn [00:26:08]:
Right. So so I think well, it's not first of all, let me just say it's not just me. So there are a ton of people, fortunately, that have responsibility, you know, for like a responsible AI, how we're building and developing our services. One of the things I think that is primary is when thinking about this as like an organizational structure, responsible AI, isn't just set of principles or, or tenants that that are being followed, but it is, you know, integral into the way in which we develop, build, and we think about AI. So I think that's one of the key things that this is an organizational structure. When I talk about responsible AI, it's a it's an organizational structure that establishes a culture of responsibility that helps us to, incorporate people process and technology, to be able to reduce risk, unintended impact and maximize benefit. And so when we think of it that way, then there are, or, you know, organizational change mechanisms that we put in place in order to help, you know, increase understanding and awareness, but also bring people along that journey. Then we also have to have strategic alignment in terms of our leadership, Right.
Diya Wynn [00:27:07]:
And our leadership is a huge part in terms of driving the commitment to responsible AI, not just in word, but also in action throughout the organization. So, so I think that's part of it. We have a responsible AI strategy, right. That informs and drives what we do in terms of our commitment to our customers, as well as in development of our services and the commitment long term to how we are investing in the next generation of technologists of engineers that are going to diverse, help shape the technology, as well as the ongoing research in terms of responsible and ethical development of AI. All of that strategy underpins how we are, one communicating about that internally. And, and, and then we have things like we, because it's a huge organization, we have to have like internal road shows. We have, you know, talks that we'll discuss that we have training, you know, that incorporates it. We are talking about that in our organization, in team meetings and, on, you know, main stages and our off sites as well as in, you know, broader venues.
Diya Wynn [00:28:10]:
And you all are starting to see that as well, in, you know, in some of our, you know, major events like Reinforce and Reinvent Reinforce is our security event, Reinvent our annual customer conference. Like, those are things that are on the main stage reflecting our ongoing commitment to responsible AI, talking about it so that others are awareness. And then, I mean, fortunately I and a number of others get to do public things where we engage with, the community publics, you know, industry events. Talking about responsible AI. Right, that also helps increase awareness, not only internally, but externally as well.
Jordan Wilson [00:29:30]:
You know, one thing, you know, on this topic of what happens when AI works and looking at responsible AI, because I'm sure it's not an easy role for you and your colleagues, at AWS, Dia. Right? Because this is huge. Right? This is this is, it's topical.
Jordan Wilson [00:30:04]:
It's it's philosophical. It's it's it's moral ethics. Right? Having to juggle all these things. What's the one concept of, you know, what happens when this works? What's the one thing that maybe still not necessarily keeps you up at night, but what's maybe the the the one, key or one, you know, specific aspect of responsible AI that still has you focusing and and scratching your head trying to figure this out?
Diya Wynn [00:30:28]:
Alright. So I would say, like, the first thing is and I said that there are, you know, teams of folks. I'm not I'm not an only the only individual that gets to do this. So the one thing that I would mention is that responsible AI is everyone's responsibility. Right? It's not just reserved to the technical folks or the person that has a title with, like, the responsible AI lead. But ultimately, we all have to buy into this notion of considering the things that are important that we introduce into the process, transparency, fairness, you know, security, safety, and our development implementation, and even sort of long term, like, deployment, what happens in the world in terms of the technology being developed. And that's important because we can do all the things in design and implementation. But if we don't think about, like, how that gets lived out and delivered in practice, then we still will miss the mark.
Diya Wynn [00:31:16]:
And this also connects to why the thing that I think about. So I'll use this in the context of an example. We have a customer that is able to, now leveraging AI, 15 months prior to clinical manifestation, be able to determine, the likelihood of, of this individual, an individual getting heart disease, major, major sort of move, and advancement in technology. That's important because heart disease is the number one killer of all demographic people of all demographics in the United States. Right? So imagine the idea that we could actually save people's lives, provide preventative care to keep people from dying every day, every hour from heart disease. Major, major sort of advancement. But, you do all the things to like build that to make sure that we have the right kind of data set that we are doing, and we can sort of generalize across demographics that, that that are we have the kind of consistency that that it is necessary in, those kinds of predictions. We understand the treatment that's necessary.
Diya Wynn [00:32:21]:
But the way that we allocate resources in this country often might mean that in deployment, people with Medicare, right, or Medicaid, so that's lower socioeconomic groups or those that have certain social programs may not get that service because it could be reserved to those that have private insurance. That part of this puzzle in terms of like the, the insurance of, the next step in terms of like how that gets deployed, isn't just reserved to the person that designed the technology, but also in terms of the way in which we allocate resources in the country. Right? So oftentimes those things will go to people in certain social economic groups, to certain communities. We may not get that in an underserved, underprivileged environment. And still that means that that opportunity for someone to get that life saving benefit may not occur because there still requires this other element of, of, of, of government, this other element in terms of our deployment that's necessary. And that's bigger than, you know, what I can do, what what we get to do in a technology context, right, in terms of helping people understand the impact or whatever, is bigger than, you know, even probably what that one, you know, health care provider or manufacturer does, right? This is, you know, more about being able to assist it, to systemically dismantle or dismantle some of these systemic and institutional things that keep, privilege and power reserved to some and not providing benefit to all. And that is probably the thing in this, area that, that keeps me up at night and keeps me thinking about the other kinds of connections and the partnerships and the conversations that are still so essential so that we could have this, intentionality and development that builds into, or or gets played out into what happens in the real life in terms of its deployment that can truly provide the benefit that we know the technology is capable of and we're all looking and desiring to see.
Jordan Wilson [00:34:17]:
One one other side, Dya, and I know you know, thank you. We appreciate your time. I don't wanna go too long here, but, you know, what about for the extreme positives? Right? So you kinda just mentioned it there through that, you know, example of, you know, AWS an AWS customer being able to, identify potential, you know, heart issues much sooner, which is huge. But what about just leveling the playing field for, you know, underserved populations? Right? Because I think when we think of what happens when AI works, I think so many people, sometimes myself included, just think about jobs, right, and the pitfalls that that could bring and the uncertainty that that could bring. But what about the bright sides, like leveling the playing field for underserved populations?
Diya Wynn [00:35:00]:
Right. Right. So, I mean, you know, we, we often talk about examples like in education and you just mentioned one that I got to go, like, look at that study, right? Like talking about math. Cause I remember I'm old enough to remember when we started using like graphing calculators in school and it was the same sort of conversation about how we wouldn't know how to do math and people would become dumber because right. Because we now are using technology. And so that, that article is interesting, right. To explore like what that means, because we're hearing some of the same conversation, but some of the immediate examples that we're seeing people leverage, you know, in terms of AI is in education, right? Being able to, my mom was a teacher. And so she talked about like having 30 plus students in class.
Diya Wynn [00:35:42]:
And if you had someone that had unique needs, right. In terms of how they learned, they, the typically in the class, they were optimized to the majority in terms of their learnings, their teaching style. And they may not have an opportunity to reach out to one that might have a little bit of a learning difference, or we have these mixed classes. And so someone with, that might be a little behind in reading may not get the same attention. So imagine having, you know, AI powered assistance in classes, or you have students, who English is their second language in the class as well. And now they have power translation to be able to break, and eliminate some of the language barriers. Right? All of that now is a way in which AI could level the playing field and ensuring that everyone and all of our learners get the same sort of benefit in a class where typically that might be a little bit more challenging, you know, think about, there, you know, there are companies that are providing like banking services to those that are underbanked and, in regions because they don't have access, right? That's leveling the playing field for women and for those that are in the marginalized groups that typically may not have banking services like in regions of Manila and in, in, in, in, in South Africa. You know, assistant tools that, are, perhaps not, you know, focusing more on skills.
Diya Wynn [00:36:55]:
When you look at like hiring and can reduce some of the unconscious biases that come into and there was a company that was out of Latin America that was like eliminating, some of the elements that typically would be used to, exclude individuals from the hiring process. That's one of the ways to elevate the, to sort of, level the playing field. Think about, you know, the ability to implement systems that, you know, do and provide the kind of robust testing for fairness and bias and systems that are deployed to make sure that social services or financial decisions aren't having the kind of negative impact that we've already seen that they've had for years. Those are the kinds of things that I'm thinking about when we talk about level of the playing field and making adjustments for. And then there are even some simple ways. Right? Like barriers to entry. You know, video production, for instance, used to be very, very challenging and costly, but now folks can do that with tools and technology and enter into a space that typically were reserved to those that had the resources to be able to provide, purchase that equipment. I mean, that is creating and reducing a barrier to entry now that previously existed.
Diya Wynn [00:38:09]:
And so those are all the things that I think about in ways to bring others along that typically might not have had access or might not have had the opportunity. But we still have to continue to do the intent intentional work to have the other processes and systems in place to to make that, you know, an ongoing reality.
Jordan Wilson [00:38:27]:
Diya, I think this has been, such an educational journey for all of us in in, you know, 35 minutes here so far. But, you know, as we wrap up, because we've talked about, you know, the shift, in work and upskilling, reskilling, responsible AI practices, companies should be thinking about, how companies and departments should be investing in new areas and goals. But as we wrap up, maybe, Dia, what's your one most important, takeaway that you want business leaders to remember about what happens when AI works and how we can tackle that with responsible AI?
Diya Wynn [00:38:59]:
Yeah. I I think I mentioned this in the beginning, but if I didn't, this is, like, the kind of key statement for me, at least, like, one of my grounding principles. I believe that AI can be a bridge, not a barrier, one that, you know, empowers everyone to reach their full potential, and to help us drive towards towards a more equitable world. That does not happen without the kind of intentional action that's necessary to unpack, to understand the areas of risk and to put in place the kind of organizational structure that, supports a culture of responsibility and, you know, brings in concepts of value alignment, fairness, inclusion, training and education, the commitment to looking at security and safety, all of these elements, to ensure that we ultimately see the kind of benefit that we believe is is impossible what is possible, with the technology.
Jordan Wilson [00:39:52]:
Wow. Y'all, if if if you heard, you know, some some click clacking there, that's me typing notes. There's so much so much good, information there from, Dia. This is gonna be a fun newsletter for me to write, but, Dia, thank you so much for spending your time with us this morning and coming on the Everyday AI Show. We really appreciate your time and your insights.
Diya Wynn [00:40:13]:
Yeah. And thank you. I know we've been trying to have this conversation for a while, so I'm excited that we were finally able to make things align and and get in here together. So thanks for having me.
Jordan Wilson [00:40:22]:
Absolutely. And we did y'all, we did mention a lot of different studies and resources and all of those things. Don't worry. You don't gotta go chase them down on the internet. They're gonna be in today's newsletter. So if we talked about it, don't worry. Just make sure to go to your everydayai.com. Sign up for that free daily newsletter.
Jordan Wilson [00:40:40]:
Make sure to read, today's, you know, recap. I think it's gonna be an important one that all organizations need to take seriously, all of these things that Dia was sharing her, you know, her experience in. So thank you all for joining us. We appreciate it. Make sure to join us tomorrow and every day for more everyday AI. Thanks, y'all.
Diya Wynn [00:40:58]:
Alright. Bye,