Everyday AI Podcast – An AI and ChatGPT Podcast - EP 277: How Nonprofits Can Benefit From Responsible AI

Episode Date: May 21, 2024

Generative AI offers significant benefits to nonprofits. What obstacles do they encounter, and how can they utilize this innovative technology while safeguarding donor information and upholding trust with stakeholders? Nathan Chappell, Chief AI Officer at DonorSearch AI, joins us to explore the responsible use of AI in the nonprofit sector.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode pageJoin the discussion: Ask Jordan and Nathan questions on AI and nonprofitsRelated Episodes:Ep 105: AI in Fundraising – Building Trust with StakeholdersEp 148: Safer AI – Why we all need ethical AI tools we can trustUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTimestamps:01:50 About Nathan and DonorSearch AI05:52 Decreased charity giving, AI aids nonprofit efficiency.09:39 AI enhances nonprofit efficiency, prioritizes human connections.13:35 Nonprofits need to embrace AI for advancement.16:22 Use AI to create engagement stories, scalable.18:59 Internet equalized access to computing power.25:02 Nonprofits rely on trust, need responsible AI.29:52 Ensuring trust and accountability in generative AI.33:35 AI is about people leveling up work.34:16 Daily exposure to new tech terms essential.Topics Covered in This Episode:1. Impact of Generative AI for Nonprofits2. Digital Divide in Nonprofit Sector3. Role of Trust in Nonprofits and responsible AI usage4. Traditional Fundraising vs. generative AI5. Future of AI in NonprofitsKeywords:Nonprofits, generative AI, ethical use of AI, Jordan Wilson, Nathan Chappell, DonorSearch AI, algorithm, gratitude, machine learning, digital divide, AI employment impact, inequality, LinkedIn growth, Taplio, trust, Fundraising AI, responsible AI, AI explainability, AI accountability, AI transparency, future of nonprofits, AI adaptation, predictive AI, personalization, data for donors, generosity indicator, precision and personalization, AI efficiency, human-to-human interaction, AI toSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. One group that I think can really benefit from generative AI is nonprofits.

But there's a lot to understand. There's a lot that you have to consider, not just on what are the best ways to use AI for nonprofits, but also how you can do it in an ethical manner and how you can do it responsibly. I have a little bit of a background there myself, but I'm no expert. But luckily, if you're tuning in today, we have an expert that we're going to be going over today and talking beyond AI ethics and why nonprofits must focus on beneficial and responsible AI. I'm super excited. And thank you for joining Everyday AI. If you're new here, Everyday AI is a daily live stream podcast and free daily newsletter, helping everyday people like you and me learn and leverage generative AI.

So if you haven't already, make sure to go to your EverydayAI.com and sign up for that free daily newsletter. Each and every day we recap our interview for the day, go into even more depth on the topics that we talk about, as well, keep you updated on everything going on in the news, fresh finds from across the internet, et cetera. And this is one of those, you know, normally we go live every single day at 7.30 a.m. Central time. That doesn't always work for every single guest. And sometimes we do pre-recorded ones. So today is pre-recorded, but we are debuting this live. And don't worry, I'll be in there in the comments. So still get your questions in.

maybe we'll just happen to answer them anyways on our conversation. But if not, I'll make sure to answer what I can. And maybe we'll tap into our guests for the rest. So speaking of that, I'm very excited. Let's go ahead and bring on to the show. There we go. Got him. Nathan Chappelle, who is the chief AI officer at Donor Search AI.

Nathan, thank you for joining the Everyday AI show. Yeah, thanks, Jordan. It's great to be here. I've been looking forward to this. Oh, me too. I love, like, I can talk nonprofits all day. more on that in a minute. But tell us, tell us a little bit about, you know, who you are and what you do at donor search AI. Yeah. Yeah. Well, I mean, I probably had a kind of long, windy career like in the nonprofit sector that a lot of people never intended to be in that space. And I was a technologist out of undergrad, started one of the first, well, the first dot com to sell skis on the internet.

I just early adopter. I was focused on big data storage and, and that type of thing. Got into nonprofit on accident. thought I'd be there for a few years, and I found myself there for 20 years, always a little bit of a fish out of water thinking, like, why is the sector so unique in their, like, resistance to adopting technology and why are they so slow? And back in 2017, you know, frankly, I was just kind of dismayed at the lack of innovation in the sector. So I, you know, sat down and ended up creating the first algorithm that predicted gratitude

that we spent about a year and a half, spent about a million and a half dollars to build. It worked really well. It's been totally disruptive to the sector. And that's what I do. So my team at Donor Search AI, probably the, I mean, I'm very biased in this, but we have an incredible data science team that now does this with, I mean, hundreds. I mean, we have about 10,000 nonprofit clients. And about over 100 of them, we build and operationalized custom machine learning models for.

So that and now, of course, I spent a lot of time, both in the area predictive and generative together and kind of what that means. and also on the responsible AI side, like you said in the introduction, which is definitely more my passion project. That tends to be more my nonprofit work right now. It's really on the responsible AI side. Okay. I have so many questions now. And I'm like, if you're listening on the podcast, I'm geeking out smiling because, yeah, I myself spent, you know, about nine and a half years working at a nonprofit. So, you know, what Nathan's talking about there, I feel it, right? But, you know, Nathan, I'm curious. So you've worked with, you know, you said more than, 10,000 nonprofit clients. You know, when you're talking AI to the average, you know, nonprofit worker, is it a lot? Because at least in my experience, I feel there's two types of

nonprofits. I think there's like 2% that are, you know, so into data and, you know, donor data and technology. And then I feel everyone else can kind of be a dinosaur, unfortunately. And it's hard. I mean, what's your experience been working for so long with so many nonprofits when it comes to adapting to new technologies like artificial intelligence. Yeah, I mean, and, you know, to level the playing field, there's about 1.7, 1.8 million nonprofits in the U.S. alone, around 10 million in the world. And so there is absolutely a digital divide. You know, there are those that have, you know, in this case, we'll say, like, have, you know, a view on like big data and data storage, data collection and now AI and those that don't have. And there's a stark difference. So, you know,

a vast majority of our clients buy data from us and that they've done. that for 15 years. Those are the ones that at least see the value in big data. And at, you know, for the most part, throughout my career, we always bought a lot of data, didn't really know what to do with it. It wasn't really until AI, you know, really truly more on the predictive side, like machine learning, deep learning, allowed us to take all that enriched data, the data that we had as a nonprofit, plus the data that we could buy and then start, you know, making sense of it. That's still pretty new. I mean, there are a lot of reasons why we could spend a whole, you know, we could spend an hour we're talking about why nonprofits are slow to adopt, but we don't have time for that.

But I think what's more important is that the nonprofit sector is challenge right now. Less and less people are giving to charity. I wrote a book about it called The Generosity Crisis in November of whatever last year was or the year before last year. It just time flies. But the reality is that our less and less people like you and I are giving the charity because we're essentially disconnected. And so we see like the advent of AI and how AI can now, essentially allow nonprofits to become more efficient, allow them to spend more time human to human. This is a breakthrough and an advent of time that nonprofits have always been waiting for.

And so I know you focus a lot on Gen AI. And from the Gen AI perspective, like I started 20 years ago, like when you were in a nonprofit, could you imagine what life would have been like if you had tools like, you know, perplexity or clot or ChachyPT at your disposal? Like, right? We crushed it. Like you would have crushed it. And so now we're in this time where like non-Mptych.

nonprofits can level up and compete at a level they've never been able to, yet they're still so slow to adopt. Only about 5% of nonprofits are actually fully deploying AI right now. So a lot of work to do. We made a lot of progress, but still definitely a lot of work to do. So one thing that I wanted to pull out of that response there, Nathan, is how there is this, you know, like the book behind you. You know, for those joining on the live stream, you know, there is a generosity. crisis, right? So I think that a lot of people have this thought of generative AI or AI as, you know, people think, oh, it's robots, it's, you know, Terminator, it's SkyNet, et cetera.

But what I'm hearing from you is that nonprofits can actually use AI to make things more personal and to strengthen relationships. And it's not necessarily a very impersonal, you know, tool or technology. Can you talk a little bit more about that and how maybe nonprofits might want to change how they're viewing artificial intelligence to actually be something that can bolster relationships and drive positive change for this, you know, generosity crisis we may be facing. Yeah. I mean, such a great question. Lots to unpack there. I mean, the reality is that for, you know, about 40 years now, nonprofits have been essentially using data largely based on like how wealthy people are. And they've been taking that data and prioritizing this idea of like, hey, you're a good donor or

you're a good prospect because you're wealthy. And I'm going to spend more. time with you. The reality is that wealth and generosity have very little to do with each other. Our very first data science project showed that wealth alone is about less than 10% predictive of whether or not you're likely to make a gift. So no matter how wealthy you are, there's only a 49.6% chance in America that you're going to give to any charity. Yet most nonprofits have just thought, oh, if I could, you know, get a dollar from everyone on the planet, you know, we'd be rich. So first is, I think, accepting this idea that not everyone is a good prospect. So predictive AI was really that area that we could really narrow in that people that

had the deepest connection to our organization were best prospects. That makes sense. So you're measuring connection. And then my connection today is different than my connection tomorrow in two weeks and two months and two years, using AI to really harvest that connection. So the predictive side has been around for a while and has been actually pretty helpful at streamlining and identifying not all people are created. equal, some people care about you more. And those that are raising their hand will show up in data.

Then what gets really exciting is advent of gen AI to basically take those predictions and take action on them. Right. So like predictive AI is about predicting things and making sense of all that data. But generative is about creating things. So you match. And I think this is where our sector is going to go where it's like really understanding like just creating more for the sake of creating is not going to help anyone. But creating more and specific to an individual where now we're talking about like this idea of precision philanthropy where it's like precision medicine like every person is unique to like how a therapy will help them or hurt them based on their DNA. Precision philanthropy is the same idea that there's no such thing as a good donor or bad prospect that all

people have a varying degree of connection to your organization. And then the tools and that you use to essentially activate and spend more time with those individuals is really going to be, you know, will make the big difference. And I'll say all that to say, AI is not a silver bullet. It's not a magic wand. It should never replace a human. But what it should do is it should create a tremendous amount of efficiency where people in nonprofit who are the hardest working, you know, people, as you know, having been in that

sector, like it's a passion, not a job. You know, it's a, it's a way of life, not a career. How do I use AI so that I can spend more time human to human? And that's true with AI in any domain, whether it's in, you know, clinicians, you know, wanting to use AI so they could spend more time with their patients or whatever the use case might be, that's where I see things get really exciting for the nonprofit sector. Adobe just introduced an entirely new way to create, bringing the power and precision of its

creative suite into one conversational experience. Meet Firefly AI Assistant now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's creative agent, Firefly AI Assistant lets you start with your vision, just describe what you want and shape the outcome as it takes form with the assistant. The assistant orchestrates multi-step workflows, drawing on 60 plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator, Premiere, Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for

common creative tasks, like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adopi.com. You know, something that you mentioned there is, you know, kind of this intersection of predictive AI and generative AI. So, Nathan, maybe for those people in the nonprofit sector,

out there and they're hearing this and you know, you hear all these buzzwords, right? And for those people that maybe aren't, you know, lifelong technologists or have a background in machine learning like you do, can you briefly explain, you know, kind of what that means, you know, bringing predictive AI and generative AI together and specifically, you know, how, you know, nonprofit people that maybe aren't even super technical can take advantage of that intersection. Yeah. No, that's a great point. I actually did a poll recently of like how, if someone asked, asked you, could you explain the differences between predictive and generative AI and, you know, at least fumble your way through it. So it's, you know, we're getting there. So I think what resonates

for most people, you know, this idea, especially in the nonprofit sector, are questions like, how much will someone give or when will they give or if they will give? So all those things were predictive AI, which, you know, predicts things. So it's about precision. It's about creating precision based on lots of data. Those questions are not new questions. In fact, like I, I started talking about this back in 2017, and I stumbled upon this quote by Aristotle. This is like 2300 years ago. Aristotle said, you know, the idea of giving away money is easy, but how much to give and when to give and how to give it in what way, that's hard, right?

Those are essentially barriers to generosity that have existed, like, since the beginning of like, you know, Western civilization. But the idea of so, like, predicting who might do those things or how much is one thing. But the idea of like, what's the best way to reach out to them in a way that's, say, personalized to them based on their own unique characteristics? That's generative. So generative, again, creates things where predictive AI predicts things. If you match those things together, now you have precision and personalization at scale. And, you know, I think one of the best examples that I've seen recently is like Carvana.

Like, Carvana is like as a company, you know, struggling a bit. Like the stock market's got not not been super great. they jumped into this, you know, predictive gen area because they're like, okay, we have lots of people bought cars. Those people have stories about their cars. And I don't know if you've seen this example. So if others haven't. It's a really great example.

They produced like, I think, 1.3 million custom videos. Basically, it's a picture of the car that I bought like two years ago. And it's a car talking to me. So everything is generated through, you know, Gen AI. But they use precision to determine, okay, not every customer is going to get one of these videos. only certain customers are going to get these videos, but they were able to, like, offer that precision at scale and in a really profound way.

And I see that that blend of precision and personalization, supporting the nonprofit sector so well, like being able to say, like, you are important to us because of these reasons, and this is a journey we've been on together. And, you know, our journey has just begun. Like, how about we, you know, change the world together? And be able to do that at scale and the fly on, you know, in real time, would be just absolutely amazing.

So we're already seeing these technologies emerge, you know, really, you know, prolifically in the private sector. So now in the nonprofit sector, which lags, it's like they become more affordable and they, you know, become scalable where nonprofits now, you know, for many years didn't have access to machine learning. Now they do. They didn't access to generative AI. Now everyone has access to generative AI. So it's, again, I'll leave with the optimistic side of like, it's a really cool time to be

alive. Nonprofits really need to like double down on to get out of the. comfort zone and learn something new every day about AI and probably starts with listening your podcast every day. If you do that, you're probably like 95% ahead of the, you know, the rest of the crowd. Yeah. And one thing, again, like when you keep saying these things, right, I'm like, man, what a time to be alive, right? Like, like if you're a nonprofit right now, like, because I, I remember personally, you know, I would spend, you know, 10, 15, 20 hours on these, on these projects that now take 10, 15, 20 seconds, right?

So even this example, right, of Carvana, you know, kind of operating with this, you know, precision and personalization at scale. What do you think is maybe some examples? You know, what is that example for the average nonprofit that they can use, you know, precision and personalization at scale because it's available now, right? Like you have the data. The data has always been out there for nonprofits. but the data has usually led to a lot of manual work, right?

So what is that kind of sweet spot between precision and personalization that your average nonprofit can go take advantage of right now? Yeah, and you can start small. I mean, it's all about baby steps, right? Not everyone's going to, you know, build an operationalized custom machine learning models and then, you know, build out the curve on a type thing. But the reality is that, you know, almost any nonprofit can use, they have some basic information.

So say, like a university or a K-12 school or whatever, be like, hey, this person went here around this year, this is the year they graduated, this is the degree they got. And, you know, or like they volunteered at our food bank or whatever. They can track, they absolutely can track some of that data that shows like this person cares about us. And at the very, very least, and it's super cheap to do and not many do is like, do a survey. Like find out, you know, like start out with building a Google survey to find out like,

this is what people like about us. This is what this person cares about us. We know that they're one of their top charities or, you know, they're not. Take any of that type of data, which we would consider like experiential data. It's like represents like how they care. Do just a little bit of segmentation around that. Then use generative AI essentially to then build, say, an engagement story about your organization that's going to speak to the reason why the person answered that survey that way anyway.

And so like this, like that like understanding someone from a survey response, building an engagement strategy with a person that represents their sediment of what they were feeling in that survey response, that is like super scalable. Like that is like every, that literally costs no money. So like when talking about the digital divide and nonprofits that don't have budget for this could do things like that right away. And not to say that, you know, they don't have to learn something new. Of course, you've got to learn something new. You've got to get out of your comfort zone. But start small, take baby steps and just start doing it.

and you're going to figure out how to automate those types of things, how to deploy a GPT so that you can have people engage with your nonprofit and learn more about the history and what it means and how it impacts society. So, I mean, think about things like communication, you know, from a generative perspective, like analysis, creative ideas on thanking people. Like, what a novel concept? Like, people get bored of thanking people the same way. Like, ask GPT, what are some creative ways to thank someone?

So, I mean, I could probably route off 20 use cases right now that would cost a nonprofit $0 that would actually make them feel and appear more modern like Starbucks who's like, hey, it's your birthday month. Like, we've missed you like where you could come in and see us and the coffee on us. Like those things don't, you don't have to be Starbucks to do that. Like you literally could do that right now with like $0. Yeah. And I don't know if there's ever been a time. And I'd love to get your thoughts on this. Nathan, is has there ever been a time in your experience, you know, working in this field,

you know, you obviously have a much deeper and, you know, more tenured background in machine learning and AI than the average person out there. But has there ever been a time before that you've seen where the digital divide between nonprofits and everyone else shrinks this quickly? Because, you know, even my own personal experience is, you know, we would sometimes get, you know, support or grants, you know, from these big tech companies. And they were great by, you know, you know, supporting nonprofits. But then you go see, you know, how they operate. You see the technology that they're using. And, you know, from the nonprofit perspective, it's like, wow, that seems like a different world. Well, I personally don't know if it's like that. You know, it seems like it's a

level playing field. Can you speak to that a little bit and talk? Has there been a time before where the divide seemingly is shrinking that quickly? Well, I mean, if, you know, because I'm old and I, you know, in high school, I was learning DOS. So like when I, you know, in 19. 90, you know, probably 95, 96, you know, the internet was a great equalizer between those that have and those that didn't have. So like, you know, the only firms that afforded compute power were those that like were buying like big servers, right? So like, so that was a time where the digital divide shrank very quickly. It's like, you know, it's insane to think now. It's like, hey, nonprofit, like, are you using the internet?

Well, of course you are. So like, so I think this is where a lot of people equate like generative AI to like the internet because it is a great equalizer. sense of like you don't have to like build a big server farm and you don't have to even pay AWS to have like a big or Microsoft a big cloud environment. You just like go on your internet browser and now you have like all the stuff at your disposal. But I think the scary part with this is that nonprofits again being so slow that the key difference between like generative AI or just AI in general compared to like the internet is

that AI is an exponential technology, meaning that it doesn't say the same. it gets better. And so progress in exponential technology is not measured in time. It's not measured by like, oh, I'm not ready for this. I'm a little scared of AI, so I'm going to do it in a year. You're not one year behind. You're like 365 AI cycles behind, right? And so it's like this idea, Harvard wrote this article in this like years ago,

and it's stuck in my brain of like those that failed to deploy AI may never catch up because it's that exponential technology. And so the digital divide. seemingly, to your point, because you're looking at this objectively, it's like, oh, it's like the great equalizer. Everyone has access to it. Everything should be great. The reality is, if the nonprofit sector does not get out of their comfort zone, and they are like, oh, I don't trust this. I'm not going to do it until everybody else is doing it. That digital divide will grow exponentially. And while at the same time, there's lots of reports out there from whoever

McKenzie and the IMF who is saying, you know, like AI can be, you know, can help. bridge the digital divide, it actually can also, you know, 60% of jobs are going to be impacted by AI in some regard. And by the way, inequality might increase. So this is again, I think this is a need and opportunity for the nonprofit sector to rise the occasion since the nonprofit sector focuses on inequality at large. That's kind of what they're built to do, is that waiting and seeing is contrary to like what society needs right now. And so this is where again, it's like, It's really great time of your live, really great opportunity, but it's also like a moral imperative that the nonprofit sector rise to the occasion and be like, okay, we're in this.

Like, let's help steer this technology into responsible ways. Hey, this is Jordan, the host of Everyday AI. I've spent more than a thousand hours inside ChatGPT, and I'm sharing all of my secrets in our free Prime, Prompt, Polish ChatGPT course that's only available to loyal listeners like you. Here's what Lindy, who works as an educational consultant, said about the PPP course. I couldn't figure out why I wasn't getting the results from chat GPT that I needed and wanted.

And after taking the PPP course, I now realized that I was not priming correctly. So I will be heading back into chat GPT right now to practice my priming, prompting, and polishing. Everyone's prompting wrong. And the PPP course fixes that. If you want access, go to podppp.com. Again, that's podppp.com. Sign it for the free course and start putting chat GPT to work for you. Such, you know, my brain right now is rattling because, you know, I'm trying to process something here, Nathan, because what you just said right there, right? Like my way of thinking has always been yes, you know, generative AI can be something that helps bridge the gap.

But it makes sense. It makes sense what you're saying. It can bridge the gap if you're, you know, putting through or putting in the work. I guess to actually use and understand generative AI. But if you're not, if you're falling behind, you know, that 365, you know, light, light years in AI, you know, behind, then the divide actually becomes much, much greater. So how can nonprofits keep up, right? Because I think the biggest thing is there's always, you know, having to win that trust of a nonprofit, right, before using a new technology. A lot of times from personal experience.

So, hey, if you're a nonprofit leader out there, don't get mad at me. but, you know, there is more of a distrust with new technology. Oh, for sure. Because people say, oh, you know, I want to protect my, my donors, my volunteers, which I get and I understand. So how can they balance all of that? Yeah. And I think some of that's warranted, right?

Because, you know, if you compare and contrast the nonprofit sector, the private sector, so let's use, we could pick on Twitter. I don't know. Hopefully they're not a sponsor of yours and this will piss them off. But, okay. Okay. So now it's not even Twitter anyway. So we'll just, you know, at one point, years ago,

Twitter created an algorithm that was like, I don't know if you remember this, but like racist, agist, Islamophobic and ableist and all those things, every is that you can imagine. And it got out. And this algorithm was predicting all these really kind of bad things. And of course, like their stock price goes down. But did that affect like Facebook stock or Google stock or Microsoft? Not at all, right? Because that was one single player.

That was whatever we were just talking with Twitter that did those bad things. Right. And then they had to rebuild trust with their stakeholders and shareholders. to rebuild that. But if the nonprofit sector did the same thing, and especially if, like, say, a large nonprofit is trusted, so like Red Cross or somebody like that created an Aegis, Islamophobic, ableist algorithm, it would impact trust in all nonprofits. So the reality is that the nonprofit sector has nothing to provide back to individuals in terms of, like, a service or a product. So, like, if Nike, you know, did something really bad, but I still really like Nike because they fit my feet better,

I'm going to still buy Nike. But a nonprofit sector, essentially, you're exchanging money for trust. So the nonprofit sector is in the trust business. So I do think it's warranted to some extent that the nonprofit sector needs to prioritize trust at the very highest level and ensure everything they do essentially does not diminish trust. And with that said, advances in technology and AI has been so fast. And there are no real universal standards for what responsible

AI is. So, of course, like in the UK, there's some, and the White House has very loose guidelines on like responsible AI. But so many corporations have created their own to try to instill trust. The nonprofit sector is saying, well, wait, like, let's, let's wait and see what this means until we're going to, you know, go full bore. That's where fundraising AI, you know, stepped in. And back in November of a year ago, we created the first framework for responsible AI for the nonprofit sector. And that specifically was to highlight the need for things like transparency and beyond ethics, but like transparency, accountability, explainability.

And not to only look at is this ethical, but is it also beneficial? So like we might say that like using, you know, Instagram is not unethical, but is it beneficial? And we've seen lots of downside from that. And so those are the things that I think the nonprofit sector has to wrestle with, not just things that are ethical, but are. Are they also beneficial? And so that's kind of where we're at today.

And I think we made a lot of progress in that space. But there's, you know, there's a lot of room to grow. And I, but I do think that this is a time where the nonprofit sector has to lean in. And they have to take a strategic and measured approach to using AI to remain relevant and to address inequality as as it is also created by AI. So we'll definitely link to that the framework for responsible AI in the newsletter. But Nathan, if you could, maybe just give us a super high level overview of, you know, what nonprofits should be looking at, right? Because I feel those that that quote unquote get generative AI and they can work at that,

you know, intersection that we were talking about earlier, you know, with precision and personalization, they're going to be, you know, have abilities, you know, when it comes to, you know, outreach or fundraising or data collection and what you can use the data for. maybe new capabilities or powers that they didn't have before. So, you know, what are some of those things that they should be focusing on once they do get it to make sure that they're still using it in a responsible way that benefits others? Yeah, you know, and I don't think it's that hard. I would say when you're thinking about the litmus test for a nonprofit is that, you know,

is this not only beneficial for my short-term goals, but also is it beneficial to our sector as a whole? So I think that's something that a nonprofit sector kind of uniquely has, is this kind of protectionism of the sector as a whole, of society as a whole, and as they should, right? Because we, non-profits and nonprofit employees are kind of, they're grounded by this idea of like everything that we do and it has, you know, an impact.

And so I think for the most part, nonprofits, when they lean into this, have to ask themselves that question, like, is sounding authentic the same as being authentic, right? So prioritizing authenticity, transparency, vulnerability, is something that we have to go a step beyond, whereas, like, most frameworks that exist in the world will focus on, like, robots serving humanity's best interest. And so that's like White House Lingo. The White House Lingo is like, we need to build AI that serves humanity's best interest. That literally translates to, like, robots not killing people. That's not a, I mean, of course,

that's important, right? We don't want robots going down the street. We've seen chappy, you know, and, like, bad things happen. But the reality is that nonprofit sectors, the nonprofit sector needs to really focus on on AI that is going to preserve an instill trust. If there was just one question, like if I'm using generative AI, predictive AI, and however I'm using generative AI, so whether it's communications or analysis or HR or legal support or, you know, creating images, like if I was asked myself one question, does this action preserve and protect trust? Like not rocket science at all, right?

So that's just one question. Like everything I do in generative AI, which is a lot. I could do so much with generative AI. Does this activity preserve and protect trust? And what that means in the nonprofit sector is that when you're using AI, essentially there should be the call to question, like, how do I trust a black box? Like, I need to make sure that it's the best of my knowledge. And Jen is very hard in this case because like predictive AI,

you can actually see mathematically how a prediction is made. Like it's actually fairly easy to see like prediction is made by the combination of these data points, you know, that say this person is going to do this thing. Generative is a little harder. So I think you've got to work with and use generative that is taking ample activity to make sure that they're being responsible in their building of AI. So like an open AI or a cloud or perplexity, which like essentially, you know, will give you footnotes of everything that it's doing. I think that's almost a requirement in the nonprofit sector where it's a nice to have in the private sector that to really understand how the AI is actually making decisions, what it's telling

you to do, and to ask the question, does this activity preserve and protect trust, like, go from there, you know, and so, you know, demand kind of those things, transparency, accountability, explainability. Yeah. I think the explainability, especially of generative AI, is crucial for nonprofits, right? Because, yeah, they have to be able to communicate it not only internally, but also with external stakeholders, you know, donors, volunteers, et cetera, you know, what they're, what they're doing, you know, how they're using this data that they do have. And so, so, Nathan, we've talked about so much in this episode, right? We've talked about machine learning and donor data, predictive AI, generative AI, you know,

and then how we can also, you know, how nonprofits can use this all responsibly and use it transparently to benefit others. So normally I don't ask people, you know, to predict the future in their space, because it's hard, right? Like we kind of talked about every day is almost like, you know, can almost feel like a year of development. But, you know, with someone such as yourself who spends so much time and has such a deep background for maybe those nonprofits right now that are hearing this and they haven't fully adapted, I feel if if you kind of go to where we're at now, you're going to be behind, maybe, right? So how can they, you know, sort of skate to where the puck is going when it comes

to, you know, generative AI and taking advantage of the data and the, you know, technology that's out there. Yeah, I mean, it's such a good question. I get this question a lot. You know, I mean, the reality is I don't believe that AI will replace fundraisers or AI won't replace nonprofits, but nonprofits or fundraisers that use AI will replace those that don't. So I, I mean, fear, you know, the fear is that's actually just not like fear mongering. Like, that's true. Like, you will not be in a place where you as an individual will compete, or as an organization, will compete in the future if you're not using AI. And so now is the time you have to AI proof your career and your organization.

So with that said, you know, start where you start. But to your point, like generative is so, it's a great equalizer. It's so accessible that there's no reason not to. So, I mean, I think about that movie, What About Bob, which is like Bill Murray and I forget Paul, whoever it was with Bill Murray was like this idea of like baby steps. Like you just got to take babysat and like every day learn something new about AI, try something new, always asking the question, does this preserve and protect trust? But just start small. I think when most people don't start working in AI is because they're just afraid of starting.

And, you know, the kind of common kind of philosophy in AI is that 70% of AI has nothing to do with data or models. It has to do with people. So take away that idea of like AI is all about data and models because it's not. It's about you as an individual and the people around you and how you will either level up your work, you know, and see, you know, because you spend a lot of time in generative AI. It's like, you know, producing more work faster, you know, more accurately and with less burnout. Like, who doesn't want those things? Like, that literally is a definition of every nonprofit worker I've ever met, you know? So like, let me do something faster with a higher, you know, quality of work and be less burned out, like all day long. So jump in, baby steps,

learn something new every day. I always start with podcasts. Like listen to one new thing every day. And it doesn't have to be rocket science. Just like get comfortable with the lingo. And then, you know, log in to chat GPT. It kind of starts there or clot or perplexity or co-pilot or whatever your flavor of choices. And just start, you know, start asking it questions that solve, you know, some of your more immediate business needs.

So good. And I think, Nathan, I love the analogy of baby steps. But I think that you helped all of us. And not just those who are in the nonprofit sector. I think you helped all of us take a little bit more than baby steps with today's conversation. So thank you so much for joining the Everyday AI show. We really appreciate your insights. Absolutely pleasure.

Thanks so much, Jordan. Hey, thank you. Thank you for listening, everyone. We appreciate your time. Make sure if you haven't already, go to Your EverydayAI.com. a lot of fantastic info in there. So some of the things that we reference we're going to be putting in the newsletter,

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