Will AI Be a Force Multiplier for the Social Sector?

Jacqui Watts:

One of the best ways I think donors can support the organizations in their network in terms of AI adoption and potentially using AI for either internal or mission-driven work is really making sure and holding those partners accountable to starting from a problem-first approach and not, you know, just chasing tech for the sake of chasing tech.

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Barbara Wheeler-Bride:

Hi everyone. I'm Barbara Wheeler-Bride, an editor at Stanford Social Innovation Review. This is Giving With Impact, a podcast about what's at the heart of achieving more effective philanthropy. We're produced by SSIR with support from DAFgiving360^®. 

In this episode, we focus on a topic that's not only top of mind for leaders in philanthropy and the nonprofit sectors, but across society: artificial intelligence.

Many nonprofit and social impact organizations are already exploring how AI tools might help them further their mission. But what are the risks? How should organizations and funders make sure the right guardrails are in place to protect the vulnerable communities they work with? How can they make sure that scaling up AI adoption doesn't create deeper inequities in society? How does the social sector build out the infrastructure and ecosystems to use this technology with greater care and equity? 

I talked with Jacqueline Watts, the learning and insights lead at the Patrick J. McGovern Foundation, about how the foundation funds AI for social good and how they support both grantees and the wider sector in developing and adopting new AI tools.

Our conversation has takeaways for donors, funders, nonprofit leaders, and their teams who are grappling with these questions and how or whether to use AI.

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Barbara Wheeler-Bride:

The Patrick J. McGovern Foundation is a philanthropy that, like the rest of society, is experiencing the rapid onset of AI. The foundation funds and supports nonprofits and social innovators and wants to see them thrive through this technological transformation. How do you put resources to best use to support AI for social good?

Jacqui Watts:

Thanks Barbara first off for having me here. We really think about funding technology and AI as really advancing solutions and creating an ecosystem where AI and technology can be used for social good and used for creating a more equitable and sustainable future for all. So as you mentioned, yes, there's so many concerns out there to keep in mind. And I think we'll talk a lot about responsible AI, you know, what needs to come into play to ensure that it's being implemented in a way that is really strengthening communities. And that is really central to the way that the Patrick J. McGovern Foundation operates both internally, how that comes through in our funding strategies, how it comes through with our grant partner work and, you know, we're looking for furthering a sector that can really remain human-centered in the way that AI is changing and transforming the world that we're in today.

Barbara Wheeler-Bride: 

How does the foundation get clear on its AI funding strategy?

Jacqui Watts:

Our funding strategy really starts with values and starts with thinking about a lot of those responsible AI principles that need to remain foundational to our work and the work of our partners and the work that we hope to see the rest of the sector kind of pushing the boundaries of AI and technology and those values we see as really central — things like remaining human-centered and thinking about fairness, transparency, data privacy, inclusion, sustainability.

When we think about getting clear on a funding strategy, the Patrick J. McGovern Foundation uses what we call a hypothesis-driven grant-making approach. So we really turn those values into more specific ways that we define, you know, what are the potential ways that we think AI could have an outsized impact on the sector. And that's across a number of different domains. But then within a domain, say we're talking about climate, we really craft more specific hypotheses so that way we can continue to learn over the course of our grant-making efforts to see how are we able to move the needle and create the impact that we hope to see in the world. 

And that shows up in our funding strategy by how we have conversations with partners about how they might be upholding those values that we wanna see. If we're talking to a partner who might be working with particularly, you know, sensitive data, we wanna see and learn more about how that partner might be protecting, you know, data ownership of their communities and upholding user consent and things like that. If we're talking to a partner who's building a new algorithm, we want to ensure, you know, how are you building fairness into that algorithm? How are you being proactive about the mitigation strategies that you're using to prevent bias? 

So all of those are ways that the values are coming into the funding strategies and our decisions of which grant partners we might be supporting. And then, you know, we are able to see through our grant partners’ work how those responsible AI principles show up. 

Barbara Wheeler-Bride: 

How does the foundation approach supporting the wider ecosystem of technology and AI in the sector and not just a specific project or organization using it?

Jacqui Watts:

So we have a number of different ways that the foundation thinks about supporting the wider ecosystem beyond just our grant making. You know, our grant making of course is a huge component of what we do; we've awarded over 500 million in grants since 2017, and those, you know, grant partners are doing fantastic work. I'd say from the kind of non-funding perspective, the Patrick J. McGovern Foundation also has invested a lot of efforts in things like capacity building. And so that's an aspect of our work that I focus on as the learning and insights lead. Our capacity building efforts are really geared towards supporting organizations no matter where they might be coming in at in respect to their journey of integrating or using AI for purposes that could be everything from just, you know, internal productivity gains to how do I apply this towards a particular program or to further my mission in a specific way. We really see the learning and insights efforts as a way to support the wider sector in benefiting from the shared learnings that we're seeing through our, you know, many hundreds of grant partners from the work that our internal team is doing and hopefully alleviating the need to have to learn from scratch, and instead being able to benefit from some of those best practices and learnings that have come out of the efforts of a lot of our partners. And so our learning and insights work is one huge area that we've invested in. 

We also have a team of in-house technologists, and our Products and Services team is a fantastic team of data scientists, AI, and software experts that is focused on building technology solutions. So over the past few years they've been investing their time in building a tool for the philanthropy sector to really support a more seamless due diligence process and in the, I'd say, the goals of course building a you know a useful tool for the sector, but at the same time that means that that team can experiment with how technology is changing and evolving and again kind of surface some of the best practices that they're learning to support others in the sector who might be thinking about, how do they stay on top of new technology developments and stay on top of some of these techniques that are constantly evolving. And instead of every organization needing to spend that time to experiment and learn on their own, our products and services team is also aiming to share out those learnings to support, you know, others.

Barbara Wheeler-Bride: 

Can you say a little bit more about the due diligence tool? Is that something that you envision being used by other funders or donors to vet or work with organizations?

Jacqui Watts:

Yeah. So Grant Guardian is a tool that our products and services team has been working on over the past few years to streamline the due diligence process. And it's already making impact in the space. There's over 300 funders who are already using the tool. It really is trying to reduce some of that maybe more manual overhead of the due diligence process, such as pouring through really long 990s and trying to extract, you know, financial information, trying to extract other numbers from various documents, and instead allowing grantmakers to focus on those partnerships with organizations and those conversations with organizations rather than kind of getting mired down in some of the more data extraction steps.

Barbara Wheeler-Bride: 

Thinking about your perspective as the learning and insights lead, what are some of the top insights that you've gleaned from your grant partners in this sector, specifically as it relates to artificial intelligence?

Jacqui Watts:

It's a great question. It really can vary so widely depending on what, you know, place an organization might be coming in at. If they're at that very early stage of thinking about how AI might be used to further their organization's work but they haven't made any first steps, that picture of course looks pretty different from an organization that might be a little bit further along. But for organizations who are at that super early stage, they're not starting, you know, entirely from scratch. Their staff members are already starting to use AI maybe a bit more informally. They as a leader are probably already using AI, perhaps a little bit informally. And there's just a … the gap that we're seeing and that we're hearing from organizations is really, “How do I make this a little bit more intentional of an approach to how AI is brought into the work that the organization is doing.” 

So that's really what drove collaborating with my friends over at the Duke University School of Business to be authoring our piece in SSIR on really what are some of those questions for organizations who are just getting started? And within the Patrick J. McGovern Foundation's learning and insights materials, we've funneled a lot of our resources and learnings and everything into our Learning Hub, is where then if you're an organization who's starting to ask those first questions and trying to figure out, you know, what is that strategy for bringing it into your organization more intentionally? What are the steps that you can start to take? Whether it's, you know, the initial understanding where your organization is at, there’s organizational readiness steps. If they're a little bit further along, if an organization is already starting to identify really promising use cases, and the next steps that they might be exploring is: How do we realize this potential use case? We don't maybe have a huge tech team now. Should we build a tech team? Should we outsource? That build vs. buy question. So there's resources that we've built to kind of align around all these different phases and different questions that an organization might be asking depending on what stage they are at in their thinking of how AI might be brought into their work.

And then, you know, within that framework – we call that the AI Journey framework, from kind of curiosity through impact – our goal is to be able to give some, you know, actionable guidance, again, rooted in case studies of our partners who have navigated those steps, rooted in external resources that others have created that's really quality guidance, some reusable, shareable assets that partners have put out. Maybe there's some open source data sets or open source models that others can benefit from. All of those are different components that we kind of funnel into our learning and insights work and into our Learning Hub.

Barbara Wheeler-Bride: 

Could you give a few examples of what listeners might find if they visited the Learning Hub?

Jacqui Watts:

So some of the areas of the Learning Hub that I think are especially valuable for organizations are around AI in practice. We have a use case library that, you know, folks can drill down into what are other, say, organizations in the education space who have used AI and what are some of those specific use cases? We've written up case studies about partners such as Conservation International and how they went about the organizational readiness steps to more deeply embed AI into their organization strategy. Or, you know, for example how say Earth Genome, a partner of ours, went about building Earth Index, which is a really amazing AI-enabled platform that really uses satellite data and maps out the the earth so that others might be able to use that data and that platform to identify what they're looking for. For instance, if an organization is trying to identify illegal mining happening in the Amazon region, they can go on to Earth Index and use machine learning to identify instances and really zoom in on parts of the world where that's happening. So there's case studies that have helped illustrate, you know, how did Earth Genome go about the product development and getting the user’s perspective as they were building out that AI product. 

Barbara Wheeler-Bride: 

I wonder if you could speak a little bit to some of the concerns or the challenges you hear from your partners who are maybe a little further along in using artificial intelligence. What are some of the challenges they face? Or questions they face? Maybe they've gone through this process of deciding, you know, this is how we will engage, these are the values we will use when we use artificial intelligence, now we're using AI for social good and making an impact. But are you hearing challenges or, you know, concerns from organizations and leaders that are kind of at that stage in using AI? 

Jacqui Watts:

Yeah, definitely. And I'd say a lot of those challenges can still stem from even if you're very clear on your values and responsible AI is underpinning your work, being able to uphold it in practice can still be challenging, especially if you're working within sectors where there's shortages of quality data or just a lack of shared infrastructure to help make that easier for your organization to be able to build a solution and so having to kind of tackle some of those things first of course makes it even more challenging to implement the solution you might have in mind. And so those areas are something that the Patrick J. McGovern Foundation thinks about in terms of our grant making strategies, not just, you know, organizations that are implementing actual AI solutions or or products, but also, you know, what is the wider ecosystem that we can be supporting so that those implementers of AI solutions can move even faster and we can make, you know, even more impact in the space. So there's definitely a number of challenges that organizations run into while trying to implement that are kind of at that more ecosystem level, I would say. I'd say that covers infrastructure, it covers, you know, governance challenges, it covers policy challenges. There's a lot at play in that area.

Barbara Wheeler-Bride: 

The foundation talks about applying principles of inclusion in the use of AI and data. How do you see your partners applying those principles to their work? 

Jacqui Watts:

Yeah, so inclusion is something that we both, you know, think about in terms of the partners that we're working with and how do we make sure that we're reaching underserved communities and that they're not getting left behind in this technology revolution. So it really is critical, as we think about our grant portfolio, to ensure that we're bringing in the voices of everyone across the world and not just, you know, a privileged few. 

The way that that shows up for our partners is often, you know, it makes it very difficult at times to be able to build a solution, an AI solution, that does reach communities that are systemically excluded from being able to benefit from these technologies. And so there's a level of investment that we are really, you know, proud to support. And we've seen, for instance, I've been talking a lot recently with some organizations who are specifically thinking about, you know, how do you reach communities who are from more low-resource languages and make sure that our AI solutions can serve those communities as well. And I've had the privilege to talk to a lot of those organizations as a part of some of our learning and insights work that brings together peer groups around a shared challenge. So reaching low-resource language is a huge shared challenge that many organizations are facing. You know, large language models are especially effective in serving English audiences and maybe a few other languages that it supports relatively well. But the majority of the thousands of languages in the world are not served well – or at all, in many cases – by the AI models that are out there today. 

And so for instance, let's take one of our partners. Digital Green is an organization that builds  tools to reach smallholder farmers with more localized knowledge so that they can make the decisions that are best for their farms and their families. And they have really worked to build out models that support those languages that are not by default supported by the large language models. At this time I believe they support 16 languages. They've fine tuned models to reach their farmers across a number of countries in Africa, India, and I believe Brazil, and they're continuing to grow and make sure that their models are finetuned in a way that is reaching those farmers with knowledge in their own language. 

So that's just one example, but there's a number of organizations that are in that space of thinking about how do we make sure that our tools are reaching everyone and  are not just speaking to English-focused audiences, but speaking more to the global majority.

Barbara Wheeler-Bride:

How do you work with grantee partners on issues like data equity and data governance, especially when they work, as you mentioned before, with vulnerable communities?

Jacqui Watts:
Yeah. So this is definitely an aspect of our grantmaking work, which goes beyond just, you know, supporting organizations in actually building AI solutions, but is supporting grant partners who are thinking about kind of that ecosystem. The, you know, gaps that we see in data for certain domains, the infrastructure that's missing, policy or governance challenges. So in a lot of ways I think about these partners as kind of the unsung heroes. I can touch upon a few of those.   One of the partners I'd love to share who is especially thinking about data equity is CLEAR Global. We've been a partner of CLEAR Global since 2024, and they identified a really huge challenge, especially as these new technologies have evolved, where over 3.7 billion people remain excluded from speech technologies because their languages lack annotated data sets. And that gap in data really, you know, influences those who are from low-literacy communities. That gap tends to be most apparent during crises where reaching those communities through voice tools can be especially impactful. And so CLEAR Global is addressing that by building language data sets, by building shared infrastructure so that aid agencies and governments and health providers and others can communicate with people during crises or in global development efforts, and ensuring that those communities are not, you know, getting left behind in the digital age. 

Some other partners that are thinking about gaps in data as well, but maybe I'll touch upon one who's approaching it from a pretty different way, which is Climate TRACE. Climate TRACE is not actually an organization, they're a coalition of many organizations who really identified a gap in data around greenhouse gas emissions and saw that there was a huge opportunity to bolster, climate organizations being able to act faster if they had the data that they needed. And so Climate TRACE brings together over 100 organizations, universities, and researchers that have collaborated on creating an emissions inventory coming from satellite data, ground-based sensors, and other open data sources. And they have at this point I think 745 million plus assets that they measure that contributes to that inventory. And that really just sets the stage for other climate organizations to be able to build solutions so much faster than they ever would if that inventory didn't exist. 

And then maybe to touch upon an example of infrastructure, you know, shared data infrastructure, Tabiya is a grant partner of ours who thinks about inequities in labor markets for low and middle-income countries. And they build modular open source digital public infrastructure to support, you know, governments or workforce development organizations or whomever in the space is trying to support marginalized youth communities unlock better employment opportunities. And so the types of modular infrastructure that they develop is everything from, you know, taxonomy data framework that can map the capabilities to jobs to translate informal work into the skills that are, you know, map over to formal occupations and the extraction pipelines that, you know, organizations might need to use to extract unstructured text to map over that to that taxonomy. And if they so wish, use the open source models that Tabiya has also published around employment matching so that, you know, it can be then reused in other geographies and other labor markets.

Barbara Wheeler-Bride: 

How do these organizations or others that you worked with think about data governance — or protecting data, frankly — and also just, you know, transparency with the communities where the data is coming from?

Jacqui Watts:

That is a critical part of their work. And it's a critical part for us as a funder. One of the big things of how our values show up in our work and in our grantmaking efforts is to be discussing some of those questions with our partners and ensuring that as they're thinking about the work that they're implementing, how do they go about doing it in a way that is upholding strong data governance practices and policies. And so it really can differ, of course, by the work that a particular organization is doing. But maybe I'll pull from one example a grant partner of ours called HURIDOCS. They are developing tools and strategies to make human rights information more accessible. And as you can imagine, human rights information is particularly sensitive.  That's kind of at the forefront of the types of information you want to have utmost security. And so they take especially strong measures in thinking about how they store and use data and AI models. So their data and their AI models, they use open source AI models that are hosted within the bounds of their organization. They don't use cloud providers, but rather they do on-premise serving of their solution so that they can be in full control of how data is being used or shared.

Barbara Wheeler-Bride: 

So you mentioned earlier, Jacqui, that you co-authored an article in Stanford Social Innovation Review called “A Path Through AI Overwhelm” that features four questions to help leaders clarify their thoughts and concerns about using AI. And we'll put a link in the show notes so everyone can check it out. It's recommended reading, I think, for anyone in the social sector who's thinking about advancing their use of AI, or really any technology. 

You and your co-authors write that for social sector organizations, responsible AI principles consider fairness, transparency, human-centeredness, data privacy and security, inclusion and sustainability – some of the things that you've talked about today. And I would say that the article as a whole emphasizes the idea of using AI and technology as a process and not only thinking about these as programs or technologies that we can kind of add to our work. 

So I wonder if you could share some of the top lessons that you've learned or the organizations you've worked with have learned about this process of adopting AI in their organizations. What have been the takeaways as they go through learning and using AI? 

Jacqui Watts:

That is a great question. I think the biggest thing that we tend to see and, you know, one of the things we tried to get across in this article is that there's no silver bullet. There's no, like, easy way out to implement AI or integrate AI into an organization's work. I know at first it can be a bit overwhelming for leaders to think about how to make those first steps and therefore it turns into “let's put it off until we feel like we have the perfect way forward, the perfect strategy, the perfect AI use case policy, the perfect whatever it might be.” It can feel hard to make those first steps, and therefore inaction tends to be kind of the default. And so I think one of the key takeaways and one of the things that when I hear from organizations who are making really great strides on integrating AI and in their internal efforts, one of the key mindsets to really embody is that kind of learning process. And that learning process extends to even the more formal mechanisms, like an AI use case policy. It can be something that you continue to learn how it's working, where are there gaps in your policy where you see that people need a little bit more guidance, or where are there ways where you can, you know, encourage employees to be experimenting more, or how to go about that experimentation. And all of that can be an iterative process, and encouraging that mindset of it being an iterative process hopefully alleviates a little bit of that fear of action up front as new technologies and, you know, new options become available.

Barbara Wheeler-Bride:  

What guardrails are important to keep in mind for leaders at organizations who are starting to use AI? 

Jacqui Watts:

I'd say one of the most important things that we tend to drive home when encouraging organizations to experiment with new tools — which I think experimentation is a huge part of becoming familiar with the power of the different tools and options that are out there – but the biggest guardrail being, you know, while you're experimenting, hold off from, you know, using any of those real or sensitive data sets. Hold off from really plugging in a lot of your organization's, you know, internal information, and experiment with something that really can be what's the information that's already out there in public that you don't mind being publicized. Or what's some, I guess, sample information or sample data that's not real, but it kind of mimics what your actual data might look like and using that as a starting point for your experimentation. 

And really, you know, as you're getting further along in a tool that you might be working on, or a workflow that you want to build for using AI for your, you know, internal productivity, as you get further along and do want to start using your actual information, your actual organization's data, that's a perfect time to start looping in more technical team members if you have those folks available for you. Or if you don't, what external, you know, support can you look to to make sure that you can implement those those solutions in a way that is upholding, you know, data privacy and security that is going to ensure your solution isn't going to inadvertently leak information or harm your communities by sharing data when you didn't mean to share data. 

There's a balance there behind wanting folks to experiment and learn quickly and all of that with also upholding strong data privacy considerations. And so as you get further along in, you know, prototyping, “Hey this workflow actually is gonna be really useful. And I've validated that by some sample data and, you know, I have a way forward that I'm excited about, now here's the time to start looping in some more technical expertise to make sure I can do it safely.”

Barbara Wheeler-Bride: 

I wonder if you have any takeaways or advice for donors who might be listening and thinking about how they can fund or work with an organization they support in terms of utilizing AI.

Jacqui Watts:

I think a few things come to mind when I think about how donors could best support organizations in using AI — both some non-funding-related ways, but also some funding-related ways, and maybe I'll touch upon both there. 

In terms of non-funding-related ways, I mean, I've talked a little bit already about capacity building, and so I think that's a huge one, where organizations feel like, “We don't have the internal capacity or our staff need some more training. We need some leadership guidance on, you know, what's the best way for our organization to navigate what that journey might look like.” As a funder, you often have a little bit more of a 30,000-foot view of how many organizations are doing things, and so that can be really a valuable perspective to bring to the table and and seeing kind of what's been working well for organizations in your network vs. where some of the pitfalls that that they're running into. And so capacity building is a way to really support organizations with their journey. 

Technical support is another way, if you as a funder do have in-house technical expertise. I know that isn't always the norm. Patrick J. McGovern Foundation might be a little bit more unique in that way, but to the extent that even if you don't have it in-house, but you have connections that you can bring in to support the organizations that you're funding, to help give them, you know, a sounding board as they're navigating, say, a really important technical decision that might have a lot of downstream effects, and that organization wants to get some input to make sure they don't have any gaps in their thinking. Or maybe they need to be pointed in the direction of some best practices or overcome some specific hurdles of their implementation. So that's another great way to support organizations. 

And then kind of talking again about the networks that you have as a donor. If you see that there's some shared challenges across organizations, you hear from one partner that they're working on a similar type of AI adoption challenge as another, making those introductions and really encouraging cross-pollination of ideas for what one organization has tried and has or hasn't worked, and reducing the need for those two organizations to both have to learn on their own or duplicate efforts. 

And then in terms of, like, more funding-related ways of supporting organizations, I think there are definitely some differences in the way you might go about your typical grantmaking strategies that look different for funding technology, if funding technology is new for your organization. I don't think it's a surprise to anyone that technology right now and AI is changing super quickly, and so allowing some of that space for learning, experimentation, allowing your grant partners to be a bit more agile with how they're, you know, trying to achieve the outcomes they're hoping to achieve. So instead of asking for multi-year fixed roadmaps and specific, you know, implementation milestones, that might have to look a little bit different for implementing an AI solution. Always staying rooted in the same outcomes that you're hoping to achieve, but you know, the underlying nuts and bolts of how they implement a technology is probably gonna change from now to two years. In fact, it's definitely going to change. The ways that you might go about monitoring and evaluation in the same vein might, you know, have to evolve a little bit to account for it being a technology product. 

And the other thing I might highlight: One of the best ways I think donors can support the organizations in their network in terms of AI adoption and potentially using AI for either internal or mission-driven work is really making sure and holding those partners accountable to starting from a problem-first approach and not, you know, just chasing tech for the sake of chasing tech. Really helping them to uphold that, you know, starting with: Who are the communities that they're serving? And is this a real problem that they're facing? And making sure that any solutions that they're proposing and that you're funding are upholding that as the starting point in keeping people at the center of their work.

Barbara Wheeler-Bride: 

Jacqui, thank you so much. Although I feel like we've probably only scratched the surface, because as you said, there are so many technological advances happening in the field. But I appreciate you shedding some light on how the Patrick J. McGovern Foundation approaches funding AI and also offering your insights and also some insights from your grantee partners that will help improve how technology and AI is used in the social sector to really improve people's lives. That's the bottom line. So how do we kind of shore the work up with values and processes that are going to benefit the communities that organizations serve in the end. So thank you for talking with me.

Jacqui Watts:

Thank you so much for having me, Barbara.

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Barbara Wheeler-Bride:

You can read the article Jacqui co-authored called “A Path Through AI Overwhelm” in Stanford Social Innovation Review. We'll link to it in our show notes.

If you enjoyed this conversation about funding AI and want to read more about how other funders are thinking about the technology and how nonprofit leaders are adopting AI in their organizations, please subscribe to Stanford Social Innovation Review. As Jacqui said, AI solutions move quickly, so subscribe now to stay up to speed with the latest examples and thoughtful viewpoints on adopting AI in the sector. And a reminder to please subscribe, rate, and leave a review of Giving with Impact wherever you get your podcasts. This helps people discover the show. Next week, a conversation about place-based funding and economic development in the Southland of Chicago.

Giving With Impact is produced with support from Jordan Turgeon, Marcel Malekebu, and Bryan Maygers. A special thank you to DAFgiving360^® for their financial support to make this podcast possible. DAFgiving360^® is an independent 501c3 with a mission to create charitable giving in the United States. DAFgiving360® provides the tax-smart and simple giving solution of a donor-advised fund, or DAF, account. Visit DAFgiving360.org to learn more.

Stanford Social Innovation Review is published by the Center on Philanthropy and Civil Society at Stanford University. Visit SSIR.org to learn more and subscribe today.

Giving With Impact is hosted by Barbara Wheeler-Bride and produced with support from Jordan Turgeon, Marcel Malekebu, and Bryan Maygers. Artwork is by Peter Grant. Thank you to DAFgiving360 for their financial support to make this podcast possible.

DAFgiving360 is an independent 501(c)(3) public charity with a mission to increase charitable giving in the U.S. DAFgiving360 does this by providing the tax-smart and simple giving solution of a donor-advised fund (DAF) account to donors and financial advisors. Since our founding in 1999 as a 501(c)(3) public charity, DAFgiving360 donors have recommended over $50 billion in grants to 300,000 charities. Visit dafgiving360.org to learn more.