Like podcasts? Find our full archive here or anywhere you listen to podcasts: search Community IT Innovators Nonprofit Technology Topics on Apple, Spotify, Google, Stitcher, Pandora, and more. Or ask your smart speaker.
Carolyn Woodard explores responsible data governance and AI realism with Jim Fruchterman, MacArthur Fellow and founder of Tech Matters, a tech-for-good nonprofit building open source software for the social sector. Jim’s work sits at the intersection of two urgent questions every nonprofit is wrestling with right now: what do you owe the people whose data you collect, and how do you make smart decisions about AI without getting swept up in the hype?
Jim introduces the Better Deal for Data, a new data governance movement built around seven plain-language commitments nonprofits can make to the communities they serve. The core idea: you don’t own the data of the people you serve, tech vendors may be extracting it right now without your knowledge, and a basic data safeguarding policy should be as standard as a child safeguarding policy. He also explains why you can’t build a responsible AI governance policy without first getting clear on your data governance.
Then the conversation shifts to AI strategy, where Jim draws on decades of experience as an early AI entrepreneur to offer a genuinely grounded take. Gen AI fails in nonprofit program delivery about 80% of the time, and that’s still a much better track record than blockchain or the metaverse. The 20% that works tends to share a common thread.
Jim Fruchterman is a leading social entrepreneur, a MacArthur Fellow, a recipient of the Skoll Award, and a Distinguished Alumnus of Caltech. His life’s work is applying technology to benefit the 90% of humanity typically neglected by for-profit tech companies, by building the tech and data for good movements and launching nonprofit open source software enterprises.
In 2018, Jim founded Tech Matters, as a tech for good nonprofit. Tech Matters builds technology for social good movement, helping social change leaders use tech to achieve impact at scale. Tech Matters has built Aselo, a shared modern contact center for crisis response helplines; Terraso, software for smallholders and locally-led sustainability initiatives responding to climate change; and the Better Deal for Data, a data governance movement.
His first book, Technology for Good: How Nonprofit Leaders Are Using Software and Data to Solve Our Most Pressing Social Problems, was published in 2025.
Through his work as a trailblazer in the field of social entrepreneurship, Jim continues advancing his vision of a world in which the benefits of technology reach all of humanity, not just the wealthiest and most able ten percent.
Carolyn Woodard is currently head of Marketing and Outreach at Community IT Innovators. She has served many roles at Community IT, from client to project manager to marketing. With over twenty years of experience in the nonprofit world, including as a nonprofit technology project manager and Director of IT at both large and small organizations, Carolyn knows the frustrations and delights of working with technology professionals, accidental techies, executives, and staff to deliver your organization’s mission and keep your IT infrastructure operating. She has a master’s degree in Nonprofit Management from Johns Hopkins University and received her undergraduate degree in English Literature from Williams College.
She was glad to have this conversation with Jim Fruchterman about the nonprofit better deal for data pledge.
Community IT has been serving nonprofits exclusively for twenty-five years. We offer Managed IT support services for nonprofits that want to outsource all or part of their IT support and hosted services. For a fixed monthly fee, we provide unlimited remote and on-site help desk support, proactive network management, and ongoing IT planning from a dedicated team of experts in nonprofit-focused IT. Our clients benefit from our IT Business Managers team who will work with you to plan your IT investments and technology roadmap if you don’t have an in-house IT Director.
Being 100% employee-owned is important to us and our clients. It is an important aspect of our culture as a business serving nonprofits exclusively for 25 years.
We constantly research and evaluate new technology to ensure that you get cutting-edge solutions that are tailored to your organization, using standard industry tech tools that don’t lock you into a single vendor or consultant. And we don’t treat any aspect of nonprofit IT as if it is too complicated for you to understand.
We think your IT vendor should be able to explain everything without jargon or lingo.
If you can’t understand your IT management strategy to your own satisfaction, keep asking your questions until you find an outsourced IT provider who will partner with you for well-managed IT.
More on our Managed Services here. More resources on Cybersecurity here.
If you’re ready to gain peace of mind about your IT support, let’s talk.
Carolyn Woodard: Welcome everyone to the Community IT Innovators Technology Topics podcast. I’m Carolyn Woodard, your host, and I’m really happy today to welcome a new guest on the podcast, Jim Fruchterman. So, Jim, would you like to introduce yourself?
Jim Fruchterman: Sure, Carolyn. So I’m a tech nerd, a serial entrepreneur from Silicon Valley. I used to start the for-profit kinds of tech companies. I started seven in a 12-year period, and only five failed. And the two that succeeded were both what we now call AI companies. Back then we called them pattern recognition or machine learning.
We raised a lot of money for this startup from venture capitalists, and I created a new product that was going to help the blind and showed it to our board, and they vetoed it on the spot because the market for reading machines for the blind was a million dollars a year, and they’d put $25 million in the company and they just didn’t see the connection.
So ever since that board meeting, I’ve been trying to make the connection between tech and social impact, because there’s an awful lot of great ideas that are possible with tech, but they’re not sufficiently profitable to make venture capitalists excited.
So for the last few decades, every two or three years, I start a new tech for good company inside a charity to build the product that Silicon Valley won’t bother to build because they can’t make enough money off of blind people or people in Zambia or human rights activists or, you know, I don’t know, 90% of humanity and the planet probably don’t make enough money for the average tech company, the way our systems are set up.
Carolyn Woodard: Exactly. And I think you and I met when we were at the Good Tech Summit in DC a couple of months ago. And you talked a lot at that summit about some of your new things that you’re working on. So I wanted to ask you: what are you working on now?
Jim Fruchterman: Well, I mean, traditionally I do tech products. So probably the best known one I’ve done is Bookshare, which is the National Digital Library for Disabled Kids. So if you’re dyslexic or blind and you need a book for school, we’ll make it into an ebook that’s accessible.
Our two biggest projects right now are Aselo, which is like the contact center platform, open source, kind of like 911 for kids in 20 countries around the world, including the National Child Abuse Hotline here in the US.
Tarasso is: what if people on the front lines of climate change actually had someone building software for them? So we have an open source soil identification tool.
We have an open source version of story mapping, which is how do you take map data and other data and pictures and videos and make a compelling story about, I don’t know, why you should visit someplace that has ecotourism, or stop doing something terrible, or invest in them, whatever it might be.
But in the last few years, I’ve been spending a greater and greater percentage of my time on just advancing technology for good generally. And that shows up in the form of a podcast, of course, where I interview great tech for good leaders.
I just wrote a book called Technology for Good, which is the first book on how to start a tech company and not make money. So it’s aimed at the person who wants to start a nonprofit tech enterprise, either inside their nonprofit or from scratch as a software company or a hardware company.
I talk about 60 different tech for good nonprofits who’ve put technology at the core of their program delivery. In other words, they have some innovation, but the technology makes it possible to go to scale. And of course, I also talk about the many examples of failure in our system, because that’s the nature of tech.
Earlier this year, we launched a responsible data governance initiative called the Better Deal for Data. One of the things that people were asking us about is: what are the promises that nonprofits make to vulnerable communities about the data they collect? And the answers are generally none. We work with kids. No one who works with kids can get funded by a responsible foundation unless you have a child safeguarding policy.
Why on earth are we working with vulnerable people as a sector and no one thinks about a data safeguarding policy or constraining the data use? Maybe don’t sell the data of poor people to the fintech industry so they can squeeze them harder.
So the idea of the Better Deal for Data is just seven one-sentence commitments that I think would make sense to any nonprofit leader. These are all in the form of promises the nonprofit makes to their stakeholders: the people we serve (whether we call them beneficiaries or users or stakeholders), donors, and employees. We’re handling all this data.
First, the promise is: it’s your data, it’s not ours. We don’t claim ownership in it. If you ask us to delete it or correct it or transfer it to you, we will. If we do research on your data, we’ll anonymize your data and you get a free copy of the research rather than having to pay for it. We’re primarily collecting this for social good reasons. In other words, that’s our primary purpose. We’re here to help you and your community, maybe science, humanity, and the planet, but we’re not here to help private interests. We’re not here to make a buck.
We will not sell your data to the meta 250,000 companies that are all spying on us. And anyone who touches your data will make a legally binding promise to the above.
Carolyn Woodard: Interesting.
Jim Fruchterman: Yeah. And so I think the average leader would go, no, it seems reasonable. I mean, we’re a nonprofit. Why on earth would we sell out the people we serve?
But the tech companies, unbeknownst to a lot of nonprofits, are stealing the data of our stakeholders right under our nose. I mean, there are survey tools that copy everything that anyone enters into your survey. There are donor management tools where you take a donation from someone and they say, oh, Mary Smith, let’s add her. Oh, we already know about Mary Smith. Let’s now enhance our database and sell this to other people.
I think the whole thing is: people expect that nonprofits will collect data about them. People expect that nonprofits raise money by citing their impacts. People do not expect to be sold out. So a lot of it is around how nonprofits can be as trustworthy as nonprofits should be, and how to not surprise people. Our idea is: if we tell you what we’re doing with your data, you should go, oh yeah, that makes sense, as opposed to… “you’re what?!”
Carolyn Woodard: And then it seems like this is such an obvious set of principles to sign on to and to help develop your governance of that data and those databases. Do you have additional supports for nonprofits who may be struggling to have security around their database, or with which vendors they’re using, as you said, that will or won’t be spying on the data?
Jim Fruchterman: Well, I think our goal, I mean, this is brand new, so in tech terms we’re beta testing this. The early adopters are asking us questions. I mean, people are saying, hey, it says you’re not supposed to monetize the data, but of course I raise money based on my data. And I’m like, well, based on your aggregate data, right?
I think the fact that you served 4,700 people last year is not sensitive. But just don’t name them all by name, right? That would be bad.
So we’re busy working through these explanations. People are worried that it’s complicated. I’m trying to come up with an “adopting the Better Deal for Data in two hours” kind of thing, because the average community-based organization is not doing terrible things with your data. And we don’t care if you use Google Analytics. We’re not being that kind of picky.
We’re just saying: you’re serving kids, so you should safeguard the data of kids. You’re serving domestic violence survivors, you’re serving immigrants. Maybe be really careful. Maybe don’t even collect their immigration status, because the best way to protect them is just not to have the sensitive data.
If it takes off, which I hope it does, then we’re going to do more around certification. People say, oh, can you certify us? Like, look, we’re one and a half people working on this. Let’s not get ahead of ourselves on this. But I think the next year we’ll get a feel for this and then decide: are we helping people adopt this? Are we going to be supporting a bunch of consultants the way a lot of consultants exist to help you comply with standards?
But my dream is that the average community-based organization who’s never talked to a lawyer can look at these things and say, yeah, we can agree to that. We’ll put it on our website that we don’t sell your data, that we comply with the Better Deal for Data. And I want to make that possible.
Whereas someone who’s a giant international NGO collecting 15 different kinds of data, if they sign up for this, they’re going to have to actually think about all the things they’re doing with data. That’s a longer process.
Carolyn Woodard: But they have lawyers.
Jim Fruchterman: Yeah, they do have lawyers, policy people. But we’re also suggesting that people endorse the standard. You know, this is a good idea. Maybe we’ll adopt it later. I think it’s a signal around rejecting the surveillance capitalism sort of system that we have.
Over time, I do want to put pressure on tech vendors. Let’s say we work with child helplines: that’s one of our major areas. I want the International Association to say that all child helplines should adopt this policy as part of their professional obligations. And then anyone who wants to sell to the child helpline movement has to not steal their data.
I think that individual nonprofits have a hard time negotiating with big tech. But I think our sector and our donors have more leverage if we act together.
I mean, if you want to use a survey tool, there are plenty of survey tools that don’t steal your data and sell to you based on the fact that they won’t steal your data. You should use them, as opposed to the people who are mysteriously silent and just refer vaguely to a 75-page terms of service that on page 67 claims all your data and your firstborn.
Carolyn Woodard: Right, right, right.
Jim Fruchterman: Because they can, you know.
Carolyn Woodard: So I love that this seems to be tapping into this bigger question about the ownership of the data and your right to own your own data, which I think has been percolating in Europe for a while. It seems like it’s gaining some ground here, that data ownership is a justice issue. And one that foundations and philanthropy have kind of quietly extracted insights from, in ways that weren’t transparent to the populations or the communities they were working with. So it feels like a good moment to be starting this pledge.
Jim Fruchterman: Yeah. And we have, let’s pick indigenous communities around the world. They’ve been on the short end of colonial practices for a little while. And they have really good data governance standards. So our dream of the Better Deal for Data is not to replace indigenous data governance. They’ve got that down.
It’s to say to the nonprofits that work with them: honor their data governance structure. If you don’t steal their data, then you’re probably honoring their existing governance structures.
But yes, Black and Brown communities are worried about how data is used against them because it’s been used against them for a long time. So this is kind of familiar territory. And I just came back yesterday from Africa, and more and more countries are saying we don’t want to have all the data on our citizens being held by big American tech companies. And they’ve got a pretty good basis to be skeptical of why this is good for them.
Carolyn Woodard: I think that was going to be my next question. We’ve seen a lot of our clients and nonprofits coming back to think about data policies, data retention, privacy policies because of the advent of AI and AI search. Maybe this is something that we’ve been saying we should do for like a decade, but now it’s become really urgent. Could you talk a little bit about how AI interacts with this data question?
Jim Fruchterman: Yeah, I think you’re right in identifying that data should have been something that nonprofits are dealing with. Nonprofits should be good with their data. Nonprofits should not be using data in bad ways.
But because AI basically feeds on data, and it has this gigantic appetite for data, a lot of the value you get out of AI relies on feeding it data. So people are often talking about: what’s our AI governance policy? But I think you can’t do an AI governance policy without having a data governance policy. They kind of go hand in hand.
And of course, the Better Deal for Data, we have the seven one-sentence commitments, but there’s a few thousand more words about what this means and doesn’t mean. For example, one of the things is you can’t comply with the Better Deal for Data and feed confidential client data into a free version of OpenAI where they’re going to keep a copy of your client data and train on it and make commercial money off of it. That’s not okay.
So I think people are more concerned about AI governance because of their worries about AI. But we need to be working on both of these tracks: let’s do data and AI governance, and let’s keep our eye on the idea that we’re going to be doing better with both.
One of the dreams I have is that, you know, we’re a data organization, we’ve worked with a lot of sophisticated nonprofits, we’ve negotiated data sharing agreements, and that takes lawyers and takes a year or two. My hope is that if you have, let’s take 10 agricultural co-ops or 10 nonprofits working in housing in a metro area, if they’ve all adopted something like the Better Deal for Data, they could pool their data for social purposes. They could say, now that we have enough data, we can actually build a better AI model of climate smart agriculture. Or: what are the leading indicators of someone who’s going to be successful in transitioning out of temporary housing into long-term housing?
Because the challenge around AI is that five big companies have data on a billion people and they’re part of an ecosystem that is constantly vacuum cleaning up all of this data.
The nonprofit sector: all the data is in a million tiny silos. And how do we actually unleash that, not for profit, but for social impact, so we can build better AI models?
I mean, many nonprofits would love to know how they stack up against their peers, not what specific peers are doing, but benchmarking themselves. These are all things that we could do if we had a rational, basic set of data governance structures that say, yeah, doing AI for good, sure, as long as it respects the confidentiality of the people themselves. Or combining data sets so you can get a better handle on what’s going on in your metro area, sure. These are the sorts of things that business is already doing in search of the buck. Why aren’t we doing this in search of more social impact?
Carolyn Woodard: Do you have any examples? This seems like something that, say, a foundation has been working in education, so they have grantees that have a lot of data. Are there situations where they have done this kind of aggregating of data from different silos that could be a model? Or do you imagine it’s like a virtual database with security all around it, and you put your data into the data bank and then you get to ask it questions about the other data that’s in there?
Jim Fruchterman: Oh yeah, we have tons of models doing this sort of thing across the social goods sector.
But I would say if we go further up, science has been doing this pretty well for years. If I’m a young astronomer, I don’t have to get time assigned on the space telescope. I have the last 10 years of space telescope data available for me to mine and try out. So in the social goods sector, the gold standard is in medicine and public health. We have a long tradition of collecting data, measuring impact, and safeguarding the identity of individuals in the health system. In the US, this is called HIPAA, our medical privacy law. It’s an older law, it’s got its challenges, but fundamentally the social bargain is: I disclose something very confidential and sensitive, I get help from a doctor, it’s kept secret, and my data gets used in medical research. And by and large, people are happy with that social bargain. They think that’s a fair bargain.
Well, how do we do that in the nonprofit sector? So I’ll pick an example. The Gates Foundation, to pick a big example, said: if we’re paying for this data collection, you’re going to make that data available, not openly if it’s confidential data, but if we want to do a bigger study.
I remember talking to someone from Gates not too long ago where they had actually paid for a whole bunch of studies about stunting, which is where when kids are malnourished, they don’t grow as tall, they don’t grow as smart, because as a kid it’s a very sensitive time to be starving when you’re young.
One of the questions was: when is the critical period to intervene? Is it any time in zero to three, or is zero to six or nine months more critical? And it turns out by doing this big study across a bunch of their grantees, zero to six months turns out to be really crucial. You want to make sure to intervene there. Certainly, starving when you’re a two-year-old is bad, but it’s not as bad as starving as an infant.
That was something where the individual studies couldn’t have picked that up, but by combining them. And something that a lot of people don’t know is there are gigantic medical databases held at our major research universities under pretty tight control, but you can access brain scans and DNA of individuals in this very tightly controlled sandbox where you don’t get to figure out who had brain cancer. You just get to figure out whether your research thesis works or not and get the answer. Because you don’t need the individual’s data, but you do need the raw data.
So I’ll give you a last example, and this is something you should be familiar with, the battles between different kinds of vendors. I’ll simplify it as Apple versus Google. Google’s tracking your every move. If you have an Android phone, it’s very hard to turn off that tracking everywhere.
Apple doesn’t actually track your individual movements. What they do is they send a scrambled version of your location data. So they can do the same kind of analysis of, say, commute times, but they aren’t tracking Joe average and exactly what path he’s taking. Because they’re doing the AI on the scrambled data.
And this is called federated learning. Google also does it in many areas. But the idea is that we can get the benefit of mass data collection without actually having a detailed dossier of your data. It takes more data to do AI that way, but a lot of people are a lot more comfortable. And Apple is selling you on that as opposed to their competitors.
Carolyn Woodard: Oh, that makes sense. And I would be so interested to see what happens with this idea, thank you for explaining it so clearly, of taking that kind of medical or research-based database, but actually making it accessible to nonprofits’ data to be used in that way, anonymous but valuable.
Jim Fruchterman: Yeah, or under very tight legal controls. That’s another way you can solve this.
Community Solutions, who famously won the MacArthur 100 million and change challenge, they’re working on homelessness. And one of the things they do is that rather than measuring beds or meals served, kind of measuring the outputs, they’re trying to say: is everyone who was unhoused in our community three months ago, are they housed now? Well, that requires tracking individuals through the system, through a whole bunch of organizations, because a homeless person might touch 10 different agencies in that three month period.
So they have to constrain what the data is used for, but actually working on reducing homelessness to what they call functional zero is a much better way of doing it than just measuring the outputs of the homelessness industrial complex.
Carolyn Woodard: I want to pivot just a bit because I know that you are known as a bit of an AI skeptic. So I wanted to get your take on AI hype and nonprofits in this moment, because we’re seeing and hearing a lot of, you know, there are some really big grants coming out for big projects to totally transform your nonprofit doing this and that with, you know, Google or what have you. So what is your advice or your take on AI and nonprofits right now?
Jim Fruchterman: I realize I started AI companies in Silicon Valley in the 1980s as a very young person. And I’ve been doing AI for social impact ever since. So I am an AI enthusiast.
What I am not enthusiastic about is AI moronic hype. In my book, I use the example of the National Eating Disorders Association, which fired all their human counselors and replaced them with a chatbot, Tessa, the AI-powered chatbot, who was found to tell someone to count calories within three days of launch. And they had to shut it down because counting calories is the last thing you tell someone who’s got an eating disorder.
So people have bought the hype and then just cratered big time. The thing though is, and I’ll contrast this: I say 80 or 90 percent of AI projects in program fail. Again, I’m talking about program, the most sensitive part of a nonprofit is program. I don’t care if you’re using it to write a better grant application. Spell checker on steroids, go for it. You’re probably not betraying confidential data, probably not the biggest thing to worry about. But in program, 80 or 90 percent of the time it fails. And people say, oh, you’re a skeptic. And I’m like, no: blockchain failed 99.99% of the time, metaverse failed 99.99% of the time. Gen AI failing 80% of the time means a 20% success rate. That’s huge. That’s great.
So my job is to help people, because it’s so common for people in the nonprofit sector to buy what the tech industry is selling and then go, wow, we built an app and no one downloaded it. We did the blockchain solution and I spent a lot of money and it didn’t work.
So when we come back to Gen AI, because that’s what people are talking about when they say AI, they mean the latest generation. But good old-fashioned AI, which has been with us in some shape or form for 30 or 40 or 50 years, that works pretty well at certain things.
And the challenge we have with Gen AI is that in the old days, you would measure the accuracy of an AI tool on how accurate it was. We’re really used to Alexa being wrong 20 or 30% of the time. Matter of fact, we’re highly trained as humans to make up for Alexa or Google or whatever. There are limitations, and we know how to reframe and maybe get a better answer the second time.
But now with Gen AI, its job is to make up stuff. And you’re like, well, how accurate is it? And it’s like, accurate? No, it made up some stuff. It’s successful by definition as long as it’s spewing out words that are vaguely connected to what you asked it for.
Facts, it doesn’t know about. Empathy, it can fake empathy if you tell it to fake empathy, but it’s not actually empathetic any more than a rock is empathetic. And so people have this illusion that just because it spews out well-written materials that’s very compellingly argued, it knows what it’s saying. But it has no idea. So when it argues for something that is completely wrong, it’s super convincing when it’s really wrong. And it’s trained to be sycophantic.
They’re not… we’re busy working through these limitations.
I famously two years ago did a nonprofit AI treasure map. And the fundamental advice was: your average nonprofit should not be trying to do AI research or product development themselves. Because the average nonprofit doesn’t have any data people or any software people. Basically, wait for a product.
And then if there is a product, talk to your peers. Did you raise more money using this AI-powered fundraising tool than you irritated by sending out AI-drafted pitch letters? People are busy figuring out what are the 20% of the things that work.
Right now, the ones that I get enthusiastic about fall into two rough camps.
One is humans using a Gen AI tool to become more powerful and more effective. In other words, it saves them more time than they spend correcting its errors, because the people who are really successful at these things, by and large, are keeping humans in the loop. Don’t send that proposal out until you’ve actually checked that it doesn’t claim something you can’t deliver, or conjures up a non-existent spouse for this donor, all the stupid stuff that it just does. But if you can actually save more time than you spend correcting the errors, great. Or you have a lot of work to do to try to get the AI to operate unsupervised. That is a huge task.
Now, there are organizations that have successfully done this, and they’re all using a common piece of technology called RAG, which is short for retrieval augmented generation. You know, the LLMs are trained on the average of the internet, which is why if you ask it for weight advice, you might get calorie counting, because the average of the internet can be really bad.
So what you do is: before the AI answers, you say, hey, my nonprofit has 5,000 questions that expectant mothers have asked. And here are 5,000 answers written by health educators. Before you answer the question, remember you’re pretending to be the person who wrote the 5,000 answers. And if the question sounds really close to one of those 5,000, serve up the canned answer. And if you can’t figure out what the question is, don’t make up an answer. Hand it off to a human.
MomConnect, which is the number one maternal advice hotline in South Africa: about half of their questions get answered by the chatbot based on it being very close to a question someone has already asked and been answered, and it’s available in all eight of their major language groups. And half of the questions go to a human because the AI didn’t understand it, or this woman is asking three questions simultaneously, and a human needs to have the judgment of saying, that third one is the one that’s going to send you to the clinic right now. We’re not even going to talk about the first two. The AI is not that smart to be able to do that kind of thing at this point.
So when I talk to my friends in Silicon Valley, everyone says, oh, RAG, that’s so three years ago. You should be using agents. And I’m like, no, nonprofits should not be running off into the agent world. Why? Because the for-profit world hasn’t figured out how to make the agentic AI thing work, even though they’re all selling it like crazy.
I just say: let them spend a few trillion dollars over the next couple of years, and then we’ll find out what agentic AI is actually good for. And then the nonprofit sector can be two or three years behind the times, not waste billions of dollars. And frankly, if we can get the nonprofit sector to be two or three years behind, we’ve probably just moved them forward five or ten years.
I guess my last point is: the industry is there to make gigantic money. And there’s gigantic money to be made. But if you’re running a nonprofit, you’re not about profits, you’re about social impact. You have a do-no-harm sort of standard.
The for-profit companies: harm, screw it, we’re making money. Harm’s okay. We’ll outsource the harm to the nonprofit sector to fix.
But as a nonprofit, we have a higher moral and ethical standard, which is we’re here first on behalf of the people we serve. And that means we have to be really careful that we’re not unleashing bad AI on good people who need help and are not getting it from the AI.
Carolyn Woodard: That’s a lovely place to land on, I think. That’s a great summing up of nonprofits and AI. Thank you.
Jim Fruchterman: Oh yeah. Well, thank you, Carolyn.
And the other thing I’d like to mention is that my book goes open source in September. So yeah, it’s obviously available in hardcover everywhere, but there’s going to be a free e-book version starting in September, one year after the original publication date. And I’m hoping that more people are going to get the benefit of some of the stories of these great nonprofits that have learned how to use this, and also the stories of where people have failed, because I think we all learn by hearing stories of our peers.
Carolyn Woodard: Yeah, no, that’s great. And I will include all of these links in our show notes as well. So if you’re listening and wondering where you can get that book without waiting for September, we’ll have that information for you. I just ordered your book. I should have waited, but I remember you talking about it, and I was preparing for this today and I was like, I need to read that book.
Jim Fruchterman: Well, thank you. I mean, there aren’t that many books on nonprofit tech in general. And my goal was to come up with a book about how to do the team building and the product and the revenue generation and the fundraising, the whole package of how to do it, as opposed to something that’s aimed at most nonprofits who are just trying to figure out how to use it. Which is, I think, a bigger market. But
When I started the first 10 years, I didn’t know any other tech for good organization. And now there are thousands of organizations that have tech teams. Because if you’re ambitious about social change, you’re probably using technology to help a million people. Software and data is probably going to be involved in there somewhere.
Carolyn Woodard: Well, Jim, I just want to thank you so much for sharing your time with us today. It was really just a delightful conversation, and I feel like I learned a lot.
Jim Fruchterman: Well, thanks for having me on board, and I hope people go out and do great things with tech for good.
As advocates for using technology to work smarter, we’re practicing what we recommend. This transcript was drafted with the assistance of AI, and is not a verbatim transcript. The content was edited for clarity, and was reviewed, edited, and finalized by a human editor to ensure accuracy and relevance.
Photo by Katherine Hanlon on Unsplash
Wednesday July 15th at 3pm Eastern join Mimi Yeh, PTKO, and George Danilovics, AHIP, to learn your next AI steps.
Fill out the form below to request a quote. We’ll be in touch shortly to discuss your needs and take the first step toward better nonprofit IT.