Name: AI Policies for Grantmakers: How to Manage Risk and Harness AI for Good
Uploaded: 2024-08-29T16:52:06.755Z
Duration: 59 min 24 s
Description: AI Policies for Grantmakers: How to Manage Risk and Harness AI for Good

Transcript for "AI Policies for Grantmakers: How to Manage Risk and Harness AI for Good": Okay. Hi. Hello, everyone. And welcome to today's episode of Blackbaud Grantmaking's 2024 thought leadership webinar series. We will begin here in just a few moments, but, I wanna go over a few quick housekeeping items, while everyone is getting a chance to get logged in. The webinar audio today will be broadcast through your computer speakers or headset if you're wearing them. So hopefully, you can all hear me now. If you need captions, you can hover over the stage area that we're on and click on the CC button at the bottom of the screen to turn the captions on and off. If you encounter any audio or technical issues such as the slides freezing, usually a quick refresh of your browser is the best way to get things working again. Worst case scenario, you can actually just sort of leave the site, log out, log back in, and that takes care of about 90% of what could be giving you a problem. Please click on the q and a at the top right to submit any questions that you may have. We've reserved some time at the end for questions. We may attack some of those as we go along. We may not. So never fear. I've seen your questions, and I'm holding them for the end so you don't have to put them back in again if we don't get to it straight away. And, next to the q and a, you'll see a document section. So this has got a link some links to, the blog post that is the companion piece to today's webinar, as well as some other resources we thought might be interesting for you. You can adjust the settings by using the little cogwheel at the bottom of the event space, for this presentation at any time, and you can use the tabs on the top of the event space to see today's agenda and to meet your speakers. I also always like to point out that everyone is gonna get an email with a link to an on demand recording of today's presentation, in about 24 hours. So feel free to make notes as we go along, but don't feel like you gotta write down everything you hear because you're gonna get that copy and be able to reference it back later. So welcome everyone to today's presentation. My name is Ray Borkman, and I am part of the grant making marketing team here at Blackbaud. And I'll be introducing the person you really wanna hear from shortly. But, I am a white person who identifies as male. I use he, him pronouns. I am about 47 years old. I'm actually exactly 47 years old. I have no hair. I have a beard that is graying a good bit. I'm wearing a blue shirt. And I'm here, in Charleston, South Carolina in my little loft office. Charleston is located on the native lands of the Etowah, Kiowa, Edisto, Santee, and Wasamasa people. So really, really quickly, just a, introduction to Blackbaud for any of you who may not be as familiar with us. We are the world's leading Cloud software company, powering social good. And a couple years back, we celebrated our 40th anniversary. So we serve nonprofits, educational institutions, local governments, and other entities all across the social good sector by providing tailored solutions designed for the needs of all types of noncommercial organizations. So we offer software, technology services, data intelligence, and of course, our expertise across all of our solutions that range from financial management to fundraising and, of course, the sponsor solution for today's webinar, Blackbaud grantmaking. So your organization may be managing your grantmaking efforts in a variety of ways. Right? Manual spreadsheets, paper based review processes, or multiple software systems doing a variety of tasks. Right? But Blackbaud grant making is built on a powerful core that integrates all the components you need to successfully reach your giving mission in one streamlined solution. So our modern secure technology, industry leading reporting, and tailored design means your daily experience is efficient and simplified, and you don't need to worry about clunky design or navigating through features that you won't use. Lastly, we do have a team of technology experts who would love to tell you more about our broad ecosystem, of solutions for the social good space. And if you'd be interested in learning more, about that, I'm gonna put up a little, poll here. This is anonymous, so you can put this in. And if you're interested in having somebody reach out to you, we can do that. Alternatively, if you're not interested, that's fine as well or you're not ready to talk yet. In a few moments after I've introduced Peter, I'm gonna put up a little, link to a product tour for Blackbaud grant making where you can simply, kind of a one to many, take a little 30 minutes, get an overview of the solution. Alright. Well, we've gotten the commercial stuff out of the way now, so we can move on to the meat of what we really wanna talk to talk about. So I am going to stop sharing this and go back to our slides here if I can. There we go. And I will pass the ball to Peter, to allow him to introduce himself. Peter, take it away. Thank you, Ray, and it's really great to be here with all of you today. My name is Peter Panopinto. I'm a managing partner at Turn 2 Communications in Silver Spring, Maryland. I use he, him, his pronouns. We do a lot of work with community private health foundations, really working to help them, with their branding and communications needs, but we also do quite a bit of work, at the intersection of communications and technology. And I come at today's conversation, as somebody who's really been following, the world of, grantmakers and their use of technology and their use of, everything from the from, social media to AI tools to help improve their communications. And today, what we're really going to do is talk about, not just the emergence of AI for grantmakers, but, making the case for and creating an internal AI policy that will govern, the way your organization uses AI, with the goal of, you know, empowering your team to experiment and use AI tools to advance your mission and the mission of the organizations you support, but doing so in a responsible way that both protects your organization time, setting the stage today, talk about how to make a case for an internal AI policy, and then walk you through the the, the steps of, in creating a policy, even sharing some examples of, of language that's included in some policies of organizations that we are either familiar with or have worked with, and then talk about how to, spin that forward in terms of getting your team engaged and, and paying attention to how the, the world of AI technology is emerging. And then, as Ray mentioned earlier, we're going to leave plenty of time for questions at the end. So, look forward to hearing your questions and and, hearing from you about how you're, addressing, the explosion of AI at your organization and some of the concerns you're seeing. So I wanna start by just kind of placing us at the moment of time that we're in. If you go back about 15 years ago, as to the beginning of social media as kind of a widely accepted tool in our field, we saw a lot of experimentation by grant making organizations on how to use social media for good. And we're seeing that right now, as a lot of great grantmakers are starting to experiment with the use of, AI technology to help support, either their grant making work or, support organizations that are, are working on the causes that are central to their missions. So for instance, the Gates Foundation is really using, investing in AI as a tool to help fight malaria. They're using AI tools, to really map, the impact of malaria in at the community level and in real time and project where, where outbreaks might be happening in the future. It's also supporting the use of data modeling and molecular technologies that can help make the response to malaria outbreaks around the world more efficient, even while using, limited resources. So grantmakers are experimenting with AI as a public health, tool and as a way to really improve health around the world. We're seeing organizations like the Rockefeller Foundation, use in and and invest in AI tools to improve agriculture as part of a larger effort to eliminate food insecurity around the world. So they used to, invest in tools like farmer.chat, which helps, farmers in, in places the world like India, improve their practices and, and get better results for with their farming efforts. And we're seeing a a handful of other foundations really, getting aggressive in this area. Everyone from the Chan Zuckerberg Initiative, which is using AI for things like, trying to advance biomedical research, to Welcome, which is using AI to help unlock the potential of, of unlocking new drugs, that can help improve public health, and even an organization called AI for Good, which is investing in exploring AI across a number of different issue areas to help support, innovation and and change, in in our world. But, as I mentioned before, all of this is quite reminiscent to what we've experienced before in our field. And for those of us who've been working long enough to remember the late 19 nineties when the, Internet was first exploding onto the scene, more recently, about 15 years ago when Facebook, Twitter, and other social media tools were really starting to explode onto the the scene, we saw a great bit of excitement in the field. We saw a lot of grantmakers really thinking about how they could leverage these tools to accelerate the work that they're doing. But it also brought some challenges, of course, and some of those were innocuous. You may or may not remember, the American Red Cross about, 13 years ago. One of their younger employees thought she was sending a tweet out to her social network, her personal social network late at night, and, and instead, she was, tweeting about, basically, out drinking with our or home drinking with a friend, to their, organizational Twitter handle. So there was, you know, some some inadvertent slip ups, but we've also seen some bigger issues, of course, with social media. We've seen, many instances where organizations have, misled users and shared personal information, that shouldn't have been, shared outside of their organizations. We've, of course, seen a lot of, issues with disinformation, that have, disrupted our elections and have created a lot of, polarization in our nation. And we've seen the, immense mental health issues that have afflicted, generations of social media users and have really, caused a lot of societal and mental health challenges for people, who have gotten really, involved in using social media and kind of making it central to their lives. So how does this all relate to AI? Well, with every great disruption, with every great opportunity that exists, there's also the potential for downside. And if you've been following the news at all lately, you've been seeing cases of of, stories that not only tell about the immense promise of AI, like some of the ones that I shared with you earlier, but we're also seeing issues where AI is, is already posing challenges, that are both, things that can impact, organizations' reputations but can also hurt people quite a bit. So we've seen cases where, individuals have used AI tools not to help inform their work, but to plagiarize. And if you're an organization that like a a news organization or a grant maker where your reputation is is really central to your work, you know, having, running the risk of having, employees who are basically using AI to pass off their own work and then that gets discovered, that causes immense reputational challenges for organizations. We've also seen, as we have with social media, the, the sharing or the leaking of personal information or proprietary information already. Information already. So, grantmakers have to really be thoughtful about making sure their employees understand the risks that come with using AI tools, even very popular AI tools like, Otter dotai and ChatGPT, and making sure that they're not, including proprietary information from their foundations in any of their queries or the work that they're doing because there are risks that that information can get out. We've also seen a lot of cases of AI, you know, really, kind of using biases and even amplifying biases, whether they are related to gender, race, class, or other things. There are big risks with using AI and and, that, that you could be perpetuating or even, creating, new stereotypes and biases that can, that can do quite a bit of harm. And we've also seen, AI being used for, deception as we have with everything from email to social media. There are lots of opportunities, for people to use AI for evil and to use it as a way to, deceive others, to potentially scam others, and, and really cause a lot of problems that, our organizations wanna both be aware of and avoid. So after setting the stage on on what's at stake with AI, both for good and for evil, it's really time for us as organizations now that it's very clear that AI is taking hold and is is something that very is likely is being used throughout your organizations right now for us to put on our risk management hats and think about how we as grantmakers can not only help ensure that, that AI existentially isn't causing, challenges and harms, with to the issues that we are working on through our our grant making and and in support of our missions, but also that we're, you know, minimizing the risk or, protecting our organizations from from being at the center of an AI, controversy that would really damage your reputation or even create issues with how you approach your work. So, we've done a lot of work over the years with organizations to help ensure that they create, smart social media policies, and we are now also been working with some organizations to help, help them create AI usage policies that are similar to what your organization might have now, in terms of ensuring that your team is using social media responsibly, that it's upholding your brand responsibility, responsibly, but that, you're also, creating, some guidelines and rules to the road for your team to make sure that, that they are, you know, not getting, embroiled in social media controversies or or doing things that would jeopardize the the reputation of your organization. So if you've stated the case and and have have done so to create a social media policy at your organization, now now might be the time for you to to take a similar approach to creating an AI policy. And, depending on the role you are in in your organization, you may actually need to, to to make the case to to leadership or your board that now is an appropriate time for, you and and others, other colleagues at your organization to really sit down and develop an internal AI policy that can help manage the risk of your organization and help ensure that AI is being, used for, for the betterment of society rather than for creating additional challenges and exacerbating some of the issues that we're working on. So as you make the case for an AI policy, you know, you can really talk about how you you can use this policy as a way to encourage innovation and experimentation with your team, as a as a way to help advance your work and and, and do more good in the world. If you put your policy together well, you'll be creating a a situation where, you know, you're you're encouraging its responsible use and making it clear to your team that it's okay to use AI and it's okay to be thinking, creatively about how to use it. But at the same time, you're doing it in a way where you're creating some guardrails to make sure that you're protecting your organization's proprietary information, that you are are not exposing yourself to risk, and that you're you're thinking about its use in ways that don't perpetuate stereotypes and and or create additional challenges as you advance your mission. So it's really important as you're trying to to make the case for this internally that you talk about both sides of that, both that as a way to encourage innovation and, advancement of your mission, but also that you're using this as a way to both create guardrails, mitigate against risk, and make people more aware on your team about the dangers of using, some very common AI tools. So, as we shift into, to to stating from stating the case to actually, developing a policy, we really recommend that at the very beginning of the process that you don't make this an ad hoc, process, that you don't just assign this to one person in the organization and, and and have them come up with a a written set of rules and guidelines that seems like it's coming from on high and may not fully reflect, how AI is being used at your organization and may not be greeted as as warmly as if you actually enlist, your staff in creating these policies. And there are a couple of ways to do that. One is to create a a working committee that that, brings together people from different parts of your organization to to put the policy together, to publicize that, through your internal communications channels as you're developing that to let people know that this is something that is in the works and inviting them to contribute, ideas and questions that you can address in your policy. Another way to do that is if if you don't have the bandwidth to create a working committee is to, is is for the people who are are going to be leading this effort, for them to to create opportunities for people to weigh in, and and also to create, you know, ways to gather information through surveying and other things to find out how people are using, AI tools now, and to hear what their questions are so that you can start to address, those questions in the policy that you create. And as you go through the process of developing, your social or your, your AI policy, it's also important to include in the policy some some common definitions and and common language, so that everybody on your team is grounded in what you're talking about. And, that's especially important for an emerging, technology like AI, where not everybody on your team knows, in and out what AI is or even, understands some of how it intersects with things like inter intellectual property and third party information. So being really clear about defining some key terms and doing it in a in a language that is going to resonate well with your team is something that's really important. So we've included a few examples on this slide of the kinds of things that you may want to, to try to define in your AI policy to make sure that, you know, as as people are reading through the rest of the policy and even thinking about some of the the opportunities and challenges that come from using it, that they're that they understand the terminology, and they they know what you're talking about throughout, your policy. And then, after you, you know, kind of spell out some of those initial definitions in your policy, it's also important to to really set some ground rules and frame why you're creating this policy. And and to do that, we really talk about, when we're working with folks to develop these policies, to really before you get into some of the risk management pieces, really use the policy as a way to encourage people to use an experiment and talk about, at a high level how these tools can inform your daily work. So how you can use chat gpt, for instance, to, to do, you know, high level high level research that can help inform, how you might approach a problem, how to how to generate, email subject lines or help you come up with creative titles for, for, a new project that you're putting together. Really talk about how some of these tools can help, enhance your daily work and then also about how these tools can help inform the way that you approach some of the big picture work you do, so some of your grant making and some of your leadership outside of your organization, and frame the purpose of these, tools as well. Really talk about AI as not, something that you use to, replace your work, but as as as tools that can help you streamline your work, and that it's always important to make sure that you're, that that you're not using AI as kind of the endpoint, but as the starting point for what you're doing, and even include a few use cases of how to use these tools in your in your policy. So here's an example of of how this might play out in practice. This is an example from a a policy, that has been an AI policy that's been developed by a community foundation in the Midwest of the US. It it really spells out, how tools like chatgpt and Google Bard, can assist you in your work, and, encourages people to ask questions if they if if they think that there are issues or potential, challenges that are coming from the use of any of the AI tools that they might be coming across in their work. So once you kind of map out those use cases, then you wanna really move into, you know, some some real guidelines for how to use AI in ways that, you know, protect your organization's, reputation and integrity while at the same time still feeling empowered to use them. So, I've noted a little bit of this already, but you really wanna try to spell out, the fact, first of all, that you, you should always be double checking anything that's generated, through AI in your work before incorporating it into anything you use, either, just at your desktop or anything you put out publicly. ChatGPT is a tool to help you get started. It's a way for you to gather information quickly. It can be a way for you to even think about, novel ways to approach problems. But everything that comes out of AI needs to be fact checked. It needs to be edited, and it needs to be, brought into your voice. So, you know, you also wanna try to make sure that you're upholding your intellectual integrity as an organization and, again, that you're not passing off the work of AI as your own. So, again, while it can be a starting point for you, it should not be your end point. And then you also wanna to point out some of the the cases where, where AI can create, challenges for your organization, both in terms of introducing bias into your work. So, really being mindful of of, you know, any any content that comes back to you through AI, really being mindful of whether or not it includes anything that might include biases, or or or reinforce or even, create disinformation that, can be really harmful. And then above all else or or potentially alongside all else, you wanna make sure that you don't, that you make it clear to your team that they don't wanna share not any personal information, but any proprietary information that is that is unique to your organization into any of the the queries or tools that you're that you're using because you do run the risk of that information becoming public. So while you may wanna to, use a a tool like Otter dotai to help generate notes from a meeting, there may be times where you wanna turn that off because you're talking about something sensitive and and or sharing information that you wouldn't want to to to to get get used publicly. So being really thoughtful and mindful about how you can, encourage your team to to ensure that they're not sharing anything with AI tools that might come back to bite you. And, above all else, we had a, a a saying when I was a journalist that we wanna trust but verify. So you really, and this is a point I made earlier, you really wanna make sure that you are fact checking and editing everything that you use AI tools for. And, as an example, I'm actually showing my work a little bit, and and I used, chap GPT to help crowdsource some some recent examples of how foundations and and other grantmakers are using AI for good. So I ran a query there, and you saw a lot of those, results in the earlier part of this presentation. But for each one of the examples that I chose to share at the beginning, I went through and I independently verified them. I went to the organization's website, and I made sure that I found out whether the results that I got back were accurate. And then I also made sure that, I interpreted those results and I put them in my own language. So I wasn't just taking the results that AI gave me and and and put them out willy nilly. I really wanted to make sure that anything that I used from an AI tool for this presentation or really anything that I do is something that I have personally verified. Now that takes some time, but it would have taken me a lot more time to actually go in and and and look for individual examples on my own to try to to verify. Through this process, I was able to to bring back some results that I could then go out and verify, and and I actually refined my search a bit to to find some additional results too. So how, how do you play that out in your actual policy? Well, here's an example from another, community foundation that, has developed an AI pool, policy recently for their team, and it talks again about, you know, how to use the tools to assist but not replace, the the work that, they're doing. And it even recommends some tools that you can use to check information for accuracy. And what I blacked out there is just the, the username and password for that organization's Grammarly account, which I don't think they would have wanted getting out there. But, you know, you can take steps to not only, make sure that you're making clear that you that people should not be using, AI tools to pass off their own work, but that you can also give some guidance on how to do that. And, they've even gone a step for forward and and talked about how to use some of the the AI tools through popular software platforms like Zoom, and and talks about, how to, to ensure that when you're using Zoom's AI companion tools that you're not, that you're not sharing, any sensitive information during those organ during those conversations. So once you've, gone through the steps of mapping all of that out in your policy, you know, you're not done. You don't wanna just create the policy, send it out, as an attachment to an email, and and be done with it. We really recommend taking an extra step, and and doing a training where you where you walk through the policy, where you talk through the different elements of it, explain things very clearly, and you leave plenty of room for for for the your team to ask questions. And as you introduce, your policy in this type of setting, I think it's really important to, again, lead with the purpose for, not only for why you've created the policy, but why you think AI is an important tool for your organization. So really trying to, be mindful of the fact that you you don't wanna scare people away from using AI if possible. You wanna make sure that you you talk about the potential and the ways that that can make you work more smartly, more efficiently, and more creatively, and even ways that it can help advance your work at the macro level outside of your organization. But that you're doing this, to to make sure that they are using these tools in a way where, they can do that ethically. They can do that in ways that, supports the mission and values of your organization, and that it it, upholds the integrity of your organization. And, again, make sure that you leave plenty of time for questions when you do this. Your team, is going to be coming at this from various different vantage points. You're going to have some members of your team that have that are have, you know, been afraid of using AI tools or have have just not had the time to dive into this, into experimenting with them. So you wanna make sure that you're meeting them where they are and and and and making sure that you're answering those questions. And then you're gonna have more advanced members of your team who may, bring up specific use cases. And in that in those cases, especially if you've created a policy and you don't know the answers to all the questions that may come, you wanna leave room for opportunities to say, that's an interesting question. We haven't considered that yet. Let us research that, get your input, and either add that to the policy or get back to you as well. And as I noted, I think, we wanna make sure if you can to provide a list of of tools that your organization has vetted and and has approved for use. And and, you know, as your team is putting these policies together, it might be helpful, to, to spend some time, taking a look at some tools that have been, that are are more common and and may already be in use of your organization. And having your tech team and or having, your own team kind of look at, look at those and and, you know, test them out a bit and find out what you feel like is is appropriate for use at your organization. So, Central New York Community Foundation is actually, as part of its policy, created a a short but growing list of AI tools that the organization has vetted and approved for use by the full team. And, some of these, are are tools within, within software that they're already using. So they tested some of the AI tools in the Adobe Creative Suite, for instance, and some of the, the tools that Canva provides and have made sure that they have have tested them out and made sure that they they meet the organization's standards and that, and that, they have also provided some guidance on how to use them efficiently as I as I showed earlier. And then finally, it's really important to not, develop this, your policy and then put it on a shelf and expect that you're done with the work. AI is going to be evolving, quite a bit over time. It's evolving very quickly. New tools are coming online every day, and, and your organization is going to be using them differently and experimenting with them with them differently. So it's really important, as you create your AI policy to be to be mindful of the fact that this isn't a onetime process. This is something that you're going to want to, go back to, that you're going to want to create, a process for updating and, and changing as as, your organization's knowledge changes, as the tools themselves change, and as new use cases emerge for AI. So, we recommend, you know, either having the person or the team that created your AI tools consider themselves to be an ongoing group that is going to be revisiting and updating your AI policy periodically. And that may be monthly, it may be quarterly, it may be semiannually. But it is something that you're going to want to make sure that you're leaving time, you know, all the time, for, for, you know, amending and updating and and adding to, your policy, and that you're going to, also want to create, an open door for people to ask questions and to to share, challenges and share opportunities that they're seeing with you too. So it's really important again to make sure that you're engaging your team in this process, that this isn't something that is being, you know, coming out of a a corner office in your in your organization and is is something that's just gonna get tacked on to your other policies at your organization. You really, if if you really wanna maximize the benefits of AI and minimize the risks, it's very important to make sure that you're having some open communication with your team about how they're using these tools, getting their questions about how they're using them, and then doing some research and and updating of your policy and communicating that out to everybody as you go. So with that, I I did move through things a little more quickly than I thought, but with that leaves, that that leaves quite plenty of time for your questions, and and comments on what we've talked about. Absolutely. And I just, thank you, Peter. That was great. I just did a request in the chat there for some additional questions. We've got, one already queued up, but I'll I wanna say something. 2, that I think you and I talked about when we were sort of first exploring the idea of of addressing this subject, which I was lucky enough, at Blackbaud to be part of our sort of early adopter program that we did with, you know, Copilot is mostly what we're using, although we have some other tools that have their own built in AI as well. And then after going through that, you know, I've become sort of one of the champions internally of that. And, you know, the biggest one of my biggest takeaways from that was, you know, the degree to which you, you have to develop this skill. You don't think of interacting with an AI as a skill that you need to develop, but it really is. The first 2, 3, 4 weeks I was in the cohort, I was not getting anything out of it. And I was saying to myself, you know, this is just simply not gonna be the panacea that all this buzz in the world is saying it's gonna be. You know, this isn't not only not changing my life, it's not even changing my 5 minutes. You know? But over time, I began to learn how to interact with, with it, you know, how to engineer prompts better, how to, where it could be applicable in my my daily work. And suddenly now I'm, you know, engaging with it, all the time. So I do think that's, like, one of the things that I that's one of the bit of advice that I pass along to people is give it time and sort of get make yourself continue to to work with it and use it, so that you can learn how, to interact with it and what's gonna work for you. Yeah. I think that's a really important point, Ray. And that it it's like any skill. It's something that you have to develop over time, and, you're not going to be as good at it when you start as you are, as as you've had a chance to experiment with it and and and try different things and see how it it gets you the results you're looking for. And and that's a skill that I I I will admit I'm still I'm still honing myself. You know, it it it's it's different than just googling something, and it's different than, you know, doing your own brainstorm. It it really is something you have to train yourself to do. But when you do and and you start to see results from it, it can really be an important time saver, and it can be a way for you to to, you know, leverage technology in ways to accomplish more, and and, gather more information, process more information, and, you know, in in some of the cases we're seeing with some of those large case, uses by some of those large private foundations, really, create some transformative ways of approaching our work. Absolutely. Alright. I we've got several handful more questions coming in. So, Peter, I'm gonna display the questions on the stage here so that you don't have to hunt through them, and you can just see, read. Everybody can see it, and you can, answer it. So I'm gonna start with the first one. Okay. So do you have any suggestion for an online resource for us to learn more about using AI tools? Our team has not been able to dive into learning the how to's. That that's a that's a good question. I was really focused on, on on on the policy part of this, and I I, I wasn't putting a lot of effort into, into using the tools themselves for the purposes here. I do think there are are a lot of, emerging, publications that are are focusing on AI tools, and I can I can follow-up with you, Julie, afterwards with some more information on ones that that I think are good off of of the you know, that might be worth, exploring as you're as you're digging more into this? Excellent. Excellent. And I can include those in the follow-up email, any resources that you have. So you can we'll get that to you, and you can also see it there. Alright. Here's the next one up for you, Peter. Yes. This is a really good question. How do you, recommend striking a balance between being excited about the tools and trying all the things and slowing long enough down long enough to do the verification and data security? The tools are changing so quickly. Yes. I this is a a common tension that has has really happened with with all of these technologies as they've emerged. I would recommend really trying, at first, using 1 or 2 tools that, that really align well with the work that you're doing. So if you're doing kind of creative work, using something like chat gpt or, other kind of over the the counter projects or products that, that will help you, you know, do research, brainstorm ideas, and and really kind of focusing on those and doing it in a way where you're not actually putting anything proprietary out there, where you're asking kind of, more ambiguous or, not ambiguous, but more, innocuous questions, to really learn how to to train yourself, as Ray mentioned, on how to use these tools, and and start to see the value of it as a as both a time saving tool and as a as a way to to train your brain to think a bit differently. I I would recommend really focusing if you're especially if you're new on just 1 or 2 tools that that you feel comfortable with, but that can really start to show you value. And and talking to others, other colleagues, and and finding out what's working for them, and and seeing if that's something that that can be helpful for you too. But also doing so in a way where you're you're not, you're not sharing information that is is really your own. So if if you're doing a query at your foundation, you know you know, really making sure that, you know, it's it's about something that's public facing. It's not something that, is is really, proprietary or, or is is going to give away any any of the data that you're using. And for tools like, you know, Zoom's AI or or Otter AI, which which is doing, you know, creating those, meeting, transcripts and and meeting, summaries for you that, that in the same way where your board goes into executive session, you almost go into executive session when there's a part of the meeting where you're talking about something that is is clearly something internal and that you actually turn the tool off, and make it clear, at the beginning of all those meetings that, that you're planning to use this tool and then if anybody has any objections that we we don't use it. Excellent. Excellent. Good. And I I wanna I know you're doing this. So I noticed in the chat that Carrie also mentioned, which I think is in reference to Julie's, question, that, you she says our teams relied heavily on LinkedIn Learning for prompting courses and basic overviews of AI. And, of course, yeah, the same. And that's a that has been a really good, resource for that kind of thing. So I just wanted to call that out out loud so that anybody who wasn't paying attention to the chat, might see that as well. Alright, Peter. Next one for you. Okay. Yes. Do you have suggestions for addressing grant applications entirely written by AI? And this has become a bit of a challenge for some grantmakers where they're having, grant you know, folks who are applying for grants actually have their applications written by AI. I would I would make it clear in, you know, in the in the RFPs that you put out or in in any of the language that you put out, what your foundation stance is on that. And I would make it clear that, you know, that it is okay to use AI tools to help research or streamline ideas, but but, that you will not if if if this is something you don't feel is, is is in line with your organization's values that, you reserve the right to, to turn down or ask people to resubmit applications that you deemed to have been written by AI. I think that's a a a fair way to do it and do it in a way where, you know, you give you give the the nonprofit a bit of benefit of the doubt, but you also make it clear to them that, that while you wanna make the grant application processes streamlined and and easy as possible for them, that you do need to to to to, to have them ensure that they're they're putting their own work forward and that you give them an opportunity to maybe resubmit if if you see an issue there. Mhmm. Next one up, I am gonna take, myself because it's for it's about Blackbaud. So, I am going to, answer this. Let's see here. I don't know if this will display this. It will not. It sends it throughout. We have, so Blackbaud's, intelligence for good is what our AI, functionality is referred to. And, I'm going to put a banner up again, a ticker up again with a link to, our web page that goes through all that rather than take Peter's time talking through that. I'll put that out there so all of you can can take a peek at that on your own time, and I'm gonna move on to the next one, Peter, for you. Okay. How can I still utilize AI in an organization where data is sacred? For example, University Foundation. They know there's a plug in option in chat gpt, which allows you to upload Excel spreadsheets and do some cleanup for you. But at the same time, it could be a reason for a data breach. Yes. That is you know, these are are some of the issues that a lot of organizations are dealing with right now. You know, I would if if you're worried about the tool itself, putting that information out, I would I would maybe avoid some of those functions if you feel like you are, including information especially about donors, or, or grantees. I would I would just avoid using, those more open source tools for now until there are some better safeguards in place. But I would still, consider AI a tool that you can use for, for some of the more creative work that you may have to do or for brainstorming ideas for events and titles for events or, you know, really, you know, making sure that that the tools that you're using are ones that are going to, you you know, enhance your work in other ways. And I would also just be very, you know, mindful of what the privacy policies are for different tools and maybe even having, some counsel either from your IT team or or from legal counsel to take a look at at some of the fine print on these and see if there are safeguards and and securities with some of these platforms that, that can can can allow you to do some of that more proprietary work and and not have the fear of it getting out. Alright. Email is AI and organization. Oh, I got one more that just came in that I have not popped through yet. So here we go. So is Microsoft Copilot more secure than other third party AI tools? I I, you know, I'm not an an expert on, on data security, so I I can't verify that on my own. But, again, I would have, somebody who's a bit more, adept on the tech side of things to look at that for you and and make sure that that is, that that that's something that you verify on your own. Yeah. I'll I'll just reference to to us. We're we're, at Blackbaud, a a very strong, partner with Microsoft. So that's why Copilot is the the solution of choice for us. So where I'm not I'm not making a judgment about the security of any other solution. So I don't know if the onus of that question was because I'd mentioned Copilot when I was talking about what we're doing. So I didn't wanna misrepresent that I was making an assessment of the relative security, or quality of that versus anything else. It's just simply what we use. Alright. I got one more, and we've got time. So I'm gonna pop it up. So award management student essays. Okay. That's, well alright. Linda, you might have to be a little more specific. Yeah. I'm I'm yeah. Linda, can you Award management, Peter, is our scholarship solution. Okay. Grant making does scholarships. It's more, for organizations that are, independent of the university in question. So organizations that provide scholarships for students to use wherever and however they please. Award management is our solution for our higher education customers, so it would be, you know, the university itself generally giving scholarships to its current students. So it's not exclusively that cut and dried, but that's more or less how it works. But I'm not super sure what the specific question is here. So, we'll see if we got anything else. I don't think we do. So Yeah. It might be for, for making sure that that students submitting essays, like, any any submit any language suggested language for, for ensuring that they they aren't using AI to to generate their essays, I think, is potentially Yeah. She did, clarify. They have students who are obviously using AI for essays, and they don't sound at all like something a student would have written. Viewers need help scoring applications. Yeah. I don't know that there's a, you know, I I don't know where we are yet. It's for in terms of assessing that out yet. I did see that was interesting that, you know, Grammarly with that some of those, organizations one of the organizations you mentioned earlier, Peter, was using Grammarly to, suss out, plagiarism. Is that is that right? Yes. And there are some emerging tools that can that, it's like using AI to fight AI. Right? It's it's basically the it's a whole new problem or something's been plagiarized. You know, I think, you know, I I would explore, you know, what are the best of those tools, to to maybe screen essays before they even get to the reviewers and maybe flag them and and and reach back out to those students, before you send them to the reviewers if that's possible. But also make it clear upfront that that that that is a process that you're you're taking to maybe head off, head off the, you know, students trying to do that. So I think if you're clear in in, in the language, upfront, saying that, you know, we're using a a screening software to to to to to screen for those who have used AI to write their essays, and that we we reserve the right to, to disqualify anybody who does that, upfront. It might be the way to approach it, Linda. Mhmm. Mhmm. And then, as we're gathering down here towards the end, I have I'm just gonna be honest with you. Amna has, raised their hand, which is actually something that has never happened before in, one of these webinars that I've done. I didn't even know it was a possibility, and I have the it's asking me to approve or reject. So I don't know if that will allow you to ask a question out loud, which maybe you typed it in already because I know we did answer one of your questions, but I'm gonna approve it. And let's just do a little experiment, see, and see if that means that you can actually come off on sound and ask us a question. Hello. Yes. Oh, look at that. I've moved you on stage. Okay. Thank you for being my guinea pig. I had never, seen that before and I'd never tried it, but it's really interesting to know that that's how that works. So thank you for for being that. Did you have an additional question, or did you put it in the chat? No. So I just had that question, and, like, you asked to, like, raise hands. I just tried that, but, like, I still wrote it in the QA section as well just to be sure Okay. My question get answered. So, yes, thank you so much for that. You. And sorry for, for, putting you on the spot like that. That was not my intention. I actually didn't know what was gonna happen. I I I thought maybe it was gonna let you speak. I didn't know it was gonna put you on the stage, so I apologize for that. Now that I know, I can be a little more careful in the future, but that was a fun little experiment. Okay. I think I'm gonna look back at the QA one more time. Yep. Alright. So I think we have covered them all. So that is fantastic. And we've got 2 minutes remaining, so I think we are at the sort of end of our time here. So I will say this. If you are you will receive a copy of, I wanna first of all, I wanna thank all of you for joining today. First of all, if you're type frantically typing a question right now, don't stop, because I will get a copy of that question even after the presentation ends. And I will ensure that either I will answer it or if it's for Peter, I will get it to him and he can answer it and we can reply to you. So we'll email you back with that. And if you are interested in learning more about Blackbaud grant making, you can respond to the email you received as well and get some information there. I am going to put, in the chat a link to, BBcon, which is, our conference at Blackbaud and I will say that we have this year, I'm proud to say, the most sessions strictly, concentrating on grant making than we have ever had in the history of bbcon, and that's going back a long time. So if you have the the means to join us in, September in Seattle, you can do so at that registration link, that I just shared. And also that reminder that you will get the on demand recording for today's presentation as well in that, email and option to schedule a call and some additional resources that we have compiled for you. You can see Peter's, information here. You can also click on the speakers up above. And if you wanna, it'll link out to Peter's website, his, some of his social media as well. Peter, thank you so much. This was really fantastic. Yeah. Thank you, Ray. It was great, great to have the opportunity to meet with this group and hope everybody got something useful out of it. It's always been, it's always a pleasure to work with Blackbaud on on presenting information like this. So glad glad to have the opportunity. Awesome. Thank you so much. Everybody have a great rest of your day. Thanks, everyone.