AI and Ethics for Nonprofits

welcome everyone to the community I.T innovators presentation on artificial intelligence a Ai and ethics for nonprofits I am really excited to introduce our panelists today we have Johan hammerstrom who is the CEO of community I.T and Sarah detroita who is with project evident we know that artificial intelligence or AI is coming at us at 100 miles an hour and a few years from now it will have transformed our workplaces and our communities so if you're trying to use AI to do your work or if you'r

e worried about the impact that aoi will have on the communities where you live You Are Not Alone Sarah Detroit is at the Forefront of considering the ethics of AI and how nonprofits can advocate for ethical use of AI so my name is Carolyn Woodard I'm the Outreach director for Community I.T and I'd be the moderator today I'm very happy to have our panelists here so Johan would you like to introduce yourself yes good afternoon thank you Carolyn uh my name is Johan hammerstrom I'm the CEO at Commu

nity I.T I've been with the company for over 20 years have done a lot of work with non-profits and helping them use technology effectively during that time and over that 20-year period I've seen a lot of new waves of Technology coming into the sector and creating opportunities and also challenges for non-profits and it definitely feels like we're in uh starting to see something new with all of this AI technology I have a lot of questions about it so I'm really excited that Sarah detroita is join

ing us today to share her wisdom and knowledge on AI and how non-profits can can use it so Sarah can you introduce yourself it'd be a pleasure hi everyone I'm Sarah detroita I'm a strategic advisor with project evident and focused on product innovation I've been in the nonprofit sector for the last 30 years both as a funder as management leading a COO leading a scaling non-profit and then as an independent consultant and in addition to my work with project governor I spent the last three plus ye

ars just doing a deep dive on AI project evident cares about practitioners nonprofits being able to own their own data and be able to use that data to drive their own learning agendas so they can get to better outcomes for their communities so we look at AI as an incredible opportunity for non-profits to be able to make a positive action with the data that they're already collecting um and to create more Equitable outcomes for the communities that they serve through use of AI so we're really exc

ited about the Advent of AI and I spent the last three years doing AI research leading cohorts of non-profits getting ready to Pilot different types of machine learning applications currently project evident is partnered with the Stanford center for human centered AI we are doing a national survey on non-profit and Foundation AI use and I hope many of you will choose to take that survey so we have a robust understanding of what's happening with AI and I'm just really delighted to be in dialogue

with Johan and dialogue with all of you about Ai and where it could go thank you Sarah would you like to tell us a little bit more about project evidence I just put the link to the survey in the um in the chat for everyone thank you so much a couple of other links that I'll put in there as well thank you um and just to mention briefly on the survey we want as many folks inside your organization to take the survey so um if you choose to take it please do pass it on we're hoping that we can do sub

group analysis so we understand how people in different types of roles as well as different types of organizations in terms of the programmatic focus are using AI so um chair broadly share liberally so project evident was founded uh because the we watched how evaluation uh and data was being used as a way to sort of thumbs up or thumbs down on a program model as opposed to using data the way it's used in a for-profit sector um the way we use it and organically in our own lives which is to make b

etter decisions and to learn and so the goal was how do we help non-profits and practitioners and funders use data in a way that really fuels their own learning agendas what is it you're trying to learn how to do with your community how can data help you do that and inform the action of management the action of Frontline staff and also inform the community that you serve ultimately leading to um uh to more Equitable outcomes so that's the goal of a project evidence uh the signature work that we

do is the Equitable recovery wallet which is free services around data and and data analysis and use and systems for small nonprofits early Journey non-profits as well as strategic evidence planning which think of it as a strategic plan for your evidence building with larger non-profits and then over the last several years we've gotten involved with AI work as well awesome thank you so much I just jumped dumped a whole bunch of links in the in the chat about um project evidence and their work so

really appreciate that Sarah and this will all be in the transcript someone was asking there will be a video a transcript and we also released this as a podcast um so if you're struggling to take notes um don't worry you'll be able to catch up on all of it later um all right so before we go much farther I'm going to tell you a little bit more about Community I.T if you're not familiar with us we're a 100 employee owned managed services provider we provide outsourced I.T support and we work excl

usively with non-profit organizations our mission is to help non-profits accomplish their missions through the effective use of Technology we're big fans of what well-managed I.T can do for your non-profit and we serve nonprofits across the United States we've been doing this for over 20 years as Johan said and we are technology experts so we are consistently given an MSP 501 recognition for being a top MSP which we received again in 2023. um I want to remind everyone that for these presentation

s Community it's vendor agnostic or in this case I should say maybe tool agnostic so we're going to talk about a lot of different uh AI tools that are out there we only make recommendations for our clients and only based on their specific business needs but um you know it's in everybody's interest to learn more about what's out there what the landscape is and how these tools are working so I want to go on to the learning objectives for today we're hoping that by the end of today you will have le

arned what we mean when we talk about AI for nonprofits be able to recognize some common AI applications some you may already be familiar with some that may be new to you discuss what organizations need to have in place before implementing Ai and Sarah is going to offer some examples of nonprofit sector usage of AI to enhance impact and equity but before we do that we're going to start off with a poll so does your organization use AI now so you can answer yes no you're not sure or not applicable

um so it's not a trick question we really just wanted to get a sense of um where kind of where you are in this journey and um if you've never used it you want to find out about it you've come to the right place if you're already using it and kind of trying to figure out how to do governance and what it can really be used for you're also in the right place uh and there's a good question AI in general or generative AI so we're going to talk about that difference uh in just a moment but go ahead a

nd answer um from your understanding I think would be the easiest for this one and Johan would you like to read the results for us sure yes so just about 30 percent of respondents um selected yes their non-profits are using AI 40 just over 40 percent responded no around 20 percent responded not sure and then the remainder said it didn't apply to them so it's pretty pretty varied pretty mixed excellent okay so and a little bit of a curve there the curve so all right well I think Sarah then that i

s the perfect jumping off point for um your explanation of what is the difference between Ai and generative AI sure so um so AI is a technology that allows you to mimic human cognitive functions so whether that is decision making or problem solving you know essentially it mimics the way our brains um help us do those types of tasks and the way it does that is you feed it lots and lots of data and it learns from that data to predict what the answer to the problem might be right using past data th

at's different from generative AI generative AI it's still predictive but it's trying to create something new it's not looking at a new use case examining thousands of Prior use cases or hundreds of use cases um and then determining what's a likely answer generative AI is trained on massive data theft and it generates new speech in terms of sound it generates new writing that would be chat gbpd it generates new visuals so you can ask it to create an image it can also create video so generative e

ye uses large large amounts of data so folks might be aware that shaft GPT three not four which is also in the market but three was trained using all of the available information on the internet up to 2021. so that was the training data set pretty large data set um that was used to be able to make predictions about how to respond to questions it's still basically a predictive technology but it's generating new content new pictures new sound you probably have also heard the words uh machine learn

ing uh and you know machine learning versus AI uh this is essentially for Lay people the difference between ice cream and gelato like yes there's a difference between these two delightful frozen drinks that use sugar and cream and ice but if you're really hungry a hot day and you want something sweet either one will do technically machine learning applications are a subset of AI of sort of that traditional AI not generative Ai and a machine learning application would be something like a recommen

dation engine which you all experience with Netflix or with Amazon when you make a purchase would you like to see this movie would you like this book or a virtual assistant which you have probably encountered if you were trying to change your airline flight um or maybe if you were shopping online and suddenly a virtual assistant asked if you needed help or natural language processing hey Google hey Alexa hey Siri the ability to understand language so those are all different types of machine lear

ning applications and there are many more by the way those are just three that you probably have encountered in your day-to-day existence at this point I really like that analogy of ice cream versus gelato I think that really helps sort of clarify um you know what we are now calling Ai and maybe it truly AI technologies that we've been living with for for a while now um one of the questions I have is why now like what my understanding is that you know some of these generative AI models have been

around for you know four or five years and it seems like it all just sort of exploded onto the scene in the last year and there's a lot of hype around Ai and AI tools and I'm curious you know what what's sort of behind that and Is that real or is this just part of the you know the tech industry hype cycle right which does feel like we've been in a constant hype cycle for the last decade of different types of technologies that are going to revolutionize our world um so I want to talk a little bi

t about why now for traditional AI some of those machine learning applications that I just referenced as well as why now for regenerative AI so um Johan you're absolutely right the antecedents of AI have been around for probably 25 years in terms of academic research in terms of the commercialization of this technology um certainly some of those machine learning applications have been readily available to large companies for the last decade I mean it's transformed the way that we all interact as

consumers with companies we are no longer in a one-size-fits-all world in terms of the way you interact with an online retailer sometimes even with an offline retailer if you have one of their credit cards so the what's happened on the sort of traditional AI is what happened with Squarespace so for those of you who are not around when the internet first came uh and uh and suddenly everybody needed to create websites to do that you had a you went to a website design firm and there were graphic d

esigner there's a project manager they were the copywriter there was a coder there was a whole team of folks that had different specific capabilities that would custom build a website for you Flash Forward to today and you could probably have a high school intern build you a pretty decent website for your organization using Squarespace you don't need to have technical capabilities you don't need to have um uh design capabilities um and you can probably even stand it up so that you can process cr

edit card payments so that moving from customization to a tool that a non-technical person can use with travel that path with traditional AI so recommendation engines chat Bots Predictive Analytics there are now tools that are available to you through the Amazon stage Suite through Microsoft that you can literally just use the package and you can upload your data and you can kind of drag and drop if you will so I think the time is now for sort of traditional AI because it's going to get really c

heap and it's going to get much more broadly available to folks and you don't need to have the same level of technical expertise to be able to program in terms of why now for generative AI is the fact that it got released publicly and you know some of our largest companies are search companies you know Google's now in a monopoly to be called a dialogue um a legal situation in terms of its Monopoly on search search is an incredibly lucrative industry and I think one of the reasons why we're in th

e hype cycle is that chat gbt actually is a way to completely upend the way we search things and the uh the permeability of information so that it is more integrated when we receive it as opposed to looking at 12 different lengths and looking at lots of different information and then you doing the integration so I think it's possibly going to upend a big company and that's why there's a bit of hype around it and the fact that it was publicly released and it was a cool tool and it was free um tha

t certainly drove a lot of interest as well um that's really interesting I wonder if because Microsoft obviously you know is a big investor in open Ai and you know one of the first use cases for um text-based generative AI was Bing you know and it's so it's in some ways maybe Microsoft saw that as an opportunity to finally gain some search market share uh from Google um not sure if that's happening but um that's that's interesting it's important I think to see the tech industry business behind t

he technology itself like the technology doesn't exist in a vacuum it's being created by these very large corporations for very specific business purposes so that's a it's always good to be to be reminded of that that's right um I think that's a good segue into uh talking a little bit more about what nonprofits can do to be ready for AI because all these tools are coming out and I think there's a there's always the fear that if you don't you know jump on the the train quickly enough it's going t

o leave the station and you and your organization are going to be left behind so we do want to talk about that but I think we have another poll before we get to that um question yes that's true all right let me um get to that poll all right so we wanted to know from you um how your organization has addressed Ai and so we when we were talking about this um before with Sarah when we were preparing for this webinar today um we came up with these different kind of levels of where where you are at in

addressing AI so the first option is you we don't have an AI policy or governance it's pretty much a free-for-all so if you have a tool or are using a tool that has AI built into it you just go ahead and use that as an employee the next answer that's possible is we have an AI policy staff as individuals know how they are allowed to use AI to do their work so you've started thinking about it maybe you've put a policy together or some governance or are in the process of putting some governance do

cuments together um another level level that you could be at another layer here is that you have tools that use Ai and you've gone beyond just having governance documents you're intentional about increasing your efficiencies to increase your impact so you're looking for ways that you can use AI tools to make your organization work more efficiently and achieve your mission that way the last level was embracing AI to achieve your mission and maybe customized and intentional ways so maybe even tran

sformative ways doing your mission perhaps differently because AI allows you to do it in a different way in the community where you work um so I again this is not meant to be a trick question but it does require a little bit of reflection and I see about half the people have have answered it if you're not totally sure how to answer it that's fine I'm gonna go ahead and um end the poll and share it with everyone and Johan would you mind again just reading the results um and letting us know what w

hat we find in this poll yep so the the vast majority of respondents 85 percent don't have an AI use policy or governance it's a free-for-all that's I'm not going to say that's wonderful but you're in the right place um we are going to be talking about this exact issue and and you're also not alone so don't feel bad if you're if that's the answer that you gave because that's 85 of the people on the webinar today uh six percent have an AI policy um six percent have tools that use Ai and um the re

mainder are uh are not applicable well at two percent were embracing AI to achieve their mission in intentional ways so very interesting to hear and thank you everyone for sharing that with us and thank you Johan for mentioning like you're not alone everyone is struggling to figure out how to use this and how to have governance around it which is why we have Sarah here with us today um okay so I want to move on to our next um topic which is around Readiness for AI so Johan I think you had a coup

le questions for Sarah Oh lots of questions um so well I think you know one of the things that um I have to keep reminding myself of is that you can't just deploy technology you can't just launch it you have to prepare first and that's takes a lot of discipline to remind yourself of that especially when they're really interesting and novel technology tools being released you know that our tendency is to kind of jump in and start using them and figure out figuring out how they work um but there i

s a lot of important preparation that needs to take place first and um you know I'm really really interested in hearing more about what project evident has been doing you've you mentioned it in your introduction that you've spent the last two years really deeply focused on on AI and figuring out how it's going to fit into the larger goals of project evidence so I'd love to hear more about what you found in your research and how your findings can be used by non-profit organizations in getting the

mselves ready for using AI tools appropriately before we kicked out the webinar and I was chatting with Caroline and Johan a little bit and I I just talked about how much I really I love moments when I have moments of tremendous change both inside organizations and across our sector I find those to be really fertile exciting moments and we're we're definitely in those moments right now both inside our organizations and in our society writ large so I just I just want to say that I have a lot of e

nthusiasm and excitement about what can happen and what may happen for us in the future so they're one thing that I really have loved doing at project evidence is I've thought a lot about AI adoption inside organizations and I'm going to focus on that initially and share some resources I then want to talk a little bit about what I think some opportunities are for AI inspector um and maybe I'll share some examples of that as well uh if Johannes that if that's where we want to go um so uh Johan I

really appreciate appreciate the way you framed this which is actually there's a lot we can do to get ready um and this became really apparent the first cohort that I led which was preparing a set of Education organizations who specifically wanted to work with recommendations that was the ml technology that was of interest to them and we thought we would start this cohort by you know day one of a you know six month cohort was going to be let's talk about the problem that you want to focus on um

that a recommendation can be a smart solution and we realized oh people were nowhere near identifying the problem and actually we need to start earlier um and that really became the basis working with that group and then doing a a bunch of Master Class webinars around AI Readiness uh morphed into what we call the AI Readiness Diagnostic and Carolyn I sent you the um I just chatted you the link for that that's on the project evidence website it's a diagnostic of 12 questions I'm going to give you

a high level of kind of the four buckets that that falls in um and when you go through the diagnostic which we highly recommend that you do with program technology and your measurement and evaluation and learning staff because when I share with you some of the questions no one person has all this information into organization which is as it should be because AI projects are actually our cross silos inside of our organization and really require collaboration but the diagnostic will then give you

a customized Report with additional resources and um uh and some ideas about how to prioritize and help you interpret it interpret your um interpret your scores but at any rate not surprisingly the place that we really would love you to start is with equity and design justice so if you're an organization that has never brought end users into a design process that's something you should learn about before you jump into building an AI pilot if you haven't done your Dei work to be able to talk abo

ut difference and Equity because often for organizations that deploy an AI tool they will learn that maybe there were some inequities around how their program was being delivered to different subpopulations of clients that they work with being able to contextualize that and talk about that and learn from that is really important so design for justice and Equity is the most important that you have some some Basics around some skills and some work you've done as an organization the next is strateg

ic purpose not surprising if you're going to undertake a large project you want to make sure that you're bored and your leadership is on board you also have to have real clarity about your program logic model so the secret of using Ai and the non-profit sector is that your program logic model is the algorithm all of that hard-won knowledge about how you create change for somebody that is actually the algorithm that you're seeking to animate and so having a really clear program logic model that r

eflects what is actually happening on the ground not what we told funders not what was happening three years ago but we haven't looked at it it really has to reflect the best of the knowledge of your Frontline staff we then ask people to really focus on um Knowledge and Skills so what are the capabilities that you can either have on staff or can access on staff and then we talk about data and systems where's your data stored how can it be accessed what is the quality of your data with the amount

of the data that you have so design for justice and Equity strategic purpose knowledge of skills and systems and data those are the things you can do in advance right so you need to do in advance to have a strong Foundation I wish to stand to be able to build an AI pilot I'm gonna pause vary on and see where do you where do you want to go next yeah that's uh it occurs to me that um that those activities require a lot of Reflection by the organization um and and and it sounds to me I mean the im

pression I'm getting is that um these tools are about augmenting the intelligence that the organization already has and enhancing the their capacity to do what they're already doing um and maybe creating new opportunities and new ways for them to do that um but I'm wondering like particularly with the the logic model how technical does that need to be is that something where the organization really needs to understand how the AI tool is going to be implemented in order for them to carry out that

work yeah no I mean the program lodging model and there are a lot of great resources online just just been broken logic model in Lockwood school will show up or you could ask chat GB for the best programming create the most uh you know integrated best program logic model for you um what's important about the program logic models it doesn't have to be technical it has to be representative so it actually has to represent the different steps that move from inputs to activities to outcomes to long-

term change that you believe you're having not at a societal level but at an individual personal level um that's what has to be reflected because within that you can begin looking at ideally with your community again this is design Justice right with your Frontline staff with your community and say if you need more in some aspect of this program logic model at some step in this program module model if you knew more could you do better could you get better more Equitable outcomes and that's going

to help isolate for you the places where the folks who have the most lived experience the most lived expertise are going to be able to identify for you that's the problem we're trying to solve within our program logic model if we could do better here doing better here might be um might be Precision analytics if we could have better subgroup breakdown so we knew what elements of our program model best served different types of our clients or it could be it could be a recommendation engine if I w

as better able to connect more teachers with um you know with a particular professional development that was sort of just in time you know perfectly matched that would support them on being better prepared in their classrooms so it can be you know it can be the program logic model can be agnostic of the type of machine learning application but the program lodging model will be your map to help understand what is the problem you're trying to solve and then you can figure out if one of the tools t

hat are in the market can help you solve that problem got it that's very helpful the um one of the sort of a high moments for me in in you know hearing you talk about Ai and preparing for this webinar is the degree to which this is also data driven um you know we don't like you you mentioned that the that um chat GPT I believe was trained on all of the data on the internet which is you know kind of an inconceivable inconceivably massive amount of data um but it the the impression that I'm gettin

g is for non-profits to get ready to use AI tools effectively they also need to be aware of their own data because is it am I understanding this correctly it's really bare data that's going to be informing the AI tools that that they start using so here's what's really exciting about using AI tools absolutely some of the data that you're using is your data and by the way you don't need all the data uh you know from the internet up to 2021 to inform your recommendation engine for recommendation e

ngines you probably need about 200 250 quality um data cases and I'll explain a little bit what that means um so I just want to I want to right size the expectation the other really interesting thing though is that in the last decade our government has been releasing in like more and more um data sets to researchers um and just making them available to the public so you have the ability to Now integrate your data with census data which can be incredibly Rich data so uh there's a there's a there'

s a school that is using um actually a recommendation engine to identify students who are overlooked for AP or IB classes it's called equal opportunity tools that's always been their program model but they've done that by achievement and they've had a human look at school-based data attendance um uh grade point average as well as their own survey data that they would collect inside the school as well as some types of data that is outside in the community they're now using a recommendation engine

to bring census track data School data as well as their own collected data to then recommend which students are being overlooked now importantly that recommendation is not then just being given to the school the recommendation is being given to the coach the Frontline staff person who was always part of their job to find those overlooked students they now just have a machine learning application a recommendation engine that's making those recommendations to them for them to verify it frees up t

heir time um so that they can be more efficient in their decision making and spend more of their time focused on making sure that the students who do end up in those classes are successful which is the other part of their job that's such a great example because it really um shows how these tools can enhance the the human intelligence that's needed to do this work and it also sounds like they were very intentional about the approach that they took in in developing that system um how how long did

it take what was the kind of their their overall process you know how long did it take for them to get ready what sort of LED them to deploying and implementing those tools was it was it what were some of the challenges that they ran into along the way so here's my so first of all they were part of uh equal opportunity schools as part of one of the car I think what was most exciting is that it's a relatively large non-profit um and that you had a Frontline coach person sort of somebody in that r

ole talking to somebody who did measurement evaluation and learning as well as technology so when we ran that co-work we said you need a representative from each of these plus somebody from Senior Management that's willing to be in collaboration with each other around understanding what the problem is and and beginning to design what that solution could look like I think one of the most exciting things for us was when the measurement evaluation of a learning person said I didn't know this person

on staff and now she's my go-to as somebody who I want to talk to at least once a week to help me make sense of information that I'm receiving from across the network so I think one thing that's really powerful about these projects is that they have an opportunity and they have a necessity frankly of knitting together it's not a technology project I know I'm talking about Community I.T Community but it's not a technology project if you're if you want to use AI to drive your outcomes to attain y

our mission this is the program project that is supported by evaluation and measurement and learning and supported by technology all of you have to be on the same side of the table together working together um the challenges that they faced um was you know how do we engage the rest of our organization on this because we have some fear that this is going to overlook we're going to overlook students by going in this Direction versus having it still be um still be uh done by a human so like any big

shift that you're doing inside of your organization there was a change management activity that had to happen um and that activity was making sure people understood the technology understood the sort of the piloting that would happen in a safe space so they could use data from prior years to run basically the same analyzes that people would run as individuals to see whether or not the recommendation engine would be as strong as um as strong as having a person do it because they you know it was

it was how do I bring my organization along that's training that's learning that's transparency um and also how do I keep the mission front and center because the goal was we want to serve more kids they want to serve more students if we are going to be tied to a human being looking at all the data to identify those students then there's a limit to how much money we can raise and how many students we can identify but if that's the bottleneck if the identification is the bottleneck not the ensuri

ng they're on the right track and you can release that bottleneck well suddenly I can achieve more of my mission I think when you put the mission front and center that really helps people it helps people change because we're all in it for the mission we could all be doing something else and probably making more money we're all here for the mission I think keeping that front and center and making sure that you're emphasizing equity and not being biased goes a long way to Bringing organizations al

ong I'm gonna jump in and push us to the next topic yes um Johan there's a question in the Q a so I wonder if that might be a good pivot okay yeah let me pull that up before I do I just wanted to add one last thing um first of all sir that's an it's an amazing example I loved hearing about that organization and the approach that they took and I think one of the things that really stood out to me is that it was so human-centered and human focused and certainly the mission Focus keeps re-centering

them on the human need and the human objective and I think one of the big challenges that we all face not just in non-profits but in society as a whole is the fear that these tools are going to replace humans and certainly you know that's a big motivator behind a lot of the labor strikes that are happening uh right now is is this fear that um you know the the objective of the tool is to generate profit not to solve human and problems so there's just such a wonderful opportunity for non-profits

to use these tools in a very different way and really become an example for how these tools can help Society rather than you know enriching small segments of of society one of the challenges is that these tools have been really developed and owned by the commercial sector which means profitability and scalability are the two most important design Criterion and um you know uh ethics and equity are not that's not that's not how those organizations are incentivized or compensated I think one of the

powerful things we can do as a sector is get involved with AI um even though it's not perfect because if we wait for it to be perfect we'll never be a part of it by the way we will definitely make mistakes because that's the only way people learn is to make mistakes but if we start demanding tools that have that are not biased and have um Equity at the center of their design that market goal is going to eventually exert an influence on the market um and so I think I think getting involved is on

e of the ways that we shape the market and I think in order to be able to do that it definitely helps to understand some of the problems or challenges that exist with the current Tools in terms of their data biases um the ethical issues that are kind of inherent to how the tools have been built so I was wondering if you could talk a little bit more about that absolutely so there are two first of all I just want to be clear that all data I love that we are talking about Ai and bias it's so import

ant because of the scalability of Technology um uh but I I want to bring this same level of focus for whenever we're talking about data and whenever we're talking about models those models are data and models are collected and crafted by human beings we are creatures of bias our bias is reflected in those tools so I just I want to kind of flip that out where you see bias come in uh in AI or is in two places one is the data sets on which something is trained and then the second is the algorithmic

model that underpins the application so think about we've talked a bunch about check DPT we've talked about the fact that it was trained on all the data on the internet up to 2021 how much of the data on the Internet is from the global South versus the global North I mean there's a there's a pretty European American global North centered information that's on the internet relative to other countries and other parts of the globe so you can think about the bias that already exist in chat GPT just

around Global North Global South let alone other isms that are a reflection of our society that have been magnified or maybe just mirrored um uh you know on the internet so you know if you want to hire more women and you're using a data set to try and figure out who is successful and likely to get promoted this is actually a real example uh and all of your data is really showing that men tend to get promoted inside your organization if you use that as your training data as a way of figuring out

which resumes that you should look at that are going to be recommended to you it's going to not recommend that you see any women female resumes because you've just taught the algorithm by the data set that that is not the type of person that can be successful in your organization even if you're intent at the beginning was to diversify their organization so who is in and who is not in your data set is incredibly important in terms of training your algorithm the second place where bias uh comes c

omes in is just the algorithm itself now rarely are algorithms I Won't Say Never but my assumption is rarely is the algorithm saying we don't want women we don't want Catholics we don't want XYZ right that that's not likely what's happening what's happening is that the algorithm is valuing something that is more likely to be associated with a race and this ethnicity a religion or a gender so really interesting case study from Women's World banking they were looking at algorithms that were being

used to decide who was getting a micro loans by different micro lending um and Banks uh globally they notice that women were not getting as many of these loans and what they saw in the algorithm is that when it was waiting um uh a prior work experience retail work experience or work experience in the home was not weighted as highly as work experience in an office or work experience on a job site so because of the waiting was different and that women had a greater propensity to have retail experi

ence or work in the home they were not getting nearly as many of the loans because they were not deemed as credit worthy it didn't say we favor men over women it was something else that was in the algorithm so often there's a hunt to figure out what do you what are you putting in your algorithm that is actually telegraphing a bias because it's correlated to a certain subgroup of people and how do you identify those biases just through testing the algorithm are there other ways yeah so a couple d

ifferent ways so one is you can first of all if you're gonna buy an ml tool or work uh you know work with somebody around an ml2 machine learning tool you can ask questions around like what data set was this trend on I want the evidence that this was trained on you know a full data set that had subgroup you know subgroups that matter to me that were well represented um maybe didn't include some information that I you know hate speech or whatever that I don't want to have represented so you can a

sk about what data was actually used for training on the algorithms they have to be bias tested uh harder to do if you're talking to you know Microsoft co-pilot around you know huge companies around what they did it's going to be hard for us to get much purchase around getting feedback on that but you can have algorithms tested for bias and then you know algorithms are not a set it and forget it activity so if you do have an algorithm that's inside your organization that you've had custom built

or that you're using every new piece of debt you trained it on old data but every new piece of data that comes through is fuel it's learning for that algorithm so algorithms can drift because they're they're sort of live creations with every piece of information that comes through them so you have to be really Vigilant on continuing to test and watch your algorithms to make sure they're giving the responses that um that makes sense for your populations and um and near your values that's so inter

esting it's kind of a whole new way of thinking about technology tools the the idea that they evolve you know over time um that's kind of a new a new way of thinking about it for me at least you know my my impression has always been well this is a spreadsheet and this is what it does and it's you know it just calculates all the numbers and and that's it so it's interesting to to think about technology in this way um we want to leave some time for Q a um I could keep asking you questions for two

more hours Sarah but I don't want to monopolize um the webinar here today we've got some great questions coming in so the first question is um about AI policy what kind of policies should nonprofits have are there templates that you would recommend non-profits using and then second the second part of the question is um if there are additional risks beyond the ones we've talked about to non-profits using Ai and how those risks can be mitigated so I think at a minimum folks need to have an AI poli

cy in place just for your team who is using chat GPT maybe using otter AI might be uploads documents into grammarly I think the you know what's important for you as an organization is an agreement you know across the staff or with the executive team of what is what is open information that's shareable what is private information but it's shareable because it's been de-identified and then what is frankly is either personal information or intellectual property which cannot be shared and sharing is

as simple as I uploaded it into chap GPT to see you know to get a good summary of this or to compare it to you know other models that are out there in the world if you upload something into chat gbt or if you invite otter AI into your meeting the fine print on both of those tools is that your information becomes part of their fuel right it becomes part of their training data it also becomes part of the data that they can use to respond to new questions new inquiries that come their way there ha

ve been some right-wing chat gbd came out there were a bunch of news stories about companies that you know folks that uploaded customer data they had uploaded intellectual property and folks didn't realize that they were essentially taking stuff that is not shareable in their organization and making it shareable for everybody so really important because there are a bunch of tools out there that don't require you to sort of officially be a part of your Tech stack they're just available to Consume

rs um really important to have a policy around how people might be using those tools to decrease their work Warden and what data is shareable and what data really isn't shareable it's amazing like that that takes the whole concept of Shadow I.T you know to a whole new level that you know AI in your stack and AI outside of your stack and really thinking about your stack your data you know it's a great um conceptual model um for thinking about where all these things are going in I.T um I'll prostr

ain myself from asking more questions and ask another question from our audience what human capacity resources do you recommend some level of Information Management leadership for policy development process and Tech adoption with the development of an internal data science analyst office that would support program staff to help Advance the outcomes of their logic models it's kind of like a whole new way of thinking about the I.T Department what what recommendations do you have so this is so this

depends on it depends on your strategy depends on your goals so if you are a non-profit like Crisis text line talking points will.org these are all organizations that they're AI native you know they were founded with their program model has AI embedded in it those organizations have data scientists on staff you know they have to have a technology team that's doing internally facing work but they have to have a technology team that's just focused on their algorithms and the technology they're us

ing that's before their program model they also in fact have data scientists as part of that team they also have to have a data scientist on their measurement evaluation and learning team right because the the amount of data and the ability to analyze that data is radically different if you are an AI native centered organization that's really different than if you're a community-based non-profit that you know wants to incorporate chat gbt into your fundraising you know you can probably ask chat

GPT what's a good policy for fundraisers around um around AI by the way there is going to be an upcoming conference called fundraising AI that's going to be a two-day conference that's free um that's just all about fundraising and AI because I know that often comes up for folks um so it really depends on what's your purpose what's your goal you know I recommend for for non-profits that are just getting involved in something great advisory committees you know look for a board member that has expe

rtise that can join your board they can you know they can help you with some of the operational pieces before you decide this is going to be a significant part of who we are and we have to have our own embedded staff and again it all depends on what you're trying to do I mean the the tools are going to increasingly become drag and drop that can be complicated because those tools are Black Box you may not know the data that they were trained on you may not know whether or not the algorithm matche

s your values but that those assets are going to be available to you and increasingly there are good you know algorithms that are available um uh through GitHub you know that are just open and the development of algorithms is becoming more lego-like bit versus custom coding so even the cost of developing custom algorithms is coming down so it is going to come in your direction but the question is what do you want to do with it and how much is it going to be at the center of what you're planning

to do as an organization and I would hope that there are you know sector-wide incentives for making sure those black box tools are either more transparent or you know ensuring that they don't have bias or because I could just you know see the the misuse of some of these tools really undermining objectives that organizations are pursuing and this is one of the reasons why I think it's so important that um I know Carolyn shared a Blog that I wrote with the center of effective philanthropy that was

specifically around philanthropy getting involved but I believe the same is true for non-profits I think if we stand on the sidelines while AI technology is being developed we won't have a chance to make sure that equity and bias are really attended to in product development because nobody else is asking for that right now I won't say nobody else is asking for it nobody's getting paid to deliver it right now I hate to break in because we're getting close to time um I wanted to just say thank yo

u so much Sarah for all of this and for those of you that have a couple more questions um in the Q a and all the great questions we got at registration we're going to try and answer all of them and also try to put all of these uh Great Links and resources that Sarah's been mentioning today in the transcript um I feel like we hit all of our learning objectives learning what we mean when we talk about AI for nonprofits recognizing common AI applications discussing what organizations need to have i

n place before implementing Ai and offering examples of non-profit sector usage of AI you had some great stories there I also want to make sure that I hit on our upcoming webinar next month which is going to be October 18th um we have a expert from build Consulting who's going to talk about data and databases so we'll get a little bit farther into that question question and then our cyber security expert Matt Eshelman is going to join him and we're going to talk about the security that you need

around data and databases maybe in addition to or different from your general cyber security that you have for all of your I.T and I'm just looking forward to it I've given this great discussion today on data large databases and algorithms and models to get a little bit more into that so that's going to be Wednesday October 18th at 3 pm Eastern new Pacific having come to this webinar you will get an email with a link to that of course you can always find it on our website as well if you happen t

o be watching on YouTube today we encourage you to subscribe to our YouTube channel so you'll get updates every time we update our videos there you can also register for upcoming webinars on our website because even if you can't attend in person you can get on the mailing list you can ask your questions at registration and suggest topics for future webinars for us so we'd love for you to be able to do that and we have a minute or so left so I just wanted to ask Sarah before you go there were a c

ouple of questions about templates and I'm I think I've been looking too I feel like there's no great resources out there yet with policy templates is that true how about if I commit to doing a little bit of research so that when the transcript goes out I'll have some links for you but the I was looking at them while we were talking and they are behind paywall um so the technology Association of grantmaker has some I I will say in developing the um AI Readiness diagnostic it is pretty incredible

what you can ask check GPT to create for you so one option is to do multiple iterative asks of chat GPT to create a policy but let me see if I can find some example links for folks as well that sounds great because when I was poking around as well before this webinar it seems like there's a lot of very very generic yeah templates out there that maybe aren't um very geared toward non-profits or the way nonprofits would want to have that policy in place so that would just be incredibly useful and

then the other question that we've gotten a couple of times is the risks um the risks to non-profits maybe risks if you don't have a policy risks if you're using AI that might come back so I think there are I think there are two risks for folks who are who are just stepping into this so one is not having a policy that's well understood by your entire team about what is shareable information what is shareable but has to be de-identified information and then what is unshareable it is intellectual

property or it is um personal information I think that's just incredibly important for folks to know I think the the second risk is increasingly AI assistant before your Tech stack um so you know there are some places where you might be asked to upgrade something and there are other you know play an extra fee to be able to have this AI based feature and there are other places where AI is just going to be a part of things and so I I really encourage folk to ask their vendors where is AI in the c

urrent product um and where is it going to be in the future you know future versions of the product so you are not caught off guard around your data being used when you haven't even really consented to that it's just a new feature I think those are the two risks for folks who are not you know uh proactively going out and trying to build algorithms to you know to give it that you really have some control over saying yes or no yeah just to keep in mind well I want to be mindful of people's time um

we came right up to the hour as is my watch um but I just want to thank you Sarah so much for sharing all this expertise with us and giving us a lot of things to think about a lot of resources to look into Johan thank you too so much for your time and for um being able to interview Sarah today and uh thank you everyone who attended and gave us all these great questions at registration we're going to do our best to answer all of those in the transcript so I'll let you go on your way but um thank

s again everyone thank you Sarah it was a pleasure thank you so much