Introduction
Working in my role, I feel like I’m subscribed to almost every philanthropy, grantmaking, and social change newsletter out there. At this point, roughly 30% of the content in my inbox is focused on AI—and that percentage is increasing. AI is everywhere in the philanthropy space.
In the spirit of this article, I turned to ChatGPT and asked for a roundup of the current narrative, debates, and opinions on Philanthropy and AI. Interestingly, many of the pieces I found began with a question, but few truly answered them. Instead, they left us with more questions to consider as a philanthropic community—questions about the role AI plays in our daily work, its ethical implications, and the broader impact it has on the communities we serve.
To frame this discussion, I have drawn on some of the key questions posed in these articles and added a few more of my own. My goal is to provide context, signpost useful resources, and highlight real-world examples of how philanthropy is engaging with AI and data.
At Philanthropy Ireland, we hosted a webinar on AI and data last year, but it only scratched the surface of the potential and risks AI presents. The pace of change has been so rapid that our discussion is already outdated! Within our membership, there are varying levels of interest and AI adoption, but one thing is clear—our members punch above their weight. Many operate with small teams or even a single person, yet they deliver incredible impact. Compared to many of our European counterparts, who often have dedicated AI and data teams, Irish philanthropic organisations are navigating this space with far fewer resources—often balancing all roles, from administration to CEO, under one roof.
This article is intended to help our community make sense of the AI landscape in philanthropy—highlighting the opportunities to act as a supportive partner for those with limited resources, explore potential risks, and provide practical applications—as we continue to adapt and evolve in this fast-moving space.
What do we mean by Data, and AI in philanthropy?
When discussing data and AI in philanthropy, it's crucial to understand their definitions and interconnections. Data serves as the foundational element, encompassing quantitative figures and qualitative stories and narratives, collected from various sources. AI refers to the simulation of human intelligence processes by machines, enabling them to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.
In the context of philanthropy, data is invaluable for supporting strategic grantmaking, capturing impact, and allowing donors to contextualise their contribution to progress on key social issues. AI acts as a useful tool to translate and analyse this data efficiently and effectively. However, challenges arise when data is inaccurate or biased, hindering AI's effectiveness and potentially perpetuating existing biases.
A key (although hefty at almost 1,000 pages) resource for anyone looking to deep dive into this space is the Routledge Handbook of Artificial Intelligence and Philanthropy. The handbook offers some positive reinforcement as to why philanthropy should and really needs to examine their role in AI, it emphasises the importance of collaboration between AI and philanthropy, stating that it "acts as a catalyst for the dialogue between two ecosystems with much to gain from collaboration: artificial intelligence (AI) and philanthropy." The handbook brings together leading academics, AI specialists, and philanthropy professionals to explore various facets of the AI-philanthropy dynamic, critically assess hurdles to increased AI adoption and integration in philanthropy and map the application of AI within the philanthropic sector.
What are some real-world examples of philanthropy engaging with AI?
As with most things, real world examples of philanthropy engaging with AI can provide inspiration on where and how to get started. There are a growing number of examples of philanthropy organisations funding AI research and models, using it to support and map their own grant making and generally employing different tools for operational efficiency.
A notable observation is that while 81% of foundations utilise AI (in the U.S), few have a comprehensive strategy guiding its use. Recently the UK National Lottery and the London Community Foundation both came out with guidance notes on the use of AI and I’m sure we will see more grantmakers releasing their own guidance and policies for applicants and internal guides for staff.
Below is a snippet of some of the ways that AI is being used and supported by philanthropy organisations.
Wellcome Trust (UK), is one of the largest philanthropic organisations funding AI research projects into global health and biomedical sciences. They invest in AI-powered genomic research, disease modelling and early detection of infectious diseases. The recently supported AI applications to combat mental health disparities.
The Patrick J. McGovern Foundation (USA), considered a leader in AI for social good, the foundation funds AI and data-driven projects to solve humanitarian issues. It also supports initiatives such as AI-driven climate research and AI-enhanced healthcare solutions for underserved populations and provides education to nonprofits on ethical AI use.
European Foundation Centre (Europe-wide), supports the Philanthropy Data Commons, a collaborative effort that uses AI-powered data visualisation to map funding trends across Europe. AI models analyse philanthropy’s role in climate action, human rights, and global development. This claims to help European donors make data-driven decisions for impact.
Nesta (UK), a foundation using AI for data-driven social impact that develops AI tools to predict social trends, measure public health outcomes, and guide education funding strategies. They use machine learning models to forecast long-term societal challenges.
The European Climate Foundation (Europe), uses AI-driven carbon footprint models to track the effectiveness of climate philanthropy. AI helps model the long-term effects of philanthropic investments in renewable energy and sustainability projects. The foundation partners with AI researchers to ensure philanthropic funding supports the most impactful climate initiatives.
To explore more real-world examples, check out the Philea and Fondazione Compagnia di San Paolo report Data Science, AI and Data Philanthropy in Foundations: On a Path to Maturity.
When looking for Irish examples to highlight there was little information available, but please if you know of any or are working on AI use in your philanthropy organisation do reach out as we continue to gather learnings. The most prominent Irish example is Google’s AI Access Fund. In April 2024, Google.org, the philanthropic arm of Google, announced €500,000 in grants aimed at improving access to AI in Ireland, particularly focusing on vulnerable and underserved communities. This initiative is part of the AI Opportunity Initiative and seeks to support training and skills development for workers likely to be impacted by AI-driven workplace transitions. The fund encourages applications from social enterprises and nonprofits dedicated to equipping individuals with AI-related skills to prevent them from being left behind in the evolving job market.
There is a strong case for philanthropic giving in the area of data and AI. Donors have the opportunity to leverage AI to enhance philanthropy by funding AI-driven solutions, supporting nonprofit adoption, and fostering collaboration. Investing in AI can amplify impact in areas like climate resilience, healthcare, and education, shaping a more equitable and just future. However, disparities in access and trust pose challenges, this provides an opportunity for philanthropic efforts to bridge these gaps through funding, collaboration, and AI literacy-building initiatives.
What are the practical applications of AI I can use as a philanthropy organisation?
AI presents numerous applications for philanthropy organisations and grantmakers, including:
Communications and Marketing: This is the area where we at Philanthropy Ireland have most engage with AI and find it really useful in assisting in content writing, social media management, and audience engagement. Platforms like Mailchimp, Canva, and LinkedIn have integrated AI features that are a good starting point.
Grantmaking Analysis: AI can analyse grantmaking data to identify patterns, evaluate impacts, and streamline decision-making. Tools like machine learning models can quickly sift through narrative-based impact reports, some examples, are GrantAdvisor, Fluxx and Submittable.
Supporting Grant Applicants: AI tools can aid applicants, particularly those with language barriers or limited experience in grant writing. The guide mentioned above from the UK National Lottery on AI-assisted applications could be useful starting point. Charity Excellence also offer some great tools and insights for grant applicants.
Application Screening: AI can assist in filtering ineligible applications based on geographic area, organisational type, and other criteria. Many grant making platforms such as SmartSimple and Salesforce have AI integrations that can support this, so they may already be available to you.
Data Collection and Analysis: The method of 'scraping' allows AI to aggregate relevant insights from various sources, providing valuable intelligence on specific issue areas, which could be useful when looking for an eagle eye view on a thematic or geographic area.
Internal Operations: Zoom, Co-Pilot, OtterAI, there are many tools that can support operational efficiency, from writing meeting minutes, to transcribing and summarising there are a number of affordable tools available, or some might be already part of your package e.g. Zoom AI assistant and summary is already available on all pro accounts.
Despite these advantages, a big caveat- AI should be seen as a tool that complements human judgment rather than replacing it. These tools can increase operational efficiency when used correctly but just as there is human error there is also AI error.
What are the risks involved?
While AI has great potential, it also poses several risks and challenges for the philanthropic sector:
Sustainability and Environmental Impact- AI's energy consumption is a growing concern. The environmental footprint of AI models, particularly those requiring massive computational power, raises sustainability issues. Philanthropic organisations must consider how AI adoption aligns with broader environmental goals and sustainability policies.
Ethical Concerns and Bias- One of the most debated topics surrounding AI in philanthropy is the issue of bias in AI models. AI is only as good as the data it is trained on, and historical biases can inadvertently be embedded in AI-driven decision-making. If a philanthropic organisation relies on biased datasets, it risks reinforcing systemic inequalities rather than addressing them. For example, AI models trained on data that overrepresent well-funded charities or nonprofits might overlook grassroots initiatives and under-resourced communities. In philanthropy, this could mean reinforcing systemic inequalities rather than alleviating them. Ethical considerations around data privacy, informed consent, and transparency should be at the forefront of AI adoption in the sector. More information on ethical considerations here.
Last week I had the opportunity to attend the Philea Forum on Data and AI in Copenhagen where I got a flavour of the various philanthropic networks, grantmakers, and foundations that are capturing, utilising, translating, and visualising data while employing AI tools to enhance their work. Although ethics and risks associated with data and AI weren't on the main agenda, they surfaced in numerous conversations. A key takeaway for me for Philanthropy Ireland’s work was the caution against collecting data for its own sake; it's essential to consider what the data reveals and how it can be utilised by others. This is particularly pertinent in Ireland, where media scrutiny of charity scandals often overshadows the positive aspects of philanthropy. We must be mindful of how certain data types, if misinterpreted, or read in silos, without context, could misrepresent the philanthropic ecosystem.
Is AI and Date replacing the human element of philanthropy?
A main critique of data driven decision making, in general but also in philanthropy (although the dominant narrative mainly favours data-driven decision-making) is the loss of the human element. This is an important consideration and one of the main arguments surround the Effective Altruism movement. Effective Altruism promotes optimising charitable giving for maximum impact. In theory this sounds great, optimal even, but philanthropy remains deeply personal for those working in the space and for many, if not the majority of donors.
While philanthropy should have a strategic element and draw upon past experiences (through data, whatever form that takes) to improve the ways in which they can deliver social impact, in my opinion philanthropy is still a heart issue, that cannot be examined through a purely analytical lense. Many philanthropists and philanthropic families chose to give back to issues that matter most to them or have affected them personally in some way- and why shouldn’t they.
Philanthropy has often been critiqued if it doesn’t do ‘the most’ good but there are many niche social issue areas from specific diseases to placed based giving initiatives that rely on individual passions rather than strict data-driven rationales. Many of these initiatives are often too ‘small’ to avail of government funding or large-scale public fundraising or need a private philanthropic element to tap into these funding streams.
There are numerous examples of philanthropic donations being made because that is what the donor is most drawn to, not because of data or systems change initiatives but because it is an issue that they feel they can make a difference through supporting people and communities. And in many cases, they have made a real and lasting difference. Data drive decision making and Effective Altruism has its place, but it doesn’t mean the other side of the coin is ineffective.
Conclusion
AI and data present exciting opportunities for philanthropy, offering tools that can enhance efficiency, impact assessment, and strategic decision-making. However, these technologies should be implemented thoughtfully, with a focus on ethical considerations, sustainability, and preserving the human essence of philanthropy. While movements such as Effective Altruism has its merits, it is essential to acknowledge that philanthropy is not merely about optimisation—it is about meaningful human connections and responding to the needs that resonate on a personal level.
Irish philosopher Francis Hutcheson, who coined the utilitarian principle of "the greatest happiness for the greatest number," attempted to reduce morality to mathematical formulas. However, philanthropy does not fit neatly into such equations. While data and strategy play crucial roles, philanthropy is ultimately about human generosity and compassion.
As we navigate the evolving role of AI in philanthropy, our sector must remain vigilant, ethical, and inclusive. AI should serve as an enabler, not a replacement, of the compassion and intentionality that drive Irish philanthropy. By leveraging AI responsibly, philanthropy can continue to innovate while staying true to its core mission: making a meaningful difference in the lives of others.
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