As artificial intelligence (AI) develops increasing capabilities, particularly with recent advancements of “generative AI” stemming from ChatGPT’s launch, its potential to address the world’s most pressing societal issues has grown. However, philanthropic organizations – though uniquely positioned to advance “AI for social good” — have yet to fully unlock this potential. Philanthropic organizations across fields can drive social impact by getting smart on AI.
What is AI, and why is philanthropy uniquely positioned to drive its adoption?
AI refers to the field of computer science that enables machines to simulate human intelligence, which includes acquiring, processing, and learning from information for problem-solving. Today, 35 percent of companies worldwide use AI and an additional 42 percent are exploring adoption. Leveraged for social issues, AI holds tremendous potential to revolutionize impact across fields – from public health to education to climate change adaptation – by automating processes, enhancing decision-making, and revealing new possibilities for innovation and efficiency.
As a highly flexible form of capital, philanthropy can play a crucial role in adopting AI. Unencumbered by the direct revenue-generating priorities of corporations and the political pressures of government, philanthropic organizations can deploy capital in pursuit of innovative ideas, tackle thorny societal problems, and invest in long-term solutions. The sector’s flexibility and independence enable focus on AI’s broader societal implications and its responsible and ethical adoption.
Philanthropic actors driving AI for social good to date
To date, largely a subset of philanthropic organizations has directed resources to AI for social good initiatives – namely those already focused on technology and innovation.
For example, in 2019, the innovation-focused Rockefeller Foundation and Mastercard Center for Inclusive Growth launched the Data Science for Social Impact Collaborative – a pioneering model of collaborative philanthropy – to support data science capacity-building for nonprofit organizations. In 2022, Google.org launched “AI for Global Goals,” an initiative bringing together research, technology, and funding to support NGOs and social enterprises working with AI to accelerate progress on the United Nations Sustainable Development Goals (SDGs). Additionally, philanthropic arms of companies including IBM, Amazon Web Services, Intel, Salesforce, and Cisco lead in driving AI for social good by leveraging their corporate assets and expertise. Tech leaders, such as Meta’s Mark Zuckerberg, Tesla’s Elon Musk, and eBay’s Pierre Omidyar, through their philanthropic efforts such as the Chan Zuckerberg Initiative, Musk Foundation, and the Omidyar Network, have also made pledges and significant donations to AI-driven solutions and NGOs.
While philanthropic actors focused on technology and innovation offer existing expertise to enable AI for social good, these efforts should not be left to these players alone.
How other philanthropic organizations can invest in AI for social good
Philanthropic organizations driving social impact across sectors can invest in AI by incorporating the technology internally, building capacity of grantee organizations, and convening cross-sector stakeholders.
Internally, this might look like leveraging AI for application processing or to strengthen monitoring, evaluation, and learning (MEL).
For grantees – typically nonprofits supported by philanthropic partners – the use of AI can significantly improve efficiency (i.e., for fundraising or grant writing) and improve service delivery (i.e., for programming). Nonprofits are often limited in their capacity to adopt new technologies due to philanthropic funding constraints, which prioritize programming over operational expenses. To help alleviate these barriers, philanthropies can offer dedicated grant programs and fund capacity-building initiatives designed to enhance nonprofits’ AI literacy.
Philanthropic organizations can also leverage their roles as conveners to support knowledge and resource-sharing among stakeholders that typically work in silos such as technologists, academic institutions, NGOs, and activists.
Regardless of their strategy to drive AI for social good, philanthropic organizations must begin by getting smart on AI. This begins with the following tactics:
- Identifying AI applications: Assess how AI can be applied to their organization’s unique focus areas and accelerate progress toward their missions. For example, a foundation focused on combatting climate change can prioritize funding the development of AI models used to predict climate trends.
- Upskilling existing staff: Provide training focused on AI literacy including ethics and responsible AI practices. This is important to empower staff to identify and address potential risks of AI-powered tools used internally or by grantee partners, such as unintended bias from unrepresentative or outdated data and unethical data usage.
- Investing in expertise: Ensure the organization has appropriate talent, including data scientists with AI expertise.
- Fostering organizational buy-in: Obtain commitment from senior leadership (ex. through new or existing roles like an Innovation or Strategy Lead) and consider creation of an AI task force or cross-functional teams to champion AI integration and implementation across the organization.
As both the technology and philanthropy landscapes continue to evolve, these tactics will ensure that more philanthropic organizations are equipped to drive AI for social good.
Looking to the future: A call to prioritize “responsible” AI adoption
Philanthropy, driven by a subset of organizations focused on technology and innovation, has played an important role in advancing technological adoption for social good.
As we look ahead, it will become increasingly crucial for a more comprehensive array of philanthropic organizations to prioritize and invest in AI to accelerate social impact – and do so responsibly. This involves designing, developing, and deploying AI systems in ways that mitigate unintended bias in data and algorithms; protect privacy and sensitive data; and minimize risks to staff, grantees, and society.
By harnessing AI’s potential, the broader philanthropic sector can continue to shape a future in which responsible innovation and tackling systemic challenges go hand in hand.