24297 research outputs found
Sort by
Collective Learning in Philanthropy: AI, Trust, and the Future of Grant Reporting
No full textGrant reporting has largely devolved from a learning tool to a bureaucratic exercise that reinforces power asymmetries and burdens nonprofits. As philanthropy confronts mounting crises demanding equity and transparency, reporting must transform into a space for collective sensemaking.
This paper explores how Artificial Intelligence (AI), paired with Oral and Alternate Reporting (OAR) methods, can build equitable, human-centered reporting systems. We propose the E4 framework — Efficiency, Effectiveness, Expansiveness, and Equity — for intentionally leveraging AI in service of more equitable reporting practices.
Traditional reporting often sidelines the expertise of nonprofit staff and communities most affected by funded programs. Drawing on our own AI experimentation and grantee partnerships, we offer design principles and micro-moves for immediate implementation. We call on philanthropic leaders to both reposition reporting as a strategic site for trust-building and to intentionally leverage AI to support innovation and transformation in grant reporting that centers community voice, advances equity, and enables the collective learning essential for addressing complex social challenges
Philanthropy as Risk Capital: Shaping Trust and Learning at the Speed of AI
No full textAs artificial intelligence rapidly reshapes society, philanthropy is increasingly called to act as risk capital for the public good. But risk alone is not enough. Without rigorous, field-wide learning, philanthropy\u27s bold bets may remain isolated and short-sighted, failing to catalyze the systemic change this moment demands.
This article draws on three sources of learning at Omidyar Network—an evaluation of The Tech We Want initiative, external strategy consultations with 29 stakeholders, and early learnings from a generative AI portfolio—to identify how philanthropy can uniquely de-risk AI innovation for collective benefit.
Through trust-based partnerships, ecosystem infrastructure support, and narrative change, these learning opportunities revealed that philanthropy must show up beyond funding, signaling what works, sharing lessons openly, and creating enabling conditions for others to act.
This article identifies four critical insights for how philanthropy can use learning to unlock collaboration, shift public narratives, and equip the field to act with both urgency and wisdom in shaping AI\u27s trajectory toward shared power, prosperity, and possibility
When Shift Happens: Navigating Toward a Framework for Responsible Philanthropic Exits
No full textPhilanthropy can be a powerful force for social change, with influence extending far beyond the funding period. When foundations decide to cease funding in a specific area, how they exit can significantly impact the field they are leaving in both the short- and long-term. A poorly executed exit risks blindsiding grant partners and communities, damaging key relationships, undermining progress, and potentially leaving the field worse off than it was found. In contrast, a responsible exit can help the work continue long after the foundation ceases its funding.
What defines a responsible exit? In this article, learning leaders at three different philanthropies attempt to answer this question by drawing on literature, focus groups, interviews with foundation staff and nonprofit leaders, and their own experiences in philanthropy and the nonprofit sector.
The resulting framework outlines seven core elements of a responsible exit to ensure that the ecosystem is as resilient and well-equipped as possible to continue the work when funders step away. This framework is shared with humility and the hope that others will improve upon it as the philanthropic sector advances its practice with a commitment to both equity and the perspectives of grant partners
