There is enormous potential for integrating computational and network and machine-learning techniques to support philanthropic foundations evaluate the thousands of proposals they receive every year. Computational tools can help grantors and applicants identify promising grants and improve their resource allocations, provide suggestions and impact estimates for potential collaborations and future work, identify latent biases in existing funding paradigms, and lead to greater impact for each dollar of philanthropic capital. Furthermore, understanding patterns in grants data in real time can support a deeper understanding of where research wants to go years before the resulting journal articles and preprints get published.
Philanthrobotics, an effort to advance work towards a universe of open grants data and analyses, is a new collaboration between MIT Open Learning, MIT Solve, the Knowledge Futures Group, and Lever for Change around the planning, data collection, data structuring, model creation, application development, and publishing required to build these quantitative tools to support philanthropic ventures. The project, originated in 2020 with support from Lever for Change, has begun to:
· collect and structure grant application & decision data;
· link collected data to related information sources & visualizations;
· analyze statistics from grant applications & grant reports;
· initiate machine-learning analysis of the collected & structured data; and
· help the broader community convene and publish about their work
How can we build an open community-led commons of grants data? How can this drive more funding to the highest-quality proposals and projects? How can foundations and other nonprofits help make the data that they collect more widely accessible? How can we better add data and context to the proposals that we receive? And how can we make some level of machine evaluation useful for grantmakers and grant seekers? What cautions or limits to these approaches need to be considered in advance – and how can artificial intelligence (AI) help us identify existing biases, rather than reifying them?
We welcome you to join us and propose adding particular collections of data we have or might make available, to connect and discuss with groups that have similar initiatives, and to plan next steps.
Getting started w/ this community: create an account; community dashboard; contribute a pub
