Philanthropic foundations are not always the best places for innovation. They can be risk averse, bureaucratic, hierarchical, and cliquey. But a significant minority of funders are working to open things up, to adopt new methods and act in the more accountable ways. As a result it’s now possible to see how philanthropy could become a lot more data-driven and better at learning.
Here I suggest potential innovations that could transform how the bigger funders - and we at Nesta - might work in the future. I’m hoping for constructive engagement from other funders on how we could cooperate to test out use of AI and open data to reduce the burden on applicants and grantees.
1. Open data as the default - the first step is to move further towards open data as the default e.g. what 360 Giving has done for grants. The big challenge in all fields is to make data not just open but also of a good enough quality to be useful. In parallel, much more work will be needed to create classifications that would make it easier to use data, linking to and layering other datasets around: organisational growth, take up of novel ideas, how funded activities relate to scales and patterns of need and how well evidenced activities are.
2. Smarter sifting - explore the use of machine learning to improve the speed and quality of sifting of initial applications. It should be able to automate some of the most time-consuming elements of grant-making processes, teaching itself by analysing training data of previously successful and unsuccessful applications, and learning to screen, grade, and rank the strongest applications.
3. Smarter applications - AI could also help to overhaul the application process itself. Instead of written forms which tend to favour highly educated applicants, or a small industry of consultants, an alternative would be to use technology to increase accessibility by avoiding the usual written formats, for example through a structured interview process using speech to text, asking applicants to describe key aspects of their work.
4. Smarter shared handling of data - a common grant application digital format would allow organisations to maintain, manage and reuse their proposals for submission to multiple funding opportunities. The creation of a secure common repository for global common grant applications (inspired by this) would simplify workflows for both the applicants as well as the philanthropies.
5. Smarter scans of needs and issue areas - it should be feasible to map what is being funded, at what scale and with what focus, timescale and desired outcomes to provide funders with a better sense of where they can add value e.g. Washfunders mapping of local shared funding for water sanitation. The kind of work Nesta has done to map innovation ecosystems, points to what is possible; combining data sharing and web-scraping (summarised here).
6. Mobilising crowd intelligence - a final set of options would deliberately mobilise crowd intelligence to help identify priorities or decide on grants. This would probably only be relevant to the largest funders. Nesta showed one way of doing this when it opened up the topic of the new Longitude Prize to a public vote.
For the next step I would urge the foundations that are already doing the things described here to share their strategies as an open source wherever possible. I hope this article will also elicit responses from foundations who have an appetite to jointly fund some experiments and anyone interested in more joint commitment to common methods and standards, such as open data and evidence standards.
Geoff Mulgan is chief executive of Nesta. This is an abbreviated verison of the full article which can be read here