Making artificial intelligence and data useful for philanthropy: The international perspective
There is growing awareness in the philanthropic sector that the strategic use of data and artificial intelligence (AI) can offer great potential for foundations. Data makes it possible to better understand complex challenges, recognise patterns and better track progress.
Data- and evidence-based philanthropy ultimately strengthens the impact of philanthropic activities and creates more transparency for the general public. Foundations outside of Switzerland are also experimenting with AI within their organisations, looking for ways to improve the use of data and increase cross-foundation collaboration.
Philea publishes study on: Data Science, AI and Data Philanthropy in Foundations
The maturity level of internal data and AI-related activities varies greatly. Some foundations are taking a pioneering role, while others are exercising restraint. Philea, the Philanthropy Europe Association, published in February a study on the use of AI and data within various European foundations and concluded that, despite the recognised potential, it is still underutilised and there is a need for increased investment in data infrastructure and internal skills. Discussions with stakeholders from the Danish sector and the USA revealed that many foundations see no need for change and have major reservations and, above all, fears of the « robot in the loop ». They fear, for example, that the knowledge and expertise they have built up over many years will no longer be needed and will be replaced by automated matchmaking. One of the challenges is that the specifics of philanthropic activities require specialised tools that go beyond the usual applications and have to meet complex requirements that do not even exist today.
An exciting example of how data can generate more transparency and insights is the Novo Nordisk Foundation, which provides full transparency on its website. For example, you can directly see how many applications are currently in the pipeline, how much money is being spent on which type of project.
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