Seeing Around Corners: A Feminist Take On Predictive Grant-Making

Maybe like me, you have sat in way too many breakout rooms with other social justice actors where we talk about our response against backlash as being more reactive and proactive. If only we could get ahead of the curve, structure our funding to see around the corner. 

Philanthropy is often late, but movements already know what is coming. Movements feel the tremors before the quake. 

When philanthropy is late the cost is a ripple effect of consequences that continue to exacerbate existing inequalities and distance more communities from the vision of enjoying and exercising their full rights. From the last-minute disbursement of funds to organise rallies in response to legislative bans, to grantee partners receiving their grant amount after the fact as a reimbursement of already paid for activities, philanthropy must do better to remain true to their assumed role of service to humanity.  

Enter predictive grantmaking: an approach that uses data analysis, artificial intelligence (AI) and other technological tools to strengthen grantmaking decisions.  

Working for a feminist grant-maker now, I am often in awe of just how much data we have at our disposal, data that spans decades that could help sharpen and inform our grantmaking as well as the work of our grantee partners. A consistent issue within my team is how we can continue to share information on trends within our regions and use this data to advocate for better funding and improve our own internal funding practices. I imagine this challenge exists everywhere and maybe, just maybe, predictive grantmaking holds part of the solution. 

Promising a space where philanthropic innovation and tech collide, predictive (or proactive) grantmaking uses data analysis and other tech leveraged tools such as AI to predict: 

1. Under-resourced movements in regions of shifting political risk, 

1. Which issues will become urgent before they escalate, 

1. Where funding is likely to have the most catalytic impact, 

1. How to build agile funding portfolios, 

1. How to identify which factors are likely to affect a grantee partner’s execution of their work. 

Predictive grantmaking uses historical grant data (from philanthropic databases), open-source intelligence, indicators like human rights violations or legislative changes and AI models that spot patterns to design its offerings. 

Who’s doing it? 

Anticipatory tools for social impact work are nascent but not unheard of. These examples can be used to refine predictive grantmaking approaches and they include: 

• Civic Data Coop in the UK using data to build insights on public participation 

• DataKind efforts in applying AI to civic participation  

• The Centre for Humanitarian Data is partnering with The Rockefeller Foundation to increase the use of predictive analytics to drive anticipatory action in humanitarian response. 

• Stanford Cyber Policy Centre’s work on forecasting 

The risk: Can predictive grantmaking be feminist? 

The fact that predictive grantmaking relies heavily on the use of data, AI and other digital tools to design its recommendations raises serious ethical concerns. Some feminists have called out Big Tech for extractive data practices, privacy issues, disembodiment of data and colonial, racist biases embedded in their models.  

This alone presents a mammoth challenge of making proactive grantmaking movement focused. This could be achieved through: 

• Using predictions as dialogue —not decisions—predictive insights are used only as conversation starters with movements and to help grant makers prepare internally, 

• Co-designing the framework with grantee partners through future mapping workshops and scenario building that informs the AI model indicators, 

• Building shared data sites with the community of grantees through regional data cooperatives, where they can access the data used to build predictive insights, 

• Pairing the insights with flexible, movement-led funding e.g. through rapid response grants, 

• Embedding transparency and consent in use of data into the process, 

• Using predictive insights for internal planning, not external ranking of partners. 

Key actors in predictive grantmaking  

This approach to grantmaking offers a unique opportunity for feminist tech collectives (e.g. Pollicy, Association for Progressive Communications) and data labs to support philanthropy, bringing together tech that can really be used for good.  

Additionally, pooled funds, philanthropic collaboratives (e.g. EDGE Funders) and large hybrid funders (e.g. FORD Foundation) could be ideal partners due to their large and often shared data infrastructure supported by philanthropic databases. Regional feminist funds, already embedded in the local political context can sharpen and further inform piloted approaches.  

The ethical complexity presented by AI should invite us to cautiously experiment and discover its vast potential. The for-profit world continues to use technology to lock out the very people it claims to serve. In order for philanthropy to move from reactive to proactive it must combine movement intuition, feminist analysis and predictive data to anticipate when and where funding can be most impactful. This avenue may have the potential to be ethical and collaborative when it is built around this core belief: “Movements are the most accurate predictors of their own futures” and any additional forecasting information seeks to support their knowledge, not override it.  

 

By Imungu Kalevera
Programme Officer (Regional. Africa and West Asia) at Mama Cash
Digital Frontiers Institute Community Member 

 

Established in 2015,  Digital Frontiers Institute  is a proud brand of  Digital Frontiers. 

 

(Article originally shared on LinkedIn/Pulse by Imungu Kalevera on 26 June 2025)