TL;DR: Witness is a small web app that lets you log donations you’ve made elsewhere and see them as a growing portfolio of estimated impact. No money flows through it. I built the prototype from the behavioural research on giving, and I’d value your feedback before going further.

About me

I’m a researcher based in Belgium, with a PhD in economics and management focused on human-computer interaction. My background is in mixed-methods research on how people respond to technology.

The concept

Witness lets you log a donation made elsewhere, to a GiveWell top charity or any other organisation, and translates it into estimated outcomes. You see concrete units per programme, such as children receiving vitamin A supplementation or bednets distributed, along with cumulative lives saved and healthy life-years. The app also offers an annual giving goal (including the 10% Pledge), recurring gifts, milestones and a lifetime projection. Your data stays in your browser, and anonymous usage data is only collected if you opt in. You can try the first prototype made using AI here: https://witness-impact-portfolio.lovable.app

Why I think it’s worth testing

The problem I’m focusing on is retention. In Giving What We Can’s 2023-2024 impact evaluation, only about 30% of 10% Pledgers were still recording donations on the platform five years after pledging, and the 2025 evaluation found attrition to be faster than previously estimated. My hypothesis is that existing tools record donations well but rarely help people see, year after year, what those donations are achieving.

How the research shaped the design

• Concrete impact: In two small experiments, describing tangibly what a donation funds raised donations by over 80% (Cryder, Loewenstein and Scheines, 2013). Giving only made people happier when they could see its impact (Aknin et al., 2013). Witness therefore shows each gift in programme units as well as in lives saved.
• Who to reach first: In a field experiment with over 21,000 prior donors, adding evidence of effectiveness made larger donors more likely to give and the smallest donors less likely (Karlan and Wood, 2017). I’m starting with people who already care about effectiveness. You can still log any charity, as on Giving Multiplier, where pairing a favourite charity with an effective one raised effective donations by 76% (Caviola and Greene, 2023).
• Light gamification: Average effects are small to moderate (Sailer and Homner, 2020), and expected rewards reduce intrinsic motivation (Deci, Koestner and Ryan, 1999). Milestones are therefore tied to impact, with no points or leaderboards. Because broken streaks reduce later engagement (Silverman and Barasch, 2023), the streak follows each user’s giving cadence and can be repaired.
• Honest uncertainty: GiveWell estimates its top charities typically cost $3,000 to $5,500 per life saved. Ranges don’t appear to undermine trust (van der Bles et al., 2020), so Witness shows ranges instead of single numbers.
• Measuring its own effect: Witness asks, optionally, whether you would have made each donation without it. GWWC uses a similar survey logic and estimates it caused about a third of its pledgers’ donations.

What I’d love feedback on

1. What is your giving cadence, and what would bring you back to an app like this between donations?
2. Do programme units or cumulative lives saved speak to you more?
3. Do the figures and the methodology feel trustworthy, and does anything look overstated?
4. Which studies have I missed, especially ones that could contradict these choices?

Transparency

Witness handles no money and isn’t affiliated with GiveWell or any listed charity. It’s a personal project for now. I built it with Lovable and Claude.

Comments are very welcome here, or message me directly. Thanks for reading.

Quentin
 

References

Aknin, L. B., Dunn, E. W., Whillans, A. V., Grant, A. M., & Norton, M. I. (2013). Making a difference matters: Impact unlocks the emotional benefits of prosocial spending. Journal of Economic Behavior & Organization, 88, 90–95. https://doi.org/10.1016/j.jebo.2013.01.008

Caviola, L., & Greene, J. D. (2023). Boosting the impact of charitable giving with donation bundling and micromatching. Science Advances, 9(3), eade7987. https://doi.org/10.1126/sciadv.ade7987

Cryder, C. E., Loewenstein, G., & Scheines, R. (2013). The donor is in the details. Organizational Behavior and Human Decision Processes, 120(1), 15–23. https://doi.org/10.1016/j.obhdp.2012.08.002

Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–668. https://doi.org/10.1037/0033-2909.125.6.627

GiveWell. (2024, April). How much does it cost to save a life? https://www.givewell.org/how-much-does-it-cost-to-save-a-life

Giving What We Can. (2025, June 3). GWWC’s 2023–2024 impact evaluation (executive summary). Effective Altruism Forum. https://forum.effectivealtruism.org/posts/AywMrGRvtA7Rq5b6r/gwwc-s-2023-2024-impact-evaluation-executive-summary

Karlan, D., & Wood, D. H. (2017). The effect of effectiveness: Donor response to aid effectiveness in a direct mail fundraising experiment. Journal of Behavioral and Experimental Economics, 66, 1–8. https://doi.org/10.1016/j.socec.2016.05.005

Sailer, M., & Homner, L. (2020). The gamification of learning: A meta-analysis. Educational Psychology Review, 32(1), 77–112. https://doi.org/10.1007/s10648-019-09498-w

Silverman, J., & Barasch, A. (2023). On or off track: How (broken) streaks affect consumer decisions. Journal of Consumer Research, 49(6), 1095–1117. https://doi.org/10.1093/jcr/ucac029

van der Bles, A. M., van der Linden, S., Freeman, A. L. J., & Spiegelhalter, D. J. (2020). The effects of communicating uncertainty on public trust in facts and numbers. Proceedings of the National Academy of Sciences, 117(14), 7672–7683. https://doi.org/10.1073/pnas.1913678117