Caring about the world isn't hard. Figuring out which of your options actually helps the most — that's the part we practice.
Effective altruism starts from an uncomfortable observation: not all ways of helping people are equally good. Some interventions save a life for a few thousand dollars. Others cost a hundred times more for the same result. If you're trying to do good with limited time or money, that difference matters enormously.
So instead of picking a cause because it's visible, local, or emotionally resonant, we try to ask a harder question first: of all the ways I could spend this hour or this dollar, which one does the most for the people or animals on the other end of it? Then we follow the evidence, even when it points somewhere unfamiliar.
It's not a political position or a single charity — it's a habit of asking "compared to what?" before we act.
We don't require members to agree on which cause matters most — only on how to reason about the question. These are the areas that come up most often in our discussions.
Malaria prevention, vitamin A supplementation, and cash transfers — interventions with some of the best-evidenced cost-per-life-saved numbers of anything we've found.
Billions of farmed animals live in conditions that get almost no public attention relative to their scale. We look at where advocacy and policy work actually move the needle.
As AI systems become more capable, the technical and policy work that shapes how safely that happens is short on people — including undergrads willing to learn fast.
Preventing the next pandemic is far cheaper than responding to one. We follow the research on early detection, biosafety norms, and policy gaps.
Eight weeks, one short reading and one discussion per week. It's not a lecture series — it's a small group of students actually arguing about what the evidence says, guided by a facilitator who's done it before. No prior knowledge of EA required.
A small student committee plans the fellowship, invites speakers, and handles the logistics nobody else wants to.