Effective Altruism

Do the most good you can with what you have. Effective altruism (EA) applies evidence and reason to the question of how to help others: compare interventions by impact (lives saved, welfare gained per dollar), prioritise the best options, and follow the evidence even when it points away from intuitive or emotionally salient causes. The movement emerged in the 2000s–2010s, drawing on utilitarian ethics, development economics, and prioritisation research. The core ideas are cause neutrality (choose the cause that does the most good, not the one that feels best), marginal impact (your next dollar or hour goes where it helps most), and openness to revision (update as evidence changes).

EA doesn't prescribe a single cause. It prescribes a method: estimate impact, compare, prioritise. In practice, many EAs have focused on global health (e.g. bed nets, deworming, cash transfers), animal welfare (factory farming, wild animal suffering), and long-term risk (AI safety, pandemic preparedness) — not because those are morally special but because rough cost-effectiveness estimates suggest they may offer very high impact per unit of resource. The discipline is to ask: where does my next unit of time or money do the most good? The answer may be counterintuitive (e.g. earning to give, or working on a cause that feels abstract).

Critics argue that EA over-relies on measurable outcomes, underweights non-quantifiable values, and can justify odd conclusions (e.g. that one might do more good in finance than in direct service). The response is that measurement is imperfect but better than intuition alone, and that cause neutrality is a default to be tempered by evidence and values, not abandoned. The strategic use is not to adopt every EA conclusion but to adopt the habit: compare impact, prioritise, and update.