Do Better: The Limits and Lessons of Effective Altruism

Inspired by: “Do Better” by Gideon Lewis-Kraus, The New Yorker, August 15, 2022

Introduction: Doing Good, Smarter—but at What Cost?

Effective altruism began as a compelling thesis: by applying rigorous evidence and careful analysis, we could ensure that each dollar donated or hour volunteered produced the most benefit. Inspired by thinkers like William MacAskill, EA practitioners evaluate charities and interventions against metrics such as cost-effectiveness and scale, channeling resources toward the highest-impact opportunities. But when Lewis-Kraus takes us behind the scenes, we see that this data-driven model has its own blind spots—and raises difficult questions about values, equity, and human complexity.

Literary Reflection

In his profile, Lewis-Kraus depicts MacAskill as a thoughtful idealist who grapples with the weight of his own philosophy. MacAskill’s charisma and clarity draw others into what can feel like a moral crusade: a movement where rational calculation promises to turbocharge generosity. Yet Lewis-Kraus also notes moments of tension—between the movement’s utopian aspirations and the messy realities of human suffering. When EA advocates debate which cause to prioritize—malaria prevention versus animal welfare versus existential risk—the underlying emotional stakes become painfully clear.

Systems Reflection

Viewed through a Humboldt’s Home lens, effective altruism exemplifies the strengths and perils of systems thinking. By mapping global needs, quantifying outcomes, and optimizing resource flows, EA offers a model of precision philanthropy. But systems are only as good as their inputs and assumptions. A focus on measurable results can overlook structural injustices that defy simple metrics—like systemic poverty or cultural marginalization. In striving to maximize impact, EA risks treating people as data points rather than complex individuals embedded within their own social contexts.

Broader Implications

MacAskill’s intentions remain firmly altruistic, yet Lewis-Kraus cautions against unexamined faith in algorithms and rankings. When donors rely exclusively on cost-effectiveness scores, they may inadvertently perpetuate hidden biases—favoring causes that are easier to quantify over those that matter deeply to affected communities. Moreover, an overemphasis on future benefits can divert attention from urgent needs in the present. For example, existential risk research may promise to save billions of potential lives, but it offers limited solace to families suffering malaria today.

SIDEBAR: Tensions in Moral Calculus

• EA’s focus on efficient giving can undercut efforts aimed at structural change, which may yield broader social justice over time.
• Prioritizing the welfare of future generations sometimes conflicts with commitments to alleviate immediate suffering.
• Quantitative metrics capture scale but struggle with qualitative values like dignity, autonomy, and cultural integrity.
• A strict utilitarian framework can ignore systemic factors that require collective action beyond individual donations.

CLASSROOM PROMPTS (Upper Grades)

• Ethics & Philosophy: Is it moral to allocate resources based solely on numerical estimates of impact? How should we balance quantifiable outcomes against qualitative human experiences?
• Economics & Policy: Compare the cost-effectiveness of two charitable interventions and discuss non-metric priorities that might still justify supporting the less efficient option.
• Sociology: Analyze how power dynamics and cultural context might shape which causes receive funding under an EA framework.
• Creative Writing: Craft a dialogue between an EA advocate and a local community leader who emphasizes immediate, context-specific needs.

Conclusion: Beyond the Algorithm

Effective altruism offers a powerful toolkit for maximizing good, but it is not a panacea. As Lewis-Kraus demonstrates, embracing data-driven compassion requires humility—acknowledging that not all values can be reduced to numbers. The conversation must expand to include voices from diverse communities, ethical perspectives beyond utilitarianism, and a willingness to sit with complexity and moral ambiguity. In doing so, we can honor the original EA goal of doing better—while resisting the urge to oversimplify what it truly means to help one another.