Executive summary: The post argues that deontological moral theories dangerously misprioritize abstract metaphysical distinctions—such as doing vs. allowing or agential causation—over real human lives, and that properly framed utilitarian reasoning leads to more defensible and humane decisions in life-and-death cases.

Key points:

1. The author critiques deontology for prioritizing metaphysical concepts (e.g., “doing” vs. “allowing”) over actual human outcomes, which can lead to preventable deaths.
2. Common objections to utilitarianism—such as its clash with moral language intuitions—are seen as superficial and addressable through strategies like fictionalism or dual-level theories.
3. By contrast, deontology lacks a compelling rationale for its moral verdicts, often hinging on distinctions irrelevant to the well-being of those affected.
4. A thought experiment involving a wire-decapitated passerby illustrates that utilitarian decisions can align with intuitive judgments when abstracted from immediate agency, undermining the supposed horror of “Transplant”-style reasoning.
5. The author notes that real-world rules against instrumental killing (e.g., doctors harvesting organs) are better justified on utilitarian grounds of expected consequences than on deontological absolutes.
6. The post is a philosophical critique leveraging intuition pumps and reductio-style examples to undermine metaphysically grounded deontology in favor of outcome-focused utilitarianism.

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Executive summary: This exploratory post argues that many cross-cause prioritization judgments in Effective Altruism (EA) rely on philosophical arguments that are too fragile, underdeveloped, and contentious to justify high confidence, and calls for greater humility, skepticism, and diversification in how cause prioritization is approached.

Key points:

1. Philosophical foundations of cause prioritization are often weak and contested: High-stakes comparisons between causes like global health, animal welfare, and existential risk rely on contentious philosophical assumptions (e.g., population ethics, decision theory) where even specialists disagree and evidence is largely intuitive or inconclusive.
2. Aggregation methods yield dramatically different results and are themselves underdefined: Tools like Rethink Priorities’ moral parliament show that depending on how you aggregate across moral theories, you might end up prioritizing entirely different cause areas—even with the same inputs.
3. We should treat philosophical evidence with the same skepticism often applied to empirical studies: EA norms promote caution around empirical findings (e.g., needing replication for RCTs); similarly, philosophical conclusions—especially recent ones—should not be assumed robust just because they seem internally coherent.
4. Overconfidence in philosophical conclusions risks distorting decision-making: Given the fragility of many key premises, strong endorsements of specific causes or interventions often outpace the available justification, especially when they rest on specific philosophical worldviews or aggregation methods.
5. Calls for epistemic humility and practical diversification: Instead of treating EA as settled answers, we should treat it as a method for inquiry, remain open to pluralistic approaches, and explicitly consider uncertainty in cause prioritization and funding decisions.
6. Relying on intuitions, “common sense,” or anti-realist views doesn’t resolve the uncertainty: These alternatives fail to escape the need for explicit reasoning and risk undermining EA’s foundational commitment to evidence and argument.

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Executive summary: This post from Wild Animal Initiative outlines current approaches to assessing wild animal welfare using the arousal-valence model, indicators, and composite metrics—highlighting both promising tools and the need for further validation, especially for wild species. Key points:

1. Welfare is defined as an animal’s valenced affective state—how positive or negative their experiences are over time—often visualized through an arousal-valence model where valence represents emotional quality and arousal represents intensity.
2. Welfare can’t be measured directly, so scientists infer it using indicators (behavioral, physiological, and environmental) and welfare metrics that aggregate multiple indicators.
3. Whole-animal indicators (like Qualitative Behavioral Assessment or activity budgets) provide a broad view of emotional state, while partial indicators (like fear behaviors or hormone levels) target specific components.
4. Environmental indicators assess potential welfare risks (e.g., predation) rather than current welfare states and are thus less direct.
5. Existing welfare metrics like the Five Domains Model and cumulative pain scores are in use, but most were developed for captive animals and need adaptation for wild contexts.
6. Validation of new indicators is a research priority, with current efforts focused on tools like cognitive bias tests for birds and bees, frailty indexes for insects, and non-invasive stress measurements such as fecal glucocorticoid metabolites.

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Executive summary: This post argues that the effective altruism biosecurity community should invest more in precision pandemic response strategies—targeted, data-informed interventions enabled by modern surveillance tools—as a complementary approach to existing focus areas like prevention and detection, citing their demonstrated value in past public health successes and untapped potential for mitigating global catastrophic biological risks.

Key points:

1. Historical evidence from smallpox eradication shows that novel, targeted disease control strategies can dramatically improve pandemic outcomes without relying solely on technological innovations.
2. Recent advances in surveillance—predictive analytics, spatial analysis, and real-time data systems—enable pre-emptive and geographically precise public health interventions.
3. Precision pandemic response strategies include proactive infrastructure planning, strategic resource allocation, targeted social distancing, and adaptive protocols that dynamically respond to changing epidemiological conditions.
4. These strategies can mitigate harm more efficiently than blanket measures like national lockdowns, especially when guided by timely and localized data.
5. The effective altruism community can contribute by advocating for integration of these strategies into preparedness plans, funding related implementation research, and building networks to scale best practices.
6. While not arguing against prevention and detection investments, the post emphasizes that precision response is underexplored and likely tractable, warranting further evaluation for its relative cost-effectiveness.

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Executive summary: This exploratory analysis outlines how transformative AI may reshape various animal advocacy interventions—potentially enhancing impact through automation, predictive modeling, and coordination tools, while also introducing symmetrical threats from opposition groups and risks to credibility, signaling an urgent need for proactive, strategic adaptation by the movement.

Key points:

1. Symmetrical access to AI tools means both animal advocates and opposing industries (e.g., animal agriculture) can use similar capabilities, making it critical to seek and exploit potential asymmetries (e.g., greater adaptability of startups).
2. Predictive modeling and automation could dramatically enhance advocacy efforts in targeting, strategy design, research, and outreach, while also shifting key labor bottlenecks.
3. Information and attention dynamics may worsen due to AI-generated content flooding media and undermining authenticity and trust, suggesting the need for novel communication strategies.
4. For each of the five interventions examined, AI presents both specific opportunities (e.g., AI-aided monitoring in corporate outreach, multilingual coordination in network building, efficient legislative drafting in government outreach) and distinct risks (e.g., system gaming by corporations, infighting within movements, overwhelming policymakers with AI-generated messages).
5. Strategic preparedness—including infrastructure development, AI upskilling, tracking of opposition AI use, and reevaluation of cost-effectiveness—will likely determine whether AI acts as a force multiplier or hindrance to animal advocacy.
6. Uncertainties remain about how AI will affect certain dynamics (e.g., nudge effectiveness, control of AI systems, credibility risks), reinforcing the need for ongoing assessment and flexibility.

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Executive summary: Based on a broad survey of clinical and observational research, this evidence-based analysis concludes that reduced risk tobacco products—particularly e-cigarettes and snus—are at least as effective, and often more effective at a population level, than other cessation methods for helping smokers quit, supporting their role as a viable tool in tobacco harm reduction strategies.

Key points:

1. Comparative effectiveness: Randomized controlled trials show that nicotine vapes are about 50% more effective than nicotine replacement therapy (NRT), and similarly effective to pharmaceutical aids like varenicline.
2. Population-level impact: Observational data from countries like Sweden, Norway, England, and Japan show significant smoking reductions associated with increased availability and use of noncombustible products, often surpassing the impact of conventional cessation tools.
3. User preference and uptake: Noncombustibles, particularly vapes, are far more commonly used than traditional methods, leading to greater absolute numbers of successful quitters despite similar per-attempt effectiveness.
4. Limitations of existing methods: Unassisted quitting remains the most common but least effective method, while pharmaceutical and behavioral interventions show limited effectiveness and adoption, especially outside clinical settings.
5. Causal uncertainty and correlation caveats: While real-world data are compelling, causal claims are limited by the observational nature of most studies and potential confounding factors.
6. Implication for effective altruism: The evidence suggests that concerns about the effectiveness of reduced risk products should not be a major obstacle to considering tobacco harm reduction a high-impact cause area.

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Executive summary: This first part of a two-part review passionately argues that global depopulation poses a serious threat to human progress and well-being, emphasizing that more people mean more innovation, economic capacity, and moral value—while rebutting common concerns like climate change with the claim that technological solutions, not population decline, are what really matter.

Key points:

1. Depopulation is an underrecognized existential risk: The world has passed “peak baby” and is on track for a steep population decline, which could lead to a premature end to humanity’s story if current fertility trends continue.
2. Climate change won’t be solved by fewer people: Spears and Geruso argue that since we must decarbonize in the next few decades, depopulation occurring after 2080 is irrelevant or even harmful, as it reduces the capacity for innovation and infrastructure.
3. Innovation is driven by population size: The book highlights how more people lead to more ideas, technologies, and economic specialization—benefits that shrink in a depopulated world, even if no one is exceptionally talented.
4. People are not competitors for scarce resources—they are creators: The author pushes back on zero-sum thinking by emphasizing that having more people increases the chance that someone creates what you most value.
5. Ethically, more good lives are better: The post endorses the principle that a world with more happy, flourishing people is better than one with fewer, so long as those lives are worth living.
6. Debate should focus on externalities and intrinsic value—not coercion or racial anxieties: The author urges readers to steer clear of conflating concern about depopulation with reproductive authoritarianism or far-right ideology, emphasizing reproductive freedom and global well-being instead.

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Executive summary: This impassioned and data-driven essay argues that honey consumption likely causes vastly more animal suffering than any other commonly consumed animal product—due to the sheer number of bees affected and the severe harms they endure—making honey ethically worse than even factory-farmed meat or foie gras.

Key points:

1. Honey likely causes more suffering than any other animal product per kilogram consumed, with over 200,000 bee-days of farming required per kg—dwarfing the animal-days associated with beef, eggs, or chicken.
2. Bees in commercial honey production endure extremely poor welfare conditions, including malnutrition, painful deaths, chronic stress from inspections and transport, and mass die-offs from cold, starvation, and disease.
3. Bees display surprising signs of cognitive sophistication and sentience, meeting most behavioral criteria used to infer consciousness—suggesting they may suffer in ways ethically significant to humans.
4. Even under conservative assumptions, the post estimates that eating a kilogram of honey may be hundreds or thousands of times worse (in terms of suffering caused) than eating a kilogram of chicken.
5. Arguments that honey supports environmental goals or pollination are misleading or oversimplified, as the commercial honey industry often harms wild pollinators and ecosystems.
6. The author urges vegans and others to reconsider honey consumption, arguing that abstaining from it is a relatively easy, high-impact way to reduce animal suffering.

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Executive summary: This post critiques Bentham's Bulldog’s recent defense of moral realism on the EA Forum, arguing that none of his linguistic, rationality-based, or intuition-driven arguments successfully establish objective morality—though the author remains agnostic and sees moral realism as plausible only if theism is true.

Key points:

1. Linguistic arguments are unconvincing: The author argues that the fact moral claims are treated propositionally in English doesn’t imply they are objectively true—just as gendered nouns in other languages don’t reflect objective gender properties.
2. Rationality doesn’t require realism: BB claims that moral anti-realists must deny that obviously irrational actions (e.g., self-torture) are irrational; the author rebuts this by distinguishing between being rational (acting on one’s desires) and whether one should have certain desires, noting that anti-realists can still affirm rational norms without objective morality.
3. Moral intuitions lack testability: Unlike perceptual, mathematical, or logical intuitions, moral intuitions cannot be empirically tested, making them less epistemically reliable—especially since evolution wouldn’t have selected for their accuracy.
4. Intuition isn’t enough: BB’s appeal to intuition as justification for moral realism is undermined by the historical fallibility and untestability of moral intuitions; the author emphasizes that seeming true isn’t sufficient evidence in this domain.
5. Conditional credence in realism: The author finds moral realism plausible if theism is true (since a moral God could instill accurate intuitions), but sees it as implausible under atheism due to the lack of any grounding for objective moral truth.
6. Summary judgment: While moral anti-realism may feel “weird,” the author contends that weirdness is not a defeater, and remains unconvinced by BB’s arguments in favor of moral realism.

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Executive summary: This exploratory essay proposes that marginal work on “Optimal Reflection”—humanity’s deliberate effort to figure out what it should do with the deep future—may be more valuable than AI safety work aimed at preventing extinction, due to its greater neglectedness, potentially high tractability, and essential role in avoiding suboptimal value lock-in; however, the author does not currently endorse any conclusions and seeks feedback on this early-stage model.

Key points:

1. Definition and significance of Optimal Reflection (OR): OR refers to the most effective, feasible, and actionable process for evaluating all crucial considerations about humanity’s future—examples include the Long Reflection, Coherent Extrapolated Volition, and Good Reflective Governance.
2. Preliminary model suggests OR may be ~5x higher EV than AI safety work: Despite the overwhelming focus on extinction prevention, early modeling indicates OR might yield more expected value on the current margin due to its neglectedness and high potential upside.
3. OR is especially crucial due to the risk of “value lock-in”: As humanity approaches the capability to irreversibly shape the future (e.g., via advanced AI), a small window remains to positively “lock in” the conditions for future flourishing; OR helps ensure this lock-in is intentional, inclusive, and wisely chosen.
4. Marginal tractability is the decisive crux: While both cause areas are vital, OR may currently offer more low-hanging fruit due to a smaller existing field (rough estimate: <100 people working on OR vs. ~1,200 in AI safety) and a large, motivated potential workforce.
5. Risks and dependencies acknowledged: The essay stresses that OR only matters conditional on surviving AI-related extinction risks, and political buy-in—especially from AI labs and policymakers—may be harder to secure for OR than for AI safety.
6. Author is seeking feedback and emphasizes tentativeness: The post is part of Existential Choices Debate Week, and the author is explicit about the speculative and preliminary nature of the claims, encouraging critical engagement from readers.

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