AI ClaimsRapid review

Joshua Greene · 2013

Moral Tribes

Emotion, Reason, and the Gap Between Us and Them

Cover via Open Library

Rough AI truth score

58/100

Greene is on strong ground that moral judgment combines affective intuitions with controlled reasoning and that parochial cooperation can coexist with intergroup bias. The controversial step is normative and empirical: sacrificial-dilemma data do not show that deliberation reliably produces impartial utilitarianism, nor that one consequentialist meta-morality will resolve value conflict across groups.

Based on three central claims · medium confidence

The three claims

01Mostly supported

Moral judgment draws on both automatic affective responses and more controlled deliberative processes.

Behavioral, lesion, timing, and neuroimaging studies support multiple interacting processes in moral judgment. The mapping is not one-to-one: emotion can support consequentialist choices, deliberation can support rules, and sacrificial dilemmas measure only a narrow slice of morality.

02Mostly supported

Ingroup cooperation can coexist with outgroup bias and intergroup conflict.

Social-identity, cooperation, and conflict research documents ingroup favoritism and context-dependent outgroup derogation. Group boundaries can also organize trust and collective action without hostility; institutions, cross-cutting identities, norms, and interdependence alter the relationship.

03Weak

Controlled reasoning reliably yields utilitarian judgments suitable as a shared global meta-morality.

Reflection sometimes shifts answers on sacrificial dilemmas, but preregistered replications and conceptual critiques weaken the claim that the shift represents impartial utilitarian reasoning. Moving from cognitive process evidence to a uniquely correct public morality also requires normative premises the experiments cannot supply.

Other claims worth checking
  • Moral intuitions that coordinate within groups can create conflict between groups.
  • A common decision procedure still requires legitimate institutions and agreement about rights and aggregation.

What this number means. It is an AI-generated first-pass judgment of three central factual or causal claims—not a rating, exhaustive fact-check, or human peer review. Claim credits are 100% for supported, 75% for mostly supported, 50% for mixed, and 25% for weak, then averaged and rounded. Lower confidence means the score should move more as better evidence arrives.

Method three-central-claims/0.1.0 · checked 2026-09-01 · 3/3 selected claims assessed · method and source audit