Current AI moral reasoning treats ethics like a classification problem. MHF treats it like what it actually is: hierarchical constraint propagation through relationships. Here is how we ground the theory in real human data.
No single dataset captures moral reasoning. Reddit gives us scale and rawness. UniMoral gives us individual moral profiles. Pew gives us cross-national ground truth. Together, they let MHF do something no existing system can: parameterize moral judgment by culture, community, and conviction -- then validate the predictions against real human data.
The largest crowdsourced moral judgment dataset in existence. Real people, real dilemmas, community verdicts. We mine it for secular norms, contested cases, and the raw material of everyday ethics.
Deep diveThe first dataset that connects each annotator's own Haidt moral foundations profile to their actual moral judgments. This is our killer validation: parameterize MHF with their profile, predict their verdict.
Deep diveNationally representative attitudes on abortion, alcohol, homosexuality, gambling, and divorce -- exactly the fault lines where Christian and secular parameterizations diverge. Our cross-cultural ground truth.
Deep diveEvery component in MHF has a data source. The table below maps what we build, where the data originates, and how the Christian and secular parameterizations diverge.
| What We Build | Source | Christian Context | Secular Context |
|---|---|---|---|
| Root Node | Architecture | God (Scripture, TLR Protocol). Authority=0.90, Sanctity=0.95 | Social Consensus (AITA norms). Authority=0.09, Sanctity=0.07 |
| Haidt Profile Weights | Social Chemistry 101 KJV Bible | Care 0.80 / Fairness 0.60 / Loyalty 0.75 / Authority 0.90 / Sanctity 0.95 / Liberty 0.45 | Care 0.47 / Fairness 0.18 / Loyalty 0.19 / Authority 0.09 / Sanctity 0.07 / Liberty 0.00 |
| Relationship Weights | Social Chem Theology | God-Self 1.0, Spouse 0.9, Parent-Child 0.85, Church 0.7, Enemy 0.3 | Spouse 0.91, Stranger 0.91, Parent-Child 0.90, Friend 0.85, Community 0.87 |
| Constraint Library | BSB Norm Bank 1.7M | Scripture-derived commandments with internal exception logic | Crowdsourced rules-of-thumb with agreement thresholds |
| Scenario Graph Extraction | LLM Pipeline | Same pipeline, different baseline weights applied | Same pipeline, secular baseline weights applied |
| Cultural Validation | UniMoral Pew | High-Authority+Sanctity countries should match Christian params | WEIRD-country profiles should match secular params |
| Perturbation Tests | Generated | 25/25 pass (100%). Relationship changes flip judgments as predicted. | |
Raw moral data enters from three directions: crowdsourced norms, normative texts, and survey responses. Each feeds a different layer of the Moral Hierarchy Graph before constraint propagation produces a prescriptive judgment.
AITA posts, Bible verses, Pew responses, UniMoral dilemmas + annotator profiles
Social Chemistry RoTs, scripture constraints, Haidt foundation labels, cross-cultural profiles
Haidt profile vectors, relationship base weights, constraint strength scores
Stakeholder nodes, obligation edges, parameterized root node, exception conditions
Bottom-up evidence, top-down decisions, prescriptive output with full auditability
Every existing moral AI -- Delphi, ETHICS, MoReBench -- treats morality as flat classification. Good, bad, or "it depends." They cannot explain whose morality, why it matters, or what happens when you change the relational context. MHF can.
The three datasets below are not decorative. AITA gives us the secular Overton window. UniMoral lets us predict individual judgments from moral profiles. Pew lets us validate across 25 countries. Together, they let us move from "here are some things to consider" to "Because of X, you should do Y" -- with the receipts to show exactly why.