Expert rationale
Illustrative record · Herodotus, Histories 7.60
Data for the SI era
Ethora Labs produces expert-verified social science data for training and evaluating AI. Every claim traced to its source. Every disagreement kept with all its sides. Every judgment with the expert's reasoning.
Eight layers per record
Illustrative record · Herodotus, Histories 7.60
An abridged, illustrative record. Hover or select a layer to see what it holds.
01Thesis
Models now write code, prove theorems and generate the data that trains their successors. Technical capability feeds on itself.
Social and moral understanding does not work that way. In ethics, politics, religion and history there is rarely a single answer a machine can check. When models learn these questions from other models' output, diversity thins out and reasoning gets shallower.
Human thought follows technical change; it cannot generate its own ground truth. So the social and moral training of advanced models will keep depending on data made by people: sourced, argued, contested and checked by specialists. That is the data we build.
02What we deliver
Every product rests on one schema, so a history dataset and an ethics benchmark share the same provenance, quality metrics and delivery formats.
Expert-verified, multi-layer datasets licensed for training and fine-tuning. Non-exclusive, versioned and refreshed over time.
Training · Fine-tuning
Tests of pluralistic, reasoned social judgment that show where a model is one-sided, shallow or simply wrong.
Evaluation · Red-teaming
Custom collection of step-by-step reasoning from vetted specialists, in the disciplines and questions your model needs.
Custom programs
Sourced claims and perspectives your models can query, cite and stay current with.
Retrieval · Citation
03Method
Every release ships with the numbers behind it. This is how a record moves from source to delivery, and every change is logged: who made it, when, with which model and which guideline version.
A written annotation guide per discipline. Every record points to the version it was made under.
Models propose structure and candidates. Specialists accept, correct or reject each one.
Gold items are annotated twice, independently, by experts who cannot see each other's work.
A senior scholar resolves errors. Real scholarly disagreement is kept and labelled, not erased.
Inter-annotator agreement and error rates are reported per release. Test sets stay separate from training data.
Every dataset ships with its sources, rights, methods and known limits documented.
04Disciplines
We start where argument, interpretation and evidence are densest, then extend the same schema discipline by discipline. Only the domain ontology changes.
Starting with
Next
Planned
With a deliberate focus on traditions today's models know poorly.
These are often thin in training data and reach models mostly through translation. They are close to where our team works.
Tell us where your models fall short. We will show you what a layered dataset looks like for that problem.
The expert network is for historians, philosophers, sociologists, theologians and literary scholars who want their reasoning to shape how models think.