A starting point for discussion
The idea
The researchers train models on some participants’ choices. They test the predictions against choices from participants whose data were not used to train the models.
Place in the reasoning
AI assessment · Connected to reasoning
Describes held-out-participant evaluation of individual predictions, not representation in civic decisions.
Different prediction checks reveal different limits
The design evaluates predictions against recorded choices within the study.
Consistent study results may not reflect the wider public
Cross-validation within the study does not establish that the data represent Brazil.
What this means and why it matters
What this means
The test checks whether a model trained on one group can predict choices made by people whose data it did not train on.
Why it matters
Testing on choices from people whose data were not used for training checks whether the model can predict beyond its training examples. It does not show how a system would perform in real decisions.
Conditions & assumptions
Source coverage: Full readable text of the retrieved 37-page research draft, including references and appendices. Figures are not independently interpreted. The researchers split participants into groups for training and testing. They compared the model’s predictions with choices from the test group.
Questions this connects to
AI-suggested relationships. Authors have not necessarily responded to one another.
Related claims & perspectives
AI-suggested relationships. Authors have not necessarily responded to one another.