Contribution to the handbook
Supporting · Assessed 2026-10-28
The described group comparisons and balancing method are useful testing context, but this narrow methodological account does not itself establish outcomes, mitigation, or a governance design choice.
Does this challenge the goal?
In-scope gap
The question concerns how to test group differences within the existing goal; it does not challenge the goal or beneficiary boundary.
The supplied records describe methods but do not establish that balancing detects or removes all relevant forms of bias.
Why this category?
- Identifies the participant characteristics examined and the method used to balance groups.
- Clarifies that the methods describe an investigation and do not prove that balancing removes all bias or establish the results.
The evidence does not provide complete results or establish that the participant categories or balancing procedure capture all relevant sources of unequal prediction.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
- How do the authors plan to examine differences in prediction across participant groups?
- Comparing participant groups
- Testing group accuracy
- Portuguese prompt example
- Participant details used as context
- The volunteer policy data
- Accuracy and bias varied
- Accuracy varied across groups
- Age and sex results varied
- How group accuracy was checked
Tests, stop conditions and unresolved work →
Read the exploration goalNew evidence is being assessed. Any answer below is provisional; parts whose evidence changed or was withdrawn are withheld.
Identify the participant characteristics to be compared and how the researchers plan to balance groups for those comparisons.
What we can say so far
The authors planned to compare prediction accuracy across participants’ self-reported political ideology, age, education, and sex. For comparisons where one group was overrepresented, they planned to downsample the larger group to create balanced samples, retrain the models, and assess accuracy separately for each group.
What remains open
No material gap is recorded within this part’s current scope.
Conditions and limits
- This describes the authors’ reported plan and method; it does not establish that balancing eliminates every source of bias.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
supports
Comparing participant groups The authors plan to examine whether models predict choices differently across participants’ self-reported political ideology, age, education, and sex. For these comparisons, they balance the groups by downsampling the group with more participants; the supplied text does not include the results.
Identifies the four characteristics compared and says the larger group was downsampled.
Large Language Models (LLMs) as Agents for Augmented Democracy
Read full source: Large Language Models (LLMs) as Agents for Augmented Democracy (opens in a new tab) ↗Read source discussion: Large Language Models (LLMs) as Agents for Augmented Democracy →
context
Portuguese prompt example For a robustness check, the researchers made Brazilian Portuguese prompts describing participant characteristics. The features included age (young or older/“third age”), political ideology (conservative, centrist, or left-wing), urban or rural zone, education (with or without higher education), sex (male or female), and city and state. One example described a 26-year-old liberal man with a master’s degree in Rio de Janeiro choosing a proposal to curb tax evasion over one to expand privatization of state-owned companies and infrastructure concessions.
Describes demographic features in a Portuguese-prompt robustness check, not the group comparisons asked about.