Artificial Intelligence and the 2020 Municipal Elections in Brazil
Bibliographic record
Abstract
The main purpose is to examine the possible role of artificial intelligence (AI) in the uncertain context of the 2020 municipal elections in Brazil. The central argument indicates that, regardless of when the elections are held, the COVID-19 pandemic opened spaces for candidates to build their political platforms on the initiatives to combat the disease, but also the opportunity for the dissemination of fake news and profiles regarding the spread of the new coronavirus and social distancing and quarantine measures with political purposes. The electoral discourse has increasingly used technologies and data such as voters’ concerns, preferences, and oppositions, collected on social networks through AI. New data-based technologies can give rise to an unreal, induced, forged public opinion, in the same way that they can bring greater possibilities of discernment to the voter. The situation requires a more robust regulation for AI, but there are still many unregulated aspects and obstacles for the implementation of an effective regulation of online activities in Brazil, such as the poor adaptation of the legal space to highly volatile phenomena.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".