AI policy debates in Brazil: struggles over regulation, governance and labour
Bibliographic record
Abstract
This article explores Brazil’s artificial intelligence (AI) policy landscape through the lens of regulation, development and governance, focusing on the complex power struggles shaping its direction. As Brazil positions itself as a leader in AI in Latin America, debates over regulation reveal conflicting interests between Big Tech, national industry, unions and civil society. Although government plans like the Brazilian Artificial Intelligence Plan 2024–2028 aim to foster innovation and digital sovereignty, labour issues and workers’ rights remain marginalised. The article argues that AI governance in Brazil is a site of political dispute over who benefits from AI and under what terms. By unpacking these dynamics, the analysis highlights the urgent need for participatory AI governance models that foreground social justice and labour protections, particularly within the broader context of Latin American technological development. This article is part of the ‘Artificial Intelligence Observatory for the World of Work (AIPOWW) Symposium’.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".