A A Importância de Humanizar a Inteligência Artificial: a Decisão da Máquina por uma Ética Pluralista e Democrática
Bibliographic record
Abstract
A IA surge como ferramenta menos custosa e mais eficiente para a melhoria das funcoes cotidianas, sendo importante ao Direito. Estuda-se a machine learning e o dataset, de onde a maquina identificara padroes e decidira questoes sociojuridicas. Porem, deve-se evitar machine bias, prejudiciais para uma sociedade plural e democratica. Partindo das ideias de biopoder e dispositivos de controle, bem como do conceito de capitalismo de vigilância, busca-se fundar principios eticos que devam nortear o desenvolvimento do sistema de IA. Utiliza-se o modelo canadense – Montreal Declaration for a Responsible Development of Artificial Intelligence – e o enfoque das capacidades, com diretrizes inclusivas e humanizadas, evitando a visao estritamente negocial e competitiva. Busca-se a uniao entre governo, industria, academia e sociedade, para definicao de estrategias de uma etica para a IA e o Direito, cumprindo com preceitos de direitos humanos e da Constituicao Federal brasileira. Utiliza-se do metodo dedutivo, com a consulta a artigos cientificos e bibliografia para o desenvolvimento da pesquisa.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".