Konstitusi dan Kekosongan Regulasi AI: Tantangan Bagi Perlindungan Hak Asasi Manusia di Indonesia
Bibliographic record
Abstract
The advancement of artificial intelligence (AI) technology has posed serious challenges to the protection of human rights in Indonesia. Although Articles 28A to 28J of the 1945 Constitution guarantee constitutional rights such as privacy, justice, and non-discriminatory treatment, there is currently no specific and comprehensive regulation governing the use and governance of AI. This regulatory gap opens the door to systematic yet invisible civil rights violations, such as algorithmic discrimination, breaches of personal data, and the lack of legal accountability for decisions made by AI systems. This research employs a normative-juridical approach by examining national legal provisions and comparing them with international legal frameworks, such as the European Union’s Artificial Intelligence Act and Canada’s Artificial Intelligence and Data Act. The findings indicate that Indonesia is not yet normatively prepared to anticipate the impacts of AI on human rights. Therefore, there is an urgent need for AI regulation that is grounded in constitutional values, technological ethics, and the principle of accountability, to ensure that digital innovation does not erode citizens’ fundamental rights, but rather supports the rule of law and democracy in the era of technological transformation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".