Strategies for addressing the lack of artificial intelligence regulations to protect consumers in digital communication
Bibliographic record
Abstract
The rapid development and implementation of Artificial Intelligence (AI) present substantial legal challenges for the digital society. While AI has the potential to offer numerous benefits, concerns about commercial exploitation and unforeseen technological risks have led many countries to seek appropriate legal frameworks to prevent potential harm. The era of digital communication presents artificial intelligence. AI should be developed to provide more optimal consumer protection, but the presence of artificial intelligence also has a negative impact with various criminal acts that occur in digital transactions. Therefore, regulations are needed to provide more optimal consumer protection. So far, regulations related to AI are still very limited, so they need to be developed. This research is very important to do. Therefore, this study conducted a study related to strategies to overcome the lack of regulations related to AI to provide consumer protection in the digital communication era. In addition, this study provides an overview of AI regulations carried out by developed countries, such as the United States, Canada, China, the European Union, and South Korea. Employing normative legal research methods, the study utilizes a statutory regulation approach, drawing on both primary data sources and secondary legal materials. All data is analyzed qualitatively. The development of AI has prompted various countries to establish regulations to protect consumers from potential risks posed by this technology. Based on these policies, it can be seen that some countries already have regulations governing the use of AI with a focus on digital communication and consumer protection. Addressing the lack of AI regulations to protect consumers in digital communication involves a combination of strategies aimed at ensuring transparency, fairness, accountability, and safety in AI technologies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| 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".