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Record W7054825253

Artificiell intelligens inom upphovsrätt och behovet av internationell harmonisering

2024· other· en· W7054825253 on OpenAlexaboutno aff

Bibliographic record

VenueLund University Publications Student Papers (Lund University) · 2024
Typeother
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertyConventionField (mathematics)Value (mathematics)Subject (documents)Human rights
DOInot available

Abstract

fetched live from OpenAlex

The development of artificial intelligence (AI) has accelerated rapidly in recent years, and AI tools that can be used for advanced cognitive and perceptual tasks are appearing in all areas of life. It did not take long before it was also realized that AI could be used for literature and art. In a matter of seconds, advanced works of art and literature can be produced at the touch of a button using so-called generative AI. This has led many to question whether, to what extent and on what grounds protection can be obtained for this category of works. Compared to other areas of law, intellectual property law, and in particular copyright law, has been subject to extensive international harmonisation efforts. This is because intellectual property rights, which, unlike tangible goods, cannot be physically confined within the borders of a particular country, are by their nature highly international. The desire to ensure that the rights of national authors are also protected abroad, together with the value of intellectual property rights as international commodities, has therefore led to a number of international conventions, such as the Berne Convention for the Protection of Literary and Artistic Works, which together establish a harmonised global minimum level of protection for copyright works. The advent of AI in the field of copyright raises the question of whether this relative global consensus has been disrupted. Unlike previous technologies that have impacted copyright, AI is fundamentally new in that it reduces the need for human creativity, or perhaps replaces it altogether. Even at this early stage, it is clear that different countries have taken different approaches to how AI works should be protected under their respective copyright regimes. In the United States, a series of decisions by the United States Copyright Office suggest that the use of so-called prompts to create works is not considered sufficient for copyright protection, regardless of how many such prompts are used. In contrast, countries such as the United Kingdom, Ireland, New Zealand, Hong Kong, India and South Africa offer specific protection for works created by computers without human intervention, and countries such as Canada and India have allowed the AI tool itself to be registered as a co-author of the work. This raises the question of whether the international copyright framework is sufficient to deal with developments in AI, or whether there is a need for further harmonisation. The paper examines this question from a number of perspectives, including economic, ethical and legal. The conclusion is that several circumstances indicate that further harmonisation is desirable, at least from a Swedish perspective.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.538
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.014
GPT teacher head0.204
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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