MétaCan
Menu
Back to cohort
Record W2967400214 · doi:10.3968/11144

Coordination of Legal Protection of Algorithms and Intellectual Property System

2019· article· en· W2967400214 on OpenAlexvenueno aff
Lin Cai, Zihang Wang

Bibliographic record

VenueCanadian social science · 2019
Typearticle
Languageen
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertyComputer scienceContext (archaeology)AlgorithmContradictionPromotion (chess)Order (exchange)Object (grammar)Law and economicsLawEconomicsArtificial intelligencePoliticsPolitical science

Abstract

fetched live from OpenAlex

In the context of the intelligent revolution, the algorithm is increasingly becoming an important tool for assisting decision-making and regulating order. Because of the professionalism and opacity of the algorithm, a series of challenges of legal rules and legal order will occur if there is no market access mechanism and post-mortem supervision. Based on the analysis of the intellectual property protection of the algorithm and the essence of the intelligent society, this paper reveals that the algorithm is the endogenous power of the intelligent society. The intellectual property protection of the algorithm is in line with the value needs of the essence of the intelligent society, which is the necessary system for the rapid development of the intelligent society in the future. The existing algorithm protection methods include copyright, trade secrets, and patent rights. The current coverage is not wide enough, the protection effect is weak, and it is easy to trigger new social problems, which can hinder the protection of social benefits and the promotion of technological progress. The authors believe that the patent law “public change protection” mechanism can not only alleviate the contradiction between “algorithm power” and public interest but also stimulate the development of algorithm technology. An algorithm is a technical solution, and it is also a rule of thinking. The algorithm has the characteristics of technical solutions and thinking rules, which is different from pure thought rules and can produce “changes in the physical state”. Therefore, it should be protected as an object of the patent law. It is necessary to determine the patent-ability standard of the algorithm as soon as possible. The algorithm acts as a new type of object protected by the patent law directly, and at the same time, it sets the algorithm value evaluation mechanism. Finally, through the system construction of algorithm protection, the intellectual property law can be used to promote the innovation of algorithms, so that the algorithm can be developed in a more rational, ethical and legal direction to boost the rapid development of intelligent society.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.017
GPT teacher head0.209
Teacher spread0.192 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCanadian social scienceSame topicLaw, AI, and Intellectual PropertyFrench-language works237,207