Identifying coping strategies trends with the ML (machine learning) threats to intellectual property
| Název česky | Identifikácia trendov stratégií zvládania hrozieb ML (strojového učenia) pre duševné vlastníctvo |
|---|---|
| Autoři | |
| Rok publikování | 2024 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Jusletter IT |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | https://jusletter-it.weblaw.ch/en/issues/2024/24-April-2024/identifying-coping-s_f6b80df874.html__ONCE&login=false |
| Doi | http://dx.doi.org/10.38023/a98ede0f-071f-418d-9049-edb5390cfc47 |
| Klíčová slova | IP-Law; Technology; Machine Learning; Coping Strategies |
| Popis | This article examines high-level research in the field of intellectual property (IP), with a particular focus on emerging trends and potential threats posed by machine learning technologies in selected legal environments. Using a high-level conceptual analysis, it explores the design of legal frameworks and regulatory responses in the European Union, the United Kingdom, and the United States, with a particular focus on preserving copyright and addressing the challenges posed by the paradigm shift in technological advancement through legal system responses based on a binary division between proactive and reactive solutions in each legal system in a comparative manner. The paper addresses two fundamental challenges: first, the adaptation of existing intellectual property laws in different territories to the rapid development of machine learning, and second, the proficiency of proactive and reactive solutions in overcoming these obstacles. The main ambition of this paper is to develop a conceptual framework that defines the legislative landscape in correlation with technological advances in IP and ML, highlighting dominant trends and existing measures. The main contribution of this paper is that it can highlight these trends and outline strategies for deeper analysis and coordinated responses, both academic and regulatory. Through this, the paper seeks to facilitate a more informed and harmonious integration of machine learning innovations into existing IP legal frameworks, balancing positives and negatives. |
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