Identifying coping strategies trends with the ML (machine
learning) threats to intellectual property

k 2024

Identifying coping strategies trends with the ML (machine learning) threats to intellectual property

PALAŠTA, Damián

Základní údaje

Originální název

Identifying coping strategies trends with the ML (machine learning) threats to intellectual property

Název česky

Identifikace trendů strategií zvládání s hrozbami ML (strojového učení) pro duševní vlastnictví

Autoři

PALAŠTA, Damián (703 Slovensko, garant, domácí)

Vydání

IRI§24 – International Legal Informatics Symposion, 2024

Další údaje

Jazyk

angličtina

Typ výsledku

Prezentace na konferencích

Obor

50501 Law

Stát vydavatele

Rakousko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Organizační jednotka

Právnická fakulta

Klíčová slova anglicky

IP-Law; Technology; Machine Learning; Coping Strategies

Změněno: 14. 4. 2024 23:54, Mgr. Damián Palašta

Anotace

V originále

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.

Návaznosti

MUNI/A/1529/2023, interní kód MU

Název: Právo a technologie XII

Investor: Masarykova univerzita, Právo a technologie XII

Citovat

PALAŠTA, Damián. Identifying coping strategies trends with the ML (machine learning) threats to intellectual property. In IRI§24 – International Legal Informatics Symposion. 2024.

@proceedings{2393687,
   author = {Palašta, Damián},
   booktitle = {IRI§24 – International Legal Informatics Symposion},
   keywords = {IP-Law; Technology; Machine Learning; Coping Strategies},
   language = {eng},
   title = {Identifying coping strategies trends with the ML (machine learning) threats to intellectual property},
   url = {https://iris-conferences.eu/en/international-legal-informatics-symposion-wed-14-sat-17-february-2024-salzburg-and-online-in-vienna},
   year = {2024}
}

TY  - CONF
ID  - 2393687
AU  - Palašta, Damián
PY  - 2024
TI  - Identifying coping strategies trends with the ML (machine learning) threats to intellectual property
KW  - IP-Law
KW  - Technology
KW  - Machine Learning
KW  - Coping Strategies
UR  - https://iris-conferences.eu/en/international-legal-informatics-symposion-wed-14-sat-17-february-2024-salzburg-and-online-in-vienna
N2  - 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.
ER  -

PALAŠTA, Damián. Identifying coping strategies trends with the ML (machine learning) threats to intellectual property. In \textit{IRI§24 – International Legal Informatics Symposion}. 2024.

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