SavedAdd your journal

Journals / Art-e Sanat Dergisi / 2020 / Cilt: 13 - Sayı: 26

Intellectual Property of Artificial Intelligence Generated Visual Art Works

Yapay Zekâ ile Üretilen Görsel Sanatlar Eserlerinde Fikrî Mülkiyet

Güllü YAKAR
Mustafa KINIK
Pages
490–516
Publication Date
2020-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is predicted that the electronic personality status proposed by the European Parliament in 2017 will come into effect in the near future and provide reasonable solutions related to the copyright of AI.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial intelligence (AI) is capable of autonomous production in areas which require creativity such as art and design.
📊
Key Findings & Data:However, the products cannot be registered to AI as its autonomy is not legally recognized.
💡
Conclusion & Impact:It is predicted that the electronic personality status proposed by the European Parliament in 2017 will come into effect in the near future and provide reasonable solutions related to the copyright of AI.
🔬Design:Review & Systematic Survey
📍Scope:2017 Verileri
⚙️Variables:Artificial Intelligence · Visual Arts · Intellectual Property

Abstract

Artificial intelligence (AI) is capable of autonomous production in areas which require creativity such as art and design. However, the products cannot be registered to AI as its autonomy is not legally recognized. This study aims to determine the intellectual property sharing methods of visual art works produced by AI. In this study of compilation, document analysis as one of the qualitative research data collection techniques is used. Approaches of different jurisdictions to the issue, legal and philosophical doctrines, and practical regulations are examined. Researches explaining the copyright conditions in contemporary legal regimes are used. As a result of the literature review, the assessments that the laws do not yet have the content to evaluate technological developments are observed. The personality statuses that can be assigned to artificial intelligence in the future and recommendations regarding the copyright of the works to be produced with these statuses are presented. It is predicted that the electronic personality status proposed by the European Parliament in 2017 will come into effect in the near future and provide reasonable solutions related to the copyright of AI.

Özet

Yapay zekâ (YZ); sanat ve tasarım gibi yaratıcılık gerektiren alanlarda özerk üretim yapabilmektedir. Ancak özerkliği hukuken tanınmadığı için, üretimler YZ’ya tescil edilememektedir. Çalışma; YZ ile üretilen görsel sanat eserlerinin, fikrî mülkiyet paylaşım usullerini belirlemek amacıyla hazırlanmıştır. Derleme türündeki çalışmada; nitel araştırma veri toplama tekniklerinden doküman incelemesi kullanılmıştır. Farklı yargı bölgelerinin konuya yaklaşımları, hukuki ve felsefi doktrinler, uygulamaya dönük düzenlemeler incelenmiştir. Mevcut yasal rejimlerde telif koşullarını açıklayan araştırmalardan faydalanılmıştır. Literatür incelemesi neticesinde; yasaların, teknolojik gelişmeleri değerlendirebilecek içeriğe sahip olmadığı yönünde görüşler tespit edilmiştir. Gelecekte YZ’ya atanabilecek kişilik statüleri ve bu statülerle üretilecek eserlerin mülkiyetine ilişkin tavsiyeler sunulmuştur. Avrupa Parlamentosu’nun 2017 yılında önerdiği elektronik kişilik statüsünün, yakın gelecekte yürürlüğe gireceği ve YZ’nın telif hakları için makul çözümler sağlayacağı öngörülmektedir.

🔖

Cite This Article

YAKAR, G., KINIK, M. (2020). Intellectual Property of Artificial Intelligence Generated Visual Art Works. Art-e Sanat Dergisi, Cilt: 13 - Sayı: 26, 490–516. https://ajindex.com/articles/70442

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  1. Abbott, R. (2016). “I Think, Therefore I Invent: Creative Computers and the Future of Patent Law”, Boston College Law Review, 57(4), p.1079-1126.
  2. Akdemir Kamalı, T. (2019). Fikrî Hukuk Kapsamında Bilgisayar Programlarındaki Değişikliklerin Sonuçları, Ankara:
  3. Seçkin Yayınları.
  4. Alpaydın, E. (2016). Machine Learning: The New AI, Massachusetts: MIT Press.
  5. Boden, M. A. (2007). “How Creativity Works”, The Creativity: Innovation and Industry Conference, 6-9
  6. Aralık, Creativity East Midlands. Bozkurt Yüksel, A. E. (2018). “Yapay Zekânın Buluşlarının Patentlenmesi”, Uyuşmazlık Mahkemesi Dergisi, Sayı 11, s.585-622.
  7. Bozkurt Yüksel, A. E. ve Bak, B. (2018). “Yapay Zekâ”, Futurist Hukuk, İstanbul: Aristo Yayınevi, s.5-35.
  8. Bridy, A. (2012). “Coding Creativity: Copyright and the Artificially Intelligent Author”, Stanford Technology Law Review, No 5, p.1-28.
  9. Bridy, A. (2016). “The Evolution of Authorship: Work Made by Code”, Columbia Journal of Law & ARTS, No 39, p.395-401.
  10. Brown, N. I. (2018). “Artificial Authors: Case for Copyright in Computer-Generated Works”, Columbia Science and Technology Law Review, No 20 (1), p.1-41.
  11. Chiou, T. (2019). “Copyright Lessons on Machine Learning: What Impact on Algorithmic Art”, Journal of Intellectual Property, Information Technology and Electronic Commerce Law, 10(3), p.398-412.
  12. Clifford, R. D. (2018). “Creativity Revisited”, IDEA, 59(1), p.25-39.
  13. Davies, C. R. (2011). “An Evolutionary Step in İntellectual Property Rights - Artificial İntelligence and Intellectual Property”, Computer Law & Security Review, No 27, p.601-619.
  14. Dee, C. A. (2018). “Examining Copyright Protection of AI-Generated Art”, Delphi Interdisciplinary Review of Emerging Technologies, no 1, p.31-37.
  15. Deltorn, J. M. and Macrez, F. (2018). “Authorship in the Age of Machine Learning and Artificial Intelligence”, Center for International Intellectual Property Studies Research Paper, No 10, p.1- 24. Denicola, R. C. (2016). “Ex Machina: Copyrıght Protection for Computer Generated Works”. Rutgers University Law Review, no 69, s.251-287.
  16. Dennis, C. A. (2020). “AI-Generated Fashion Designs: Who or What Owns the Goods?” Fordham Intellectual Property, Media & Entertainment Law Journal, 30(2), p.593-644.
  17. Elgammal, A., Liu, B., Elhoseiny & M., Mazzone, M. (2017). “CAN: Creative adversarial networks generating art by learning about styles and deviating from style norms”, Eighth International Conference on Computational Creativity, Georgia: Georgia Institute of Technology, p.96-103.