Image by Seanbatty from Pixabay
Journal of Science Policy & Governance | Volume 16, Issue 01 | April 13, 2020

Policy Analysis: Artificial Intelligence and the Copyright Survey

Kalin Hristov 
University of Science and Technology of China, School of Public Affairs, Hefei, China
Download PDF
Keywords: artificial intelligence; AI; copyright; technology policy; survey; United States of America​
Abstract: Artificial intelligence has emerged as a key contributor to American social, economic, and cultural development. Intelligent software increasingly plays a greater role in every creative industry. These industries rely on intellectual property protections to maintain equilibrium between productivity, remuneration, and competitiveness. American policymakers, however, have paid little attention to the intersection of artificial intelligence and copyright protection. This study collects data from fifty-seven AI scientists, tech policy experts, and copyright scholars through a survey and questionnaire. The data shows that while intelligent software is an important contributor to American cultural development, half of respondents believe that the US Copyright Office is not prepared to deal with an influx of computer-generated works. In light of rapid developments in artificial intelligence, this could present a serious challenge to the American copyright system and future advancements in the AI industry.

I. Introduction

Artificial intelligence (AI) is a loosely defined term which has been around for decades. AI encompasses the idea that computer programs can perform functions typically associated with the human mind. Although most people are only familiar with AI as a collective term, artificial intelligence can be divided into a number of unique sub-fields. Machine learning, natural language processing, robotics, and computer vision are just a few of the subfields of AI. Machine learning, and more specifically, its offshoot, deep learning, is present in countless academic papers and news headlines as a result of achievements which seemed unfathomable just a decade ago. With the help of Artificial Neural Networks (ANNs)—which loosely resemble the structure and functionality of biological neural networks constituting animal brains—AI has been able to outperform humans on an average IQ test; create works of art indistinguishable from those created by humans; and beat professional human players in highly complex games (Silver et al. 2016; Spice 2017; Wang et al. 2015).

Artificial intelligence has also become the status quo in the day-to-day operations of most tech companies. Alphabet’s Google search uses powerful algorithms to serve up results and advertising that are both relevant and engaging. Amazon’s recommendation engine relies on machine-learning techniques that contribute to higher company profits and greater customer satisfaction. Both Microsoft and Apple offer personal-assistant services which contribute to simplicity and efficiency of everyday tasks. These are some of the best-known applications of artificial intelligence. Companies and researchers alike are also exploring a new and fast-growing segment of the AI industry - generative AI. While still in its early stages of development, generative AI has the potential to disrupt the day to day operations of the creative and entertainment industries. The following section briefly introduces generative AI and outlines the intersection of Copyright and Artificial Intelligence.

i. Copyright and AI-produced works

Generative algorithms are responsible for producing unique works of varying complexity which differ from prior art. These works can be as a result of collaborative efforts between a human creator and an AI program, or entirely the result of independent AI processes (Hristov 2017; Thaler 2013). In both cases, artificial intelligence is at least partially responsible for the production of innovative work. Instances of such works are becoming more common as AI use becomes more frequent and algorithms improve. To date, books, songs, visual art and computer programs have all been created by generative algorithms (IBM 2017). Advancements in the tech sector along with the development of new generative AI methods will likely contribute to a greater number and quality of AI-produced works, making intellectual property (IP) rights a pressing issue for artificial intelligence programmers and users. Programming and training an AI algorithm can be both time consuming and expensive. If AI programmers are unable to recoup their efforts through the financial benefits associated with IP protection, many may be dissuaded from investing their time, money, and expertise in AI development.

The United States lacks legislation and targeted policy that addresses the attribution of copyrights for AI-produced works. This would not be of concern if the US tech sector existed in a vacuum—not influenced or affected by outside forces or by the rapid development of novel AI technologies. In reality, most global actors, with even the slightest AI-research capacities, are actively jockeying for position as leaders of the international AI race. Japan and the European Union have dedicated resources and increased efforts in determining best practices when dealing with the attribution of copyrights for AI-produced works (Delvaux 2016). China is investing billions of dollars into its AI industry in hopes of reinvigorating its slowing economy and overtaking the US as leader in AI research and development (State Council 2017). As AI increasingly permeates every aspect of our lives, the stakes are quite high for US businesses and consumers. The right laws and policy could determine the global socio-economic outlook for decades to come.

Governments around the world have indicated their intent to adopt and invest in artificial intelligence as a way to improve their citizens’ welfare and contribute to economic growth. Recent initiatives in Japan, the EU and China have attracted media attention and signaled governmental readiness to turn a new chapter in the technological forefront by openly and effectively adopting AI. Setting up policy research-taskforces and investing billions of dollars in the future development of the AI industry are tell-tale signs that the international AI race is well under way. 

-Read the full article through download.-
Download PDF

References

  1. Auerbach, D. 2015. “Do androids dream of electric bananas? Google’s DeepDream is dazzling, druggy, and creepy. It’s also the future of A.I.” Slate, July 23, 2015.  https://slate.com/technology/2015/07/google-deepdream-its-dazzling-creepy-and-tells-us-a-lot-about-the-future-of-a-i.html 
  2. Bridy, A. 2012. “Coding creativity: copyright and the artificially intelligent author.” Stanford Technology Law Review, 1.
  3. Brueck, H. 2016. “Google’s computers are making thousands as artists.” Fortune, March 1, 2016.  http://fortune.com/2016/03/01/google-deepdream-art/ 
  4. Cohn, G. 2018. "AI art at Christie’s sells for $432,500.” New York Times, October 25, 2018.  https://www.nytimes.com/2018/10/25/arts/design/ai-art-sold-christies.html 
  5. Copyright, Designs and Patents Act. 1988. Parliament of the United Kingdom.
  6. Creswell, A., T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath. 2018. Generative adversarial networks: an overview. IEEE Signal Processing Magazine 35(1): 53-65.
  7. Davies, C. R. 2011. “An evolutionary step in intellectual property rights: Artificial intelligence and intellectual property.” Computer Law & Security Review 27(6): 601-619.
  8. Delvaux, M. 2016. “Draft report with recommendations to the Commission on Civil Law Rules on Robotics.” Committee on Legal Affairs of the EU, May 31, 2016.
  9. Department of Commerce. 2017. Top markets report—media and entertainment. US Department of Commerce. International Trade Administration.
  10. Elgammal, A., B. Liu, M. Elhoseiny, and M. Mazzone. 2017. “CAN: creative adversarial networks, generating ‘art’ by learning about styles and deviating from style norms.” arXiv:1706.07068.
  11. Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair et al. 2014. “Generative adversarial nets.” Advances in Neural Information Processing Systems 27: 2672-2680.
  12. Grimmelmann, J. 2015. “There's no such thing as a computer-authored work—and it's a good thing, too.” Columbia Journal of Law and Arts 39, 403.
  13. Hristov, K. 2017. “Artificial intelligence and the copyright dilemma.” IDEA 57, 431.
  14. IBM 2017. The quest for AI creativity. IBM Report. : https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-creativity.html 
  15. Kaleagasi, B. 2017. “A new AI can write music as well as a human composer.” Futurism.com. March 9, 2017.  https://futurism.com/a-new-ai-can-write-music-as-well-as-a-human-composer/ 
  16. Kaufman, A. B., A. E. Butt, J. C. Kaufman, and E. N. Colbert-White. 2011. “Towards a neurobiology of creativity in nonhuman animals.” Journal of Comparative Psychology 125(3): 255.
  17. Microsoft (n.d.) The next Rembrandt: recreating the work of a master with AI. Microsoft News Feature.  https://news.microsoft.com/europe/features/next-rembrandt/ 
  18. Olewitz, C. 2016. A Japanese AI program just wrote a short novel, and it almost won a literary prize. Digital Trends.  https://www.digitaltrends.com/cool-tech/japanese-ai-writes-novel-passes-first-round-nationanl-literary-prize/ 
  19. Silver, D., A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587): 484.
  20. Simonite, T. 2017. “AI software learns to make AI software.” MIT Technology Review, January 18, 2017.  https://www.technologyreview.com/s/603381/ai-software-learns-to-make-ai-software/ 
  21. Siwek, S. E. 2018. Copyright industries in the U.S. economy: The 2018 report, prepared for the International Intellectual Property Alliance. International Intellectual Property Alliance.
  22. Solum, L. B. 1991. “Legal personhood for artificial intelligences.” North Carolina Law Review 70, 1231.
  23. Spice, B. 2017. “Carnegie Mellon Artificial Intelligence Beats Top Poker Pros.” Carnegie Mellon University News, January 31, 2017.  https://www.cmu.edu/news/stories/archives/2017/january/AI-beats-poker-pros.html 
  24. State Council of the P.R.C. 2017. “China issues guidelines on artificial intelligence development.” English.gov.cn, July 20, 2017.  http://english.gov.cn/policies/latest_releases/2017/07/20/content_281475742458322.htm 
  25. Tarantola, A. 2016. “AI-written novel passes first round of a literary competition.” Engadget, March 24. 2016.  https://www.engadget.com/2016/03/24/ai-written-novel-passes-first-round-of-a-literary-competition/ 
  26. Thaler, S. 2013. “Creativity machine paradigm.” In Encyclopedia of Creativity, Invention, Innovation, and Entrepreneurship, edited by Elias G. Carayannis, 451. Springer-Verlag New York.
  27. Thaler, S. 1996. “Neural networks that autonomously create and discover.” Imagination Engines, Inc.  http://imagination-engines.com/iei_pcai.php
  28. US Library of Congress. 2017a. Compendium of US Copyright Office Practices, § 306 US Copyright Office (3rd edition, 2017).
  29. US Library of Congress. 2017b. Compendium of US. Copyright Office Practices, § 313.2. US. Copyright Office (3rd edition, 2017).
  30. Villasenor, J. 2014. “How much copyright protection should source code get? A new court ruling reshapes the landscape.” Forbes, May 19. 2014. https://www.forbes.com/sites/johnvillasenor/2014/05/19/how-much-copyright-protection-should-source-code-get-a-new-court-ruling-reshapes-the-landscape/
  31. Wang, H., F. Tian, B. Gao, J. Bian, and T. Y. Liu. 2015. “Solving verbal comprehension questions in IQ test by knowledge-powered word embedding.” arXiv:1505.07909.
  32. Yanisky-Ravid, S. and S. Moorhead. 2017 “Generating Rembrandt: artificial intelligence, accountability and copyright - the human-like workers are already here - a new model.” Michigan State Law Review.  https://ssrn.com/abstract=2957722
  33. Zimmermann, K. A. 2012. “Pareidolia: seeing faces in unusual places.” Live Science, December 11, 2012.  https://www.livescience.com/25448-pareidolia.html

Kalin Hristov is a recent graduate of the University of Science and Technology of China (USTC) and recipient of the US-China Cooperation Scholarship, presented by the Chinese Scholarship Council (2015-18). While at USTC his research focused on the way artificial intelligence affects the security and economic dynamic between the United States and China. Kalin's interests include IP rights, machine learning, and Chinese culture.
DISCLAIMER: The findings and conclusions published herein are solely attributed to the author and not necessarily endorsed or adopted by the Journal of Science Policy and Governance. Articles are distributed in compliance with copyright and trademark agreements.

ISSN 2372-2193
© 2022 Journal of Science Policy & Governance, Inc. All rights reserved. The opinions, findings and conclusions from JSPG publications, additional article commentaries and related events do not necessarily reflect the views of the journal.