Artificial Intelligence as a Common Heritage of Mankind
DOI:
https://doi.org/10.32890/uumjls2023.14.1.5Keywords:
Artificial intelligence, Machine learning, Deep learning, Intellectual property, Common heritage of mankindAbstract
Artificial intelligence technologies today employ techniques known as machine learning and deep learning, which apply datasets to a suitable mathematical or statistical technique known as an algorithm. This in turn produces a model that can be employed to predict an outcome, given a new set of data that was previously unseen by the model. The principle of common heritage of mankind, which has originated in the 19th century, promotes the concept that humanity as a whole has rights and responsibilities over territories or outer space. This study aims to advance the concept of treating the components of artificial intelligence as an intellectual common in the form of a common heritage of mankind, in order to promote the discovery and the development of more novel artificial intelligence applications for the benefit of people around the world. This work employs a mix of legal doctrinal research related to intellectual property law and conceptual theoretical discussion. The potential application of open access and open data licensing is discussed. The history of the common heritage of mankind is covered, and the potential benefit of recognizing basic artificial intelligence components as a common heritage of mankind is explored. Finally, a novel method for implementing this idea is proposed. This work is significant in advancing a method to liberate certain artificial intelligence technologies from intellectual property rights protection, in order to promote greater experimentation and the development of artificial intelligence applications for the greater good of humanity.
References
Ang, S. (1994). The idea-expression dichotomy and merger doctrine in the copyright laws of the US and the UK. International Journal of Law and Information Technology, 2(2), 111–111.
Arnold, R. P. (1975). The common heritage of mankind as a legal concept. The International Lawyer, 9(1), 153–158.
Arrow, K. J. (1962). Economic welfare and allocation of resources for inventions. In C. on E. G. of the S. S. R. C. Universities-National Bureau Committee for Economic Research (Ed.), The rate and direction of inventive activity: Economic and social factors (pp. 609–626). Princeton University Press. http://www. nber.org/books/univ62-1
Azmi, I. M. A. G. (2020). Challenges for legal education in the era of IR 4.0. UUM Journal of Legal Studies, 11(2), 27–51.
Barkenbus, J. N. (1979). Deep seabed resources: Politics and technology. Free Press.
Benchoufi, M., & de Fresnoye, O. (2020). Open science and open data: Accelerating scientific research. In B. Nordlinger, C. Villani, & D. Rus (Eds.), Healthcare and Artificial Intelligence (pp. 215–221). Springer International Publishing.
Czarnitzki, D., Hussinger, K., & Leten, B. (2020). How valuable are patent blocking strategies? Review of Industrial Organization, 56(3), 409–434.
de Bossey, C. (2005). Report of the working group on internet governance. http://www.wgig.org/docs/WGIGREPORT.pdf
Drake, W. J. (Ed.). (2016). The working group on Internet governance: 10th anniversary reflections. Association for Progressive Communications.
Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., Jain, U., Pervaiz, H., Sehgal, B., Kaila, S. S., Misra, S., Aslanpour, M. S., Mehta, H., Stankovski, V., & Garraghan, P. (2019). Transformative effects of IoT, Blockchain and artificial intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, 100118.
Gordon, W. J. (2002). Excuse and justification in the law of fair use: Transaction costs have always been part of the story. Journal of the Copyright Society of the USA, 50, 149–198.
Guntrip, E. (2003). The common heritage of mankind: An adequate regime for managing the deep seabed? Melbourne Journal of International Law, 4(2), 376–405.
Hardin, G. (1968). The tragedy of the commons. Science, 162(3859), 1243–1248.
Heger, D., & Zaby, A. K. (2017). Patent breadth as effective barrier to market entry. Economics of Innovation and New Technology, 27(2), 174–188.
Hitaj, D., & Mancini, L. V. (2018). Have you stolen my model? Evasion attacks against deep neural network watermarking techniques. https://arxiv.org/abs/1809.00615
Hughes, J. (1988). The philosophy of intellectual property. Georgetown Law Journal, 77(2), 287–366.
Internet Society. (2017). Artificial intelligence and machine learning: Policy paper. https://www.internetsociety.org/resources/ doc/2017/artificial-intelligence-and-machine-learning-policy-paper/
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R (2nd ed.). Springer.
Johnson, P. (2008). Dedicating copyright to the public domain. Modern Law Review, 71(4), 587–610.
Khong, D. W. K. (2019). Intellectual property: Economic justification. In A. Marciano & G. B. Ramello (Eds.), Encyclopedia of Law and Economics (pp. 1178–1185). Springer New York.
Klein, A. (2017, July 11). The cost of hard drives over time. Backblaze Blog. https://www.backblaze.com/blog/hard-drive-cost-per-gigabyte/
Land, M. K. (2018). The Marrakesh Treaty as bottom up lawmaking: Supporting local human rights action on IP policies. UC Irvine Law Review, 8(3), 513–553.
Lehr, D., & Ohm, P. (2017). Playing with the data: What legal scholars should learn about machine learning. UC Davis Law Review, 51(2), 653–718.
MacLeod, C. (1988). Inventing the Industrial Revolution: The English patent system, 1660–1800. Cambridge University Press. https://doi.org/10.1017/CBO9780511522673
Manap, N. A. (2012). Intellectual creation in database: A superfluous test? Current Law Journal (Legal Network Series), 1, lxxi–lxxi.
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. http://raysolomonoff.com/dartmouth/ boxa/dart564props.pdf
Mirzaee, S. (2017). The conceptual foundations of the common heritage of mankind. Евразийский Юридический Журнал, 10(113), 50–54.
Moor, J. (2006). The Dartmouth College artificial intelligence conference: The next fifty years. AI Magazine, 27(4), 87–91.
Moore, A. D. (1997). A Lockean theory of intellectual property. Hamline Law Review, 21(1), 65–108.
Owolabi, K. M. W. (2013). The principle of the common heritage of mankind. Nnamdi Azikiwe University Journal of International Law and Jurisprudence, 4(3), 51–56.
Paton, C., & Kobayashi, S. (2019). An open science approach to artificial intelligence in healthcare: A contribution from the International Medical Informatics Association Open Source Working Group. Yearbook of Medical Informatics, 28(1), 47–51.
Salameh, A. (2017). Artificial intelligence as a commons: Opportunities and challenges for society. Hochschule Hannover.
Samuelson, P. A. (1954). The pure theory of public expenditure. Review of Economics and Statistics, 36(4), 387–389.
Schelter, S., Biessmann, F., Januschowski, T., Salinas, D., Seufert, S., & Szarvas, G. (2018). On challenges in machine learning model management. Bulletin of the Technical Committee on Data Engineering, 41(4), 5–15.
Schwab, K. (2016). The fourth industrial revolution. World Economic Forum.
Segura-Serrano, A. (2006). Internet regulation and the role of international law. Max Planck Yearbook of United Nations Law Online, 10(1), 191–272.
Sullivan, R. J. (1989). England’s “Age of invention”: The acceleration of patents and patentable invention during the industrial revolution. Explorations in Economic History, 26(4), 424–452. https://doi.org/10.1016/0014-4983(89)90017-X
Surden, H. (2014). Machine learning and law. Washington Law Review, 89, 87–115.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
Tramer, F., Zhang, F., Juels, A., Reiter, M. K., & Ristenpart, T. (2016). Stealing machine learning models via prediction APIs. Proceedings of the 25th Usenix Security Symposium, 601–618.
Tzimas, T. (2018). Artificial intelligence as global commons and the ‘international law supremacy’ principle, 83–88.
Vleugels, C. (2020). The Marrakesh Treaty. Journal of Disability Policy Studies. https://doi.org/10.1177/1044207320937419
Wiley, M., & Wiley, J. F. (2019). Advanced R statistical programming and data models: Analysis, machine learning, and visualization. Apress.
Published
Issue
Section
How to Cite
Research impact
Harvested 2026-09-06Counts differ between services because each indexes a different body of literature. None of them is the whole picture.
