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Blog over Kunstmatige Intelligentie, Quantum, Deep Learning, Blockchain en Big Data Law

Blog over juridische, sociale, ethische en policy aspecten van Kunstmatige Intelligentie, Quantum Computing, Sensing & Communication, Augmented Reality en Robotica, Big Data Wetgeving en Machine Learning Regelgeving. Kennisartikelen inzake de EU AI Act, de Data Governance Act, cloud computing, algoritmes, privacy, virtual reality, blockchain, robotlaw, smart contracts, informatierecht, ICT contracten, online platforms, apps en tools. Europese regels, auteursrecht, chipsrecht, databankrechten en juridische diensten AI recht.

Berichten met de tag Data Ownership
Machine Learning & EU Data Sharing Practices

Stanford - Vienna Transatlantic Technology Law Forum, Transatlantic Antitrust and IPR Developments, Stanford University, Issue No. 1/2020

New multidisciplinary research article: ‘Machine Learning & EU Data Sharing Practices’.

Download the article here: Kop_Machine Learning and EU Data Sharing Practices-Stanford University

In short, the article connects the dots between intellectual property (IP) on data, data ownership and data protection (GDPR and FFD), in an easy to understand manner. It also provides AI and Data policy and regulatory recommendations to the EU legislature.

As we all know, machine learning & data science can help accelerate many aspects of the development of drugs, antibody prophylaxis, serology tests and vaccines.

Supervised machine learning needs annotated training datasets

Data sharing is a prerequisite for a successful Transatlantic AI ecosystem. Hand-labelled, annotated training datasets (corpora) are a sine qua non for supervised machine learning. But what about intellectual property (IP) and data protection?

Data that represent IP subject matter are protected by IP rights. Unlicensed (or uncleared) use of machine learning input data potentially results in an avalanche of copyright (reproduction right) and database right (extraction right) infringements. The article offers three solutions that address the input (training) data copyright clearance problem and create breathing room for AI developers.

The article contends that introducing an absolute data property right or a (neighbouring) data producer right for augmented machine learning training corpora or other classes of data is not opportune.

Legal reform and data-driven economy

In an era of exponential innovation, it is urgent and opportune that both the TSD, the CDSM and the DD shall be reformed by the EU Commission with the data-driven economy in mind.

Freedom of expression and information, public domain, competition law

Implementing a sui generis system of protection for AI-generated Creations & Inventions is -in most industrial sectors- not necessary since machines do not need incentives to create or invent. Where incentives are needed, IP alternatives exist. Autonomously generated non-personal data should fall into the public domain. The article argues that strengthening and articulation of competition law is more opportune than extending IP rights.

Data protection and privacy

More and more datasets consist of both personal and non-personal machine generated data. Both the General Data Protection Regulation (GDPR) and the Regulation on the free flow of non-personal data (FFD) apply to these ‘mixed datasets’.

Besides the legal dimensions, the article describes the technical dimensions of data in machine learning and federated learning.

Modalities of future AI-regulation

Society should actively shape technology for good. The alternative is that other societies, with different social norms and democratic standards, impose their values on us through the design of their technology. With built-in public values, including Privacy by Design that safeguards data protection, data security and data access rights, the federated learning model is consistent with Human-Centered AI and the European Trustworthy AI paradigm.

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AI & Intellectual Property: Towards an Articulated Public Domain

New peer reviewed research article: ‘AI & Intellectual Property: Towards an Articulated Public Domain’ (download)

By Mauritz Kop

Link & citation at Texas Intellectual Property Law Journal (TIPLJ): 28 Tex. Intell. Prop. L. J. 297 (2020)

Link SSRN: https://ssrn.com/abstract=3409715

The article has been published in the Texas Intellectual Property Law Journal (2020, 28). TIPLJ is published in cooperation with the State Bar of Texas three times per year at the University of Texas School of Law. The Journal is the official journal of the State Bar of Texas Intellectual Property Law Section.

Res Publicae ex Machina (Public Property from the Machine)

Building upon the doctrinal body of knowledge, the article introduces a new public domain model for AI Creations and Inventions that crossed the autonomy threshold (i.e. no sufficient amount of human intervention that can be linked to the output): Res Publicae ex Machina (Public Property from the Machine). It includes examples.

Intellectual property framework AI systems

Besides that, the article describes the current legal framework regarding authorship and ownership of AI Creations, legal personhood, patents on AI Inventions, types of IP rights on the various components of the AI system itself (including Digital Twin technology), clearance of training data and data ownership.

Compact Artificial Intelligence & IP overview analysis

Main goal of this research is to offer an accessible, relatively compact Artificial Intelligence (AI) & IP overview analysis and in doing so, to provide some food for thought to interdisciplinary thinkers and policy makers in the IP, tech, privacy and freedom of information field.

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