Innovation, Quantum-AI Technology & Law

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 Privacy
Music Law and Artificial Intelligence: From Cloned Artists to AI-Generated Works

The rise of artificial intelligence (AI) in the music industry is sparking a revolution, profoundly changing how music is created. This development raises complex legal questions concerning AI and copyright, including related rights. How can we protect the creative rights of artists and composers while simultaneously allowing room for technological innovation? In this comprehensive yet accessible legal overview, we explore key issues regarding AI and music. These include whether AI can legally train on copyrighted materials without consent, TDM exceptions, how various rights organizations (such as Buma/Stemra and Sena) approach AI, the status of AI-generated musical works, the threshold of human creativity required, protection against AI voice cloning via privacy laws and moral rights, contractual implications, new obligations under the EU AI Act, differences between European and American law, and ongoing lawsuits. This article is tailored for artists, composers, music publishers, labels, voice actors, producers, and AI companies seeking clarity on their legal standing.

AI Training on Protected Music and Video Materials: Legal Framework and Debate

Can an AI model in the Netherlands and the EU train on copyrighted material (such as music or video) without permission from the rights holders? Generally, using protected material beyond private use or citation requires permission. Scraping or using data for AI training without permission is typically considered infringement unless a specific legal exception applies.

Buma/Stemra’s Opt-Out Policy

In the Netherlands, Buma/Stemra explicitly uses its opt-out rights, requiring prior consent for TDM on its repertoire, thus ensuring fair compensation for composers and lyricists.

EU AI Act: Transparency Obligations and System Monitoring

The EU AI Act, effective from August 2025, introduces important transparency requirements, obliging generative AI model developers to:

• Disclose training data used, including copyrighted music or texts.

• Maintain policies ensuring compliance with EU copyright law.

• Respect explicit opt-out signals from rights holders during training.

The Act doesn't prohibit using protected material for training outright but enforces transparency and compliance through oversight and penalties.

Composition, Lyrics, and Master Recordings: Different Rights Regimes

Music rights in the Netherlands broadly split into:

Copyright: Protects compositions and lyrics, managed by organizations like Buma/Stemra.

Neighboring Rights: Protect recordings and performances, managed by Sena.

AI-Generated Compositions and Lyrics

Completely AI-generated works often fail to meet traditional copyright criteria, as human creativity is essential.

Neighboring Rights

It remains uncertain whether AI-generated performances and recordings attract neighboring rights, as these typically rely on human involvement.

Copyright Status of AI-Generated Music

In the U.S., fully AI-generated works explicitly do not receive copyright protection. While Europe hasn't clarified explicitly, the prevailing legal view aligns with this stance—AI-generated works likely fall into the public domain unless there's significant human creativity involved.

Hybrid Creations

Music combining human and AI input may qualify for copyright protection depending on the human creative contribution's significance.

AI Voice Cloning: Personality Rights and Privacy

AI voice cloning technology poses challenges regarding personal rights and privacy. Artists may invoke:

• Privacy rights under EU law (Article 8 ECHR).

• Personality rights.

• Potential trademark and image rights analogously.

The EU AI Act mandates transparency in AI-generated content, aiming to mitigate unauthorized use and deepfake concerns.

Music Contracts in the AI Era

Existing music contracts require updates addressing AI-specific matters:

• Explicit licensing terms for AI training.

• Ownership clarity of AI-generated content.

• Liability assignment for copyright infringements involving AI.

Conclusion: Balancing Innovation and Rights—Be Prepared

The intersection of AI and music law presents both opportunities and challenges. Stakeholders should proactively:

• Clearly define rights in AI-generated music contractually and update existing music contracts.

• Specify permissions (licenses) and restrictions (opt-out) regarding AI training explicitly.

• Seek specialized music & AI legal advice to navigate evolving regulations.

By strategically addressing these issues, artists, companies, and AI developers can legally and effectively harness AI innovations, maintaining both creative and commercial control.

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Workshop Juridische Aspecten AI & Data bij TNO - NL AIC Startups & Scaleups TekDelta Event

Op 24 september 2020 gaf Stanford Law School Fellow Mauritz Kop een masterclass over de juridische dimensie van kunstmatige intelligentie en informatie aan de getalenteerde deelnemers van de Werkgroep Startups & Scaleups van de Nederlandse AI Coalitie (NL AIC), in het kantoor van TNO Research in Den Haag. De workshop maakte onderdeel uit van het TekDelta | NL AIC startup accelerator event, met als centraal thema het versnellen en faciliteren van innovatie door het verbinden van startende ondernemingen met bestaande leading organisaties met slagkracht: het samen bouwen aan een succesvol high tech ecosysteem in Nederland.

Masterclass 'Juridische Aspecten van AI & Data’

De 2,5 uur durende masterclass 'Juridische Aspecten van AI & Data' bij TNO verschafte de cursisten duidelijkheid over de regels voor data delen, privacy en gegevensbescherming, alsmede juridisch en economisch eigendom van informatie. We behandelden onderwerpen variërend van de bescherming van intellectueel eigendom op het AI-systeem, de software, hardware en apps, clearance van data tot het anticiperen op de aanstaande AI & Data Governance wetten van de Europese Commissie.

Multidisciplinair Panel voor Verantwoord Data Delen

Dezelfde middag vond er vanuit het TNO gebouw een online seminar plaats speciaal voor startups, onder leiding van Anita Lieverdink, Senior Orchestrator of Innovation at TNO, Directeur van TekDelta en Program Manager van de Werkgroep Startps & Scaleups van de Nederlandse AI Coalitie.

AIRecht managing partner Mr. Kop nam als juridisch expert deel in het panel dat ging over verantwoord data delen. Het was goed om deel te nemen aan dit multidisciplinaire panel en samen met onze collega's oplossingen te verkennen voor het versneld en verantwoord delen van gegevens. Het is cruciaal en urgent om belemmeringen voor de inzet van benevolente AI weg te nemen en organisaties begeleiding te bieden die rechtszekerheid en vertrouwen in de snelle introductie van deze veelbelovende transformatieve technologie aanmoedigt!

Juridische Cursussen van AIRecht

Onze cursussen ‘AI en Recht – Juridische aspecten van AI, Machine Learning en Data’ bieden een compleet overzicht van de juridische facetten van kunstmatige intelligentie, big (structured/labelled en unstructured, raw) data en de verschillende typen machine learning (supervised, unsupervised, deep reinforcement, transfer, federated). De invalshoek is breed: van beschermen idee tot en met marktintroductie van het product. Cursusdoel is het wegnemen van juridische obstakels voor innovatie. Onderwerpen die hierbij aan de orde komen zijn privacywetgeving, het maximaliseren van uw IP-portfolio (intellectueel eigendom), normering, standaardisering (interoperabiliteit) en certificering (CE mark, keurmerken, conformiteit), het stimuleren van internationaal zakendoen, en het realiseren van (training)data delen op basis van EU regelgeving, licenties, toestemmingen en rechtsgeldige contracten. Maatwerk is mogelijk.

De workshops en masterclasses zijn cross-disciplinair en verbinden de ontwikkeling en toepassing van technologie met geldend nationaal en EU recht.

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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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