Masaryk University Journal of Law and Technology

Online Platforms and Legal Responsibility: A Contemporary Perspective in View of the Recent U.S. Developments

Gergely Ferenc Lendvai, Gergely Gosztonyi — Sat, 29 Jun 2024 00:00:00 +0200

This paper critically examines the relevance of Section 230 of the Communications Decency Act in the context of recent United States Supreme Court rulings, specifically Twitter v. Taamneh and Gonzalez v. Google. The Supreme Court ruled in 2023 that determining the extent of CDA230’s immunity lies with legislators, not the judiciary. This study explores the potential liability of algorithms in supporting terrorism and the implications for European regulations under the Digital Services Act. Findings indicate that while CDA230 has fostered internet growth, it also challenges content regulation. The United States approach contrasts with the European Union’s more explicit service provider responsibilities, suggesting a need for legislative updates to balance free expression with the control of harmful content.

Alternative Legal Bases for Processing Health Data for Scientific Research Purposes

Roxanne Meilak Borg, Mireille Martine Caruana — Sat, 29 Jun 2024 00:00:00 +0200

The processing of health data for scientific research purposes requires a legal basisunder Article 6 and a justification under Article 9 (2) GDPR by way of an exceptionto the general prohibition in Art. 9(1) of the processing of special category data.Consent tends to be highly advocated for in this regard, in both literature and practice.However, the GDPR permits an alternative option: processing for scientific researchpurposes based on Union or Member State law which provides for suitable andspecific safeguarding measures. This paper undertakes an in-depth examination ofthe ‘research exception’ in Art. 9 (2) (j) GDPR permitting the processing of healthdata for scientific research purposes, thoroughly considering its elements and itsimplications. It refers to examples of Member State implementing legislation andthe proposed European Health Data Space Regulation for illustration purposes andargues that if implemented faithfully, Art. 9 (2) (j) strikes a better balance betweenthe interests of the various stakeholders than consent, which is overall burdensomeand may hinder research. Finally, in light of the uneven implementation of theGDPR’s research exception by the Member States which creates considerable legaluncertainties and results in barriers to the free flow of research data across the EU,this paper calls for a harmonised implementing Union law in this regard.

Humour and Intellectual Property Law

Michal Ježek — Sat, 29 Jun 2024 00:00:00 +0200

This article discusses the issue of humour in the context of intellectual property law,with a focus on parody in trademark law. Parody is a form of humorous expressionthat is generally protected by freedom of expression. Although copyright law hasa statutory exception for caricature, parody, and pastiche, no such exception existsin trademark law. Therefore, parody must be treated differently in this area of law.The article first introduces the legal position of parody and discusses the assessmentof parody in both copyright and trademark law in the EU and Czech law. Then itexamines the peculiarities of the trademark law approach and with the help of Germanlandmark cases, highlights the possibilities for the treatment of trademark parody inthe Czech Republic. The outcomes may apply to closely related laws throughoutthe EU territory.

The “objective test” and the downstream market presence requirement in Big Data access cases under the essential facilities doctrine - a critical assessment

Rok Dacar — Sat, 29 Jun 2024 00:00:00 +0200

One possible way to gain access to competitively relevant sets of Big Data is toapply the essential facilities doctrine. However, the European Commission and theEuropean Court of Justice have established several different criteria for applying thedoctrine. Since neither institution has yet applied the doctrine in Big Data accesscases, it is not clear which of the criteria applies in such positions. This paper attemptsto analyze the impact of the “objective test” and the requirement that the controllingcompany be active in the downstream market (which are included in all assessmentcriteria) in Big Data access cases, with the goal of answering the research question,“Do the application of the “objective test” and the requirement that the controllingcompany be active in the downstream market impede the effectiveness of the doctrinein Big Data access cases under EU competition law, and if so, how should they bechanged?” The conclusion is that in Big Data access cases, the “objective test” shouldbe mitigated and replaced by the “subjective test” or the “average company test” andthe requirement that the controlling company be active in the downstream marketshould be discarded altogether in order for the doctrine to be an effective tool foraccessing competitively relevant sets of Big Data.

Unveiling the Black Box: Bringing Algorithmic Transparency to AI

Gyandeep Chaudhary — Sat, 29 Jun 2024 00:00:00 +0200

Overall, algorithmic transparency is an important aspect of responsible AI development and deployment. Ensuring that AI systems are transparent and accountable will help build trust and confidence in these systems and ensure that they are used ethically and effectively. Artificial intelligence (AI) has emerged as a cutting-edge domain that is fundamentally redefining different areas of daily experiences, such as health care, transport, finance, education, and others. The systems are not created for making a judgment like human judgment of natural language, spotting patterns and problem-solving; rather AI produces machines that also have intelligence level same as that of human beings.

AI having more influence over us, it is to be considered the ethical directions of these tools and see that they operate under principles of transparency and accountability. The element regarding algorithmic transparency, which means the process of understanding the functioning and explanation of how AI systems make their decisions is the one that is most crucial. The issue of algorithm transparency is of fundamental importance for many considerations. AI systems are not only supported by fairness but also by their non-discrimination. If we do not know how a system of AI arrives at the decisions made, it becomes impossible to determine if the provided results meet equal treatment for everybody. If used in delicate areas like recruitment, credit, and legal system- where the AI-machine must make choices which are life changing, then this aspect is very important.

On top of fairness, algorithmic transparency is also an important factor for accountability. If we are ignorant about what an artificial intelligence algorithm does and what is the source of its decision-making process, we are unable to track and classify the mistakes or mishaps of the system. This has always mattered when central to the operation of systems with high stake, such as those used in self-driving vehicles or in health care. Algorithmic transparency may be reached using different instruments. The transparent AI systems can be made by a more transparent design, for example, the simple modelling tools, that use interpretable models. Another method is designing technologies and techniques that can help people why the artificial systems difficult to be decoded but easy to understand which they can utilize in making decisions.

Therefore, algorithmic transparency is a key factor of the AI made responsibly and used by the society. It is crucial that AI machines are both transparent and accountable since this will lead to people building trust in the system and accepting its ethical and practical implications. This paper examines regulation of algorithmic transparency in the EU, specifically provisions under the General Data Protection Regulation (GDPR), it aims to situate analysis of the GDPR's provisions on explainability of AI systems within broader technology ethics and policy discourse. The paper's scope is limited to EU regulations applicable to AI data processing transparency.