Short trainings on what your teams need to know to keep compliant as the world moves fast. Prepared by real, reliable human scholars, presented by Lexstream’s super friendly avatars.
This stream examines whether the EU’s Artificial Intelligence Act will create a new “Brussels Effect.” It argues that, due to external competition and internal design limits, the Act is unlikely to shape global standards broadly, leading instead to a patchwork of partial influence across jurisdictions.
This course offers a problem-oriented analysis of AI law, based on an invited contribution to the Elgar Encyclopedia of Comparative Law. Rather than providing an exhaustive overview of existing legal texts, the course focuses on the types of legal challenges AI regulation seeks to address. Participants will explore how specific legislative proposals define legal conditions and effects, with in-depth discussions of key provisions where necessary. Emphasising a critical and conceptual approach, the course equips students to understand the evolving landscape of AI law beyond static legal frameworks.
As AI supply chains become increasingly complex, ensuring compliance with the EU AI Act (AIA) poses significant challenges. This course introduces an automated system designed to streamline provider-side AIA compliance analyses. Participants will learn about transparency artefacts that capture essential metadata and the algorithms that use this information to deliver real-time compliance predictions, making AIA compliance faster, more accessible, and more efficient for AI developers.
This course explores the legal and regulatory challenges of Generative AI in the European Union, focusing on liability, privacy, intellectual property, and cybersecurity. It analyzes the adequacy of current and proposed EU legislation, including the AI Act, identifies gaps, and offers recommendations for the safe and compliant use of generative models.
This course examines the extraterritorial application of data privacy laws, focusing on the EU’s GDPR and Article 3. It discusses the balance between protecting data subjects’ rights and respecting actors outside the EU, the challenges of extraterritorial enforcement, and suggests tools like blacklisting to enhance effective enforcement of EU laws in the digital space.
As the EU develops its AI liability framework, key gaps remain in addressing harms caused by advanced AI systems. This course critiques the fragmented structure of the current proposals and advocates for a single harmonized regime, combining strict liability for certain high-risk AI systems with clearer rules on fault, while incorporating non-discrimination, explainability, and sustainability into AI regulation.
This course examines human oversight as a key tool in AI governance, highlighting empirical evidence that humans often fail in oversight roles due to limited competence or harmful incentives. It analyzes the EU Artificial Intelligence Act, proposes a taxonomy of oversight roles, and develops six normative principles — drawn from democratic theory — to institutionalize distrust and strengthen the effectiveness and trustworthiness of AI oversight.
This course analyzes the EU Artificial Intelligence Act (AI Act), agreed in February 2024, which establishes a risk-based framework to ensure ethical, safe, and competitive AI in the EU. It examines the Act’s emerging governance system — centered on a European AI Office, new EU bodies, and national authorities — and offers a critical assessment of its collaborative governance model and its strengths and weaknesses.
This course critiques the AI Act’s product compliance rules for failing to protect human rights. It shows that conformity assessments, designed for physical products, cannot capture rights like non-discrimination, freedom of speech, or principles such as the rule of law, which emerge case by case. Applying engineering-style logic to these nuanced rights, it argues, creates confusion and practical challenges.
This course situates large generative AI within the debate on trustworthy AI regulation and examines how legal frameworks can be adapted to their specific capabilities. After outlining the technical foundations, it analyzes four areas: direct regulation, data protection, content moderation, and policy proposals. It introduces a new terminology for the AI value chain, distinguishing between developers, deployers, professional and non-professional users, and recipients of AI-generated content, and assigns regulatory responsibilities accordingly. It proposes minimum standards for all models, stricter rules for high-risk uses, and shared duties across the AI value chain, focusing regulation on high-risk applications, transparency, risk management, and expanded EU content moderation rules.
This course examines the European Union’s Artificial Intelligence Act (AI Act) through the lens of its proclaimed risk-based regulatory approach. While the Act aims to balance innovation and oversight by avoiding over-regulation, the course critically explores where its provisions fall short of this goal. Participants will analyse how the AI Act can be more effectively implemented to align with its original intent and how its existing mechanisms offer potential for future-proof, adaptable legislation. The course also highlights key takeaways for global policymakers seeking to regulate AI based on risk and proportionality.
This course introduces the concept of Artificial Legal Intelligence (ALI) and explores how advancements in AI are transforming the legal profession. Participants will examine the evolution from classical computational logic to the development of LegalTech tools that automate legal tasks, expand access to justice, and streamline legal services. The course covers various technologies, including online legal platforms, DIY legal tools, virtual assistants, and robot lawyers. It also analyzes the implications of the EU Artificial Intelligence Act (AIA) on the use and regulation of these tools, offering insights into the future landscape of the legal services market.
This course offers a critical overview of the newly adopted EU Artificial Intelligence Act (Regulation 2024/1689). It examines the regulation’s structure, objectives, and key concepts, as well as its material and territorial scope. The course explores underlying principles such as fairness, transparency, and accountability, and analyzes prohibited AI practices and the regulation of high-risk and general-purpose AI systems. It also addresses transparency obligations, certification, supervision, and sanctions. Finally, the course reflects on the regulation’s complexity and its potential impact on responsible AI innovation in the EU and beyond.
This course explores the geopolitical and strategic dimensions of artificial intelligence regulation, with a special focus on the rise of Large Language Models (LLMs) following the launch of ChatGPT. Rather than simply analyzing regulatory proposals, the course investigates how countries and companies use regulation as a tool of power and international influence. Through historical examples and comparative analysis, we examine the trade-offs between regulatory arbitrage and fragmentation, the challenges to international harmonization, and the strategic behavior of public and private actors in the global race for AI leadership.
This course explores the EU Artificial Intelligence Act’s groundbreaking approach to regulating deepfakes. Focusing on the interpretation of the term “existing” within the Act’s definition, it critically examines how narrow readings could limit the law’s effectiveness. The course proposes a teleological, purpose-driven interpretation to ensure broader protection—extending safeguards even to synthetic media that do not depict real people or events.
This course explores the European Union's Artificial Intelligence Act (AI Act), finalized in 2024, with a focus on its transparency and explainability requirements for AI systems. It introduces the TEFT (Transparency-Explainability Functionality and Tensions) framework, which helps analyze the legal, technical, and ethical challenges of implementing these requirements in practice. Through literature review and a detailed examination of the AI Act, the course maps the interests, risks, and goals of key stakeholders—including AI providers, regulators, and affected individuals—highlighting the tensions between transparency and the protection of intellectual property.
This stream explores the ethical, philosophical, and policy implications of artificial consciousness. It begins with a critical examination of the political and ethical arguments for a global moratorium on synthetic phenomenology—a proposed international ban on research that directly aims to create or risks creating conscious artificial systems. The stream then shifts to a constructive phase, introducing a conceptual framework for refining such a moratorium through rational, evidence-based constraints. Students will engage with debates surrounding the moral status of artificial minds, the future of post-biotic intelligence, and the evolving criteria for ethically acceptable AI development. The stream encourages critical thinking and interdisciplinary dialogue on one of the most urgent questions in emerging technology: What kinds of conscious experiences, if any, should we allow or aim to create in artificial systems?
This course explores the anticipated impact of the EU Artificial Intelligence Act on key areas of intellectual property law where AI technologies raise legal and regulatory challenges. Participants will examine issues such as authorship and ownership of AI-generated works, IP protection for the technologies underlying AI systems, and the implications of using IP-protected content to train or operate AI models. The course also discusses how AI systems may infringe third-party rights or act as intermediaries in IP violations, and how traditional legal standards may be influenced by AI developments. Through a critical analysis of the EU AI Act’s scope and potential, the course highlights existing gaps and considers where new IP legislation may be needed in response to the evolving role of AI.
This course examines the regulatory challenges of applying the EU AI Act to the financial sector, where existing sector-specific rules and the structural features of AI-driven services must align with horizontal AI requirements. It highlights regulatory gaps, explores how the Digital Operational Resilience Act (DORA) can address AI-related risks through governance of ICT risks, and assesses the limits of co-regulation tools such as regulatory sandboxes. Finally, it discusses the importance of coordinated supervisory policies and cross-authority collaboration, laying the groundwork for effective financial AI oversight in Europe.
This course provides an in-depth analysis of the AI Act, the European Union’s first binding legislation on Artificial Intelligence. Through a practical and critical approach, it traces the Act’s legislative journey — from the policy documents that preceded the European Commission’s proposal to the negotiations and final approval in the European Parliament in March 2024. The course examines the key developments and challenges of the regulation, assessing how it seeks to balance the protection of fundamental rights with the promotion of AI innovation. Participants will explore the main elements of the original proposal, the amendments introduced during the legislative process, and the remaining weaknesses regarding scope, enforcement mechanisms, and future-proofing. Finally, the course offers reflections on how to achieve a fair balance between regulation, innovation, and rights protection, equipping participants with the tools to understand, interpret, and apply one of Europe’s most impactful pieces of legislation to date.
This course explores the complex relationship between artificial intelligence (AI) regulation and innovation. While regulations and standards are essential to minimise risks and maximise benefits, they are often criticised for potentially stifling innovation. Through a critical examination of this debate, the course analyses current trends in AI regulation, with a particular focus on the proposed European AI Act and the standards supporting its implementation. Participants will engage with arguments claiming that regulation hinders innovation, evaluate these perspectives, and consider alternative viewpoints that demonstrate how regulation and standards can, in fact, foster innovation in the AI field.
This course critically examines AI policy in India, arguing that efforts to position the country as a global AI leader promote a model of informational capitalism that treats data as a key economic resource. It highlights how policy supports market-led development and firm self-regulation, while maintaining a strong state role in shaping the AI market through law and infrastructure.
As generative AI spreads, policymakers risk falling into the “Copyright Trap”: the mistaken belief that expanding copyright will protect creators, when it may instead entrench powerful interests and harm vulnerable actors. This course identifies three drivers of this trap and urges alternative responses beyond proprietary logic.
As the EU’s proposed Artificial Intelligence Act seeks to standardize concepts like transparency, fairness, and accountability, difficult normative questions arise. This course identifies three pathways for standardization: SSOs making normative decisions themselves, tracking existing consensus, or requiring “ethical disclosure by default.” It argues that ethical disclosure — through testing, documentation, and public reporting — best shifts complex decisions to local stakeholders while limiting provider discretion, ensuring more legitimate and context-sensitive AI governance.
As hyper-engaging dark patterns become widespread on online platforms, concerns grow about their impact on user autonomy and well-being. This course argues that these addictive, data-driven interface designs should be considered unlawful under EU law, particularly under the Unfair Commercial Practices Directive as a form of undue influence. While the Digital Services Act and the AI Act can help address the issue, they are seen as complementary rather than primary tools for regulating such practices.
This course explores the European Union’s hybrid governance approach to Artificial Intelligence (AI), with a focus on the role of harmonized European standards (HES). While HES offer flexibility and technical expertise, they face challenges related to legitimacy and knowledge gaps. The course introduces the concept of experimental standardization—an ex-ante evaluation method grounded in experimental governance and evidence-based policymaking—as a solution to these issues. Through practical application in the context of HES, students will examine how this approach can enhance legitimacy, bridge epistemic gaps, and foster collaboration between science and policy in the regulation of AI.
This course offers an in-depth analysis of the EU AI Act’s approach to regulating general-purpose artificial intelligence (AI), including systems like ChatGPT. Highlighting a shift from reactive to proactive governance, the course explores the Act’s hybrid regulatory model—combining product safety and standardisation with elements of fundamental rights protection. Participants will examine the legal obligations, enforcement mechanisms, and penalties related to the development and use of general-purpose AI within the EU. The course also addresses the broader implications of the AI Act as a potential global standard, emphasizing the importance of adaptability, enforcement, and international collaboration.
This course addresses the urgent need to define ethical and legal frameworks for the development and use of Artificial Intelligence (AI) during what many experts consider a critical turning point in history. It emphasizes the role of political institutions in creating a minimal yet effective set of normative constraints, supported by a continuous, evidence-based process of critical discussion and revision. The course advocates for inclusive, proactive engagement from diverse stakeholders to counterbalance the concentration of power in private and military sectors. It introduces five key problem domains in AI ethics, offering practical recommendations, with a special focus on initiating action within the European Union.
This course explores outcomes-based regulation as a framework for promoting responsible Artificial Intelligence (AI) governance, with a particular focus on its application in Africa. By examining legal and technological perspectives, the course analyzes how regulatory bodies, corporations, and public stakeholders can collaboratively shape adaptive and inclusive AI policies. It highlights the importance of defining clear regulatory goals ("the what") while allowing flexibility in how institutions achieve these outcomes ("the how"). The course also considers global regulatory trends and their relevance to sustainable and equitable AI development across African contexts.
This course examines how the European Union’s Artificial Intelligence Act (AI Act) addresses the regulation of deep fakes. As the first legal framework to define and regulate deep fakes, the AI Act establishes transparency obligations aimed at mitigating their harmful use. While the Act marks significant progress, it leaves important gaps—particularly in cases such as nonconsensual deep fake pornography. Through a critical analysis of the legal provisions, the course explores the rationale behind the chosen regulatory approach, its limitations, and the broader implications of the AI Act for the future landscape of deep fake governance.
As artificial intelligence (AI) continues to transform industries, it brings not only innovation but also a range of risks that require careful governance. This course offers a comprehensive overview of existing AI governance frameworks developed by governments, organisations, and companies to manage such risks. Through a systematic literature review of 28 key studies, the course addresses essential questions about AI governance: who is responsible, what is governed, when governance takes place in the AI lifecycle, and how it is implemented. Participants will explore governance approaches across different levels — team, organisational, industry, national, and international — and gain tools to critically assess and apply the most suitable frameworks to their own AI systems. This course is designed for those seeking to understand and navigate the complex landscape of AI governance, including researchers, professionals, and policymakers.
This course examines the EU AI Act’s risk classification system and addresses the lack of a clear methodology for assessing AI risks. It proposes a new approach, inspired by the IPCC framework, to build real-world risk scenarios and balance competing values through a proportionality test. Practical applications include regulatory implementation, risk significance assessment, and internal risk management for AI deployers.
This course explores how AI-driven innovation creates legal uncertainty, as companies commercialize AI tools amid unclear and shifting regulation. It argues that co-regulatory approaches, such as those in the European AI Act, can intensify instability by forcing businesses to rely on uncertain legal expectations. It calls for institutional diversification to support more stable and resilient AI governance.
This course offers a critical exploration of the intersection between the European Union’s Artificial Intelligence (AI) Act and EU copyright law. It examines how AI regulation affects copyright throughout the AI lifecycle — from data input and model training to output generation — highlighting legal ambiguities and gaps that may shape future policy. Key topics include the role of text and data mining (TDM) under the Copyright in the Digital Single Market Directive, transparency and compliance requirements, and the Act’s extraterritorial effects. The course also addresses concerns around fair remuneration for creators, proposing solutions such as collective licensing, statutory licensing, and AI output levies. Participants will gain insights into the evolving relationship between AI governance and copyright protection in the EU.
This course explores the challenges that artificial intelligence poses to human legal systems. As AI machines begin to perform cognitive tasks independently and interact directly with the world, traditional human-centred law faces new limits. The course examines why legal control over AI cannot be direct and must instead be mediated through human behaviour. It outlines what the law can realistically achieve in regulating AI, proposes guiding principles for this regulation, and compares three transnational approaches for implementation.
This course explores copyright challenges related to generative AI, using ChatGPT as a central case study. It examines how AI systems rely on large datasets — often containing copyrighted material — for training, raising questions about fair use, derivative works, and the legality of data use. The course discusses potential copyright infringements and offers strategies to protect rights holders while promoting AI innovation. Special focus is given to how copyright laws may need to evolve to address authorship and ownership in AI-generated content.
This course explores the European Union’s regulatory impact on artificial intelligence (AI), focusing on the proposed AI Act. It examines how the Act may generate a “Brussels Effect” by influencing global standards, while also highlighting its limitations—particularly in safeguarding fundamental rights. These shortcomings may undermine the EU’s broader ambition to export its values through AI governance. The course also considers the implications of this approach for other regulatory efforts, such as the Council of Europe’s proposed AI convention.
This course explores the complex issue of liability in the context of Artificial Intelligence (AI), focusing on who should be held responsible when AI systems cause harm. Using real-world scenarios — such as accidents involving autonomous vehicles or discriminatory decisions by AI tools — the course examines the limitations of risk-based regulation, like the EU AI Act, which often fails to provide victims with rights or clear paths to compensation. Through a law and economics lens, participants will analyse how proposed AI liability rules in the EU, including updates to the Product Liability Directive, aim to complement existing regulations and create fairer, more effective accountability mechanisms for AI-related harms.
This course analyzes how the European Union portrays itself as the main protector of citizens from technological risks through the proposed Artificial Intelligence Act. It argues that, despite its focus on fundamental rights, the Act delegates protection to expert bodies rather than empowering citizens, and ultimately serves to strengthen the EU’s political authority more than to advance genuine digital emancipation.
As AI supply chains become increasingly complex, ensuring compliance with the EU AI Act (AIA) poses significant challenges. This course introduces an automated system designed to streamline provider-side AIA compliance analyses. Participants will learn about transparency artifacts that capture essential metadata and the algorithms that use this information to deliver real-time compliance predictions, making AIA compliance faster, more accessible, and more efficient for AI developers.
This course presents a governance framework designed to help organisations innovate responsibly with Artificial Intelligence (AI) systems, in alignment with the EU Artificial Intelligence Act (AIA). Based on secondary and primary research, the framework addresses four key pressures — technological, normative, value creation, and regulatory — that shape organisational behaviour in AI development. Through a conceptual model and empirical validation with organisations in the Netherlands, the course explores how factors such as company size, outsourcing, and offshoring affect AIA compliance. Participants will gain insights into the challenges of implementing responsible AI governance and strategies for aligning innovation with legal and ethical standards.
This course explores the impact of the EU Artificial Intelligence Act (AI Act) on AI regulation within the banking and financial sectors. As a pioneering legislative framework, the AI Act introduces risk-based classifications and strict requirements for high-risk AI systems, aiming to ensure transparency, accountability, and fundamental rights protection. The course focuses on how the Act intersects with financial supervision, highlighting the roles of the AI Office and AI Board. Participants will examine regulatory challenges, including creditworthiness assessments and general-purpose AI applications, and discuss how adaptive governance can promote ethical AI use while supporting innovation and consumer protection in finance.
This course examines how the EU’s Artificial Intelligence Act (AIA) can be implemented and enforced by national and EU bodies, including the European Commission and the new AI Office. It clarifies the AIA’s governance framework and proposes a normative model to ensure coordinated, effective enforcement across institutional levels.
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