Publications
Top Keywords
- AI Act (11)
- Art. 10 EVRM (25)
- Art. 17 CDSM Directive (13)
- Artificial intelligence (78)
- Big data (12)
- Constitutional and administrative law (11)
- Consumer law (11)
- Content moderation (22)
- Copyright (201)
- Cybersecurity (10)
- Data protection (29)
- Data protection law (11)
- Digital Services Act (DSA) (31)
- Digital Single Market (13)
- EU (19)
- EU law (25)
- Europe (13)
- Fake news (14)
- Freedom of expression (49)
- Fundamental rights (18)
- GDPR (22)
- Human rights (31)
- Intellectual property (30)
- Internet (24)
- Journalism (16)
- Kluwer Information Law Series (43)
- Licensing (14)
- Media law (29)
- Online platforms (20)
- Patent law (20)
- Personal data (35)
- Platforms (24)
- Privacy (327)
- Regulation (12)
- Social media (11)
- Software (10)
- Surveillance (11)
- Text and Data Mining (TDM) (21)
- Trademark law (15)
- Transparency (19)
The Cost of Reach: Testing the Role of Ad Delivery Algorithms in Online Political Campaigns external link
Abstract
Political campaigns increasingly rely on digital ads to engage potential voters, with platforms like Meta offering advertisers detailed targeting options based on user demographics, behaviors, and interests. A lesser-known feature of social media advertising is the ad delivery algorithm, which sets ad prices through auctions and may deliver ads to “relevant” audiences without explicit direction from advertisers. This automated process raises the question of whether it may introduce systemic biases into digital political advertising. To examine the pricing of political ads, we conducted a pre-registered study in collaboration with three Dutch political parties. We placed 135 identical ads on the parties’ official Facebook and Instagram accounts, targeting nine different audiences during the 2022 municipal elections. All ads ran simultaneously with identical settings, daily budgets, texts, and images, ensuring any differences in pricing and delivery were due to the advertiser and target audiences. We anticipated that parties would pay less when ads targeted “relevant” audiences. However, we observed pricing differences between parties and audiences that did not always align with our expectations. For instance, one party paid 9.24% to 10.74% less per 1,000 users. Additionally, lower-educated citizens, women, and younger individuals (18–24) were more expensive to reach. These results have important implications. The unequal costs create unfair and unequal playing field, while the exclusion of certain groups from ad delivery may limit their access to election information. Our findings suggest that simply restricting targeting capabilities, without addressing the role of ad delivery algorithms, would leave pricing disparities unchecked and without transparency
RIS
Bibtex
Annotatie bij Hof van Justitie van de EU 7 december 2023 (OQ \ Land Hessen) download
Financial Regulation, Political Context, and Technology in the European Union download
Abstract
Three decades after the establishment of the European Single Market, the ongoing digital transformation of its core socioeconomic pillars generates regulatory shifts. These shifts are also initiated by geopolitical pressures. Within its data-driven economy, innovative financial solutions and technologies strive to unfold against the backdrop of what is now a multilayered, complex regulatory environment. This chapter dismantles this complexity by outlining the main regulatory building blocks relevant to the European Union’s financial industry vis-à-vis the policy goals of digital and economic sovereignty. Given the focus of this book on decentralization technologies in the financial industry, it then contextualizes the impact of such policy on these and discusses the present dynamics between regulation and innovative technology.
Links
RIS
Bibtex
Generative AI and Creative Commons Licences – The Application of Share Alike Obligations to Trained Models, Curated Datasets and AI Output external link
Abstract
This article maps the impact of Share Alike (SA) obligations and copyleft licensing on machine learning, AI training, and AI-generated content. It focuses on the SA component found in some of the Creative Commons (CC) licences, distilling its essential features and layering them onto machine learning and content generation workflows. Based on our analysis, there are three fundamental challenges related to the life cycle of these licences: tracing and establishing copyright-relevant uses during the development phase (training), the interplay of licensing conditions with copyright exceptions and the identification of copyright-protected traces in AI output. Significant problems can arise from several concepts in CC licensing agreements (‘adapted material’ and ‘technical modification’) that could serve as a basis for applying SA conditions to trained models, curated datasets and AI output that can be traced back to CC material used for training purposes. Seeking to transpose Share Alike and copyleft approaches to the world of generative AI, the CC community can only choose between two policy approaches. On the one hand, it can uphold the supremacy of copyright exceptions. In countries and regions that exempt machine-learning processes from the control of copyright holders, this approach leads to far-reaching freedom to use CC resources for AI training purposes. At the same time, it marginalises SA obligations. On the other hand, the CC community can use copyright strategically to extend SA obligations to AI training results and AI output. To achieve this goal, it is necessary to use rights reservation mechanisms, such as the opt-out system available in EU copyright law, and subject the use of CC material in AI training to SA conditions. Following this approach, a tailor-made licence solution can grant AI developers broad freedom to use CC works for training purposes. In exchange for the training permission, however, AI developers would have to accept the obligation to pass on – via a whole chain of contractual obligations – SA conditions to recipients of trained models and end users generating AI output.
Artificial intelligence, Copyright, creative commons, Licensing, machine learning
RIS
Bibtex
When public values and user-centricity in e-government collide – A systematic review download
Abstract
User-centricity in e-government is a double-edged sword. While it helps governments design digital services tailored to the needs of citizens, it may also increase the burden on users and deepen the digital divide. From an institutional perspective, these fundamental conflicts are inevitable. To better understand the role and effect of user-centricity in e-government, this paper analyses academic literature on user-centricity and public values. The analysis leads to three main insights: First, there is a conflict in citizen representation that may result from the normative dominance of decision-makers. Second, we identify an accountability conflict that can prevent usercentric innovation from thriving in a highly institutionalized environment. Third, we identify a pluralism conflict that emerges from a clash between the reality of a diverse society and the assumed homogeneity of actors. The need to address these conflicts increases with rapid technological innovation, such as distributed ledger tech nologies, artificial intelligence, and trust infrastructures. These technologies put the user at the center stage and permeate aspects of social life beyond government. In response to these insights, we outline suggestions for further research and practice.
Links
Informatierecht, Overheidsinformatie
RIS
Bibtex
The AI Act National Security Exception: room for manoeuvres? external link
Abstract
In 2024, the EU legislators adopted a detailed national security exception to the AI Act, contravening prior EU case law. Beyond the possibility of a future ruling that would realign the AI Act’s scope with said case law, the impact of this exception might be limited by other applicable laws and the interpretative and practical difficulty of distinguishing between national and public security. The AI Act’s failure to sufficiently account for these intricacies risks further legal uncertainty within the already complex security landscape. Therefore, this blog post explores the challenges of implementing the exception of national security to the AI Act’s scope of application.