arXiv subjects

Frederik Zuiderveen Borgesius

Publications and source records attributed to Frederik Zuiderveen Borgesius.

44 records · Page 3Linked to original sources

arXiv2025

AI, insurance, discrimination and unfair differentiation. An overview and research agenda

Insurers underwrite risks: they calculate risks and decide on the insurance price. Insurers seem captivated by two trends enabled by Artificial Intelligence (AI). First, insurers could use AI for analysing more and new types of data to assess risks more precisely: data-intensive underwriting. Second, insurers could use AI to monitor the behaviour of individual consumers in real-time: behaviour-based insurance. For example, some car insurers offer a discount if the consumer agrees to being tracked by the insurer and drives safely. While the two trends bring many advantages, they may also have discriminatory effects on society. This paper focuses on the following question. Which effects related to discrimination and unfair differentiation may occur if insurers use data-intensive underwriting and behaviour-based insurance?

cs.CY↗

arXiv2025

The EU Digital Services Act: what does it mean for online advertising and adtech?

What does the Digital Services Act (DSA) mean for online advertising? We describe and analyse the DSA rules that are most relevant for online advertising and adtech (advertising technology). We also highlight to what extent the DSA's advertising rules add something to the rules in the General Data Protection Regulation (GDPR) and the ePrivacy Directive. The DSA introduces several specific requirements for online advertising. First, the DSA imposes transparency requirements in relation to advertisements. Second, very large online platforms (VLOPs) should develop a publicly available repository with information about the ads they presented. Third, the DSA bans profiling-based advertising (behavioural advertising) if it uses sensitive data or if it targets children. Besides these specific provisions, the general rules of the DSA on illegal content also apply to advertising. Advertisements are a form of information, and thus subject to the general DSA rules. Moreover, we conclude that the DSA applies to some types of ad tech companies. For example, ad networks, companies that connect advertisers to publishers of apps and websites, should be considered platforms. Some ad networks may even qualify as VLOPs. Hence, ad networks must comply with the more general obligations in the DSA. The application of these general rules to advertisements and ad networks can have far-reaching effects that have been underexplored and deserve further research. We also show that certain aspects of the DSA are still unclear. For instance, we encourage the European Commission or regulators to clarify the concepts of 'online platform' and 'recipients' in the context of ad networks and other adtech companies.

cs.CY↗

arXiv2024

The GDPR's Rules on Data Breaches: Analysing Their Rationales and Effects

The General Data Protection Regulation (GDPR) requires an organisation that suffers a data breach to notify the competent Data Protection Authority. The organisation must also inform the relevant individuals, when a data breach threatens their rights and freedoms. This paper focuses on the following question: given the goals of the GDPR's data breach notification obligation, and we assess the obligation in the light of those goals. We refer to insights from information security and economics, and present them in a reader-friendly way for lawyers. Our main conclusion is that the GDPR's data breach rules are likely to contribute to the goals. For instance, the data breach notification obligation can nudge organisations towards better security; such an obligation enables regulators to perform their duties; and such an obligation improves transparency and accountability. However, the paper also warns that we should not have unrealistic expectations of the possibilities for people to protect their interests after a data breach notice. Likewise, we should not have high expectations of people switching to other service providers after receiving a data breach notification. Lastly, the paper calls for Data Protection Authorities to publish more information about reported data breaches. Such information can help to analyse security threats.

cs.CR↗

arXiv2024

Automated decision-making and artificial intelligence at European borders and their risks for human rights

Many countries use automated decision-making (ADM) systems, often based on artificial intelligence (AI), to manage migration at their borders. This interdisciplinary paper explores two questions. What are the main ways that automated decision-making is used at EU borders? Does such automated decision-making bring risks related to human rights, and if so: which risks? The paper introduces a taxonomy of four types of ADM systems at EU borders. Three types are used at borders: systems for (1) identification and verification by checking biometrics, (2) risk assessment, and (3) border monitoring. In addition, (4) polygraphs and emotion detectors are being tested at EU borders. We discuss three categories of risks of such automated decision-making, namely risks related to the human rights to (1) privacy and data protection, (2) nondiscrimination, and (3) a fair trial and effective remedies. The paper is largely based on a literature review that we conducted about the use of automated decision-making at borders. The paper combines insights from several disciplines, including social sciences, law, computer science, and migration studies.

cs.CY↗

arXiv2024

Non-discrimination law in Europe: a primer for non-lawyers

This brief paper provides an introduction to non-discrimination law in Europe. It answers the questions: What are the key characteristics of non-discrimination law in Europe, and how do the different statutes relate to one another? Our main target group is computer scientists and users of artificial intelligence (AI) interested in an introduction to non-discrimination law in Europe. Notably, non-discrimination law in Europe differs significantly from non-discrimination law in other countries, such as the US. We aim to describe the law in such a way that non-lawyers and non-European lawyers can easily grasp its contents and challenges. The paper shows that the human right to non-discrimination, to some extent, protects individuals against private actors, such as companies. We introduce the EU-wide non-discrimination rules which are included in a number of EU directives, and also explain the difference between direct and indirect discrimination. Significantly, an organization can be fined for indirect discrimination even if the company, or its AI system, discriminated by accident. The last section broadens the horizon to include bias-relevant law and cases from the GDPR, the EU AI Act, and related statutes. Finally, we give reading tips for those inclined to learn more about non-discrimination law in Europe.

cs.CY↗

arXiv2023

Targeted and Troublesome: Tracking and Advertising on Children's Websites

On the modern web, trackers and advertisers frequently construct and monetize users' detailed behavioral profiles without consent. Despite various studies on web tracking mechanisms and advertisements, there has been no rigorous study focusing on websites targeted at children. To address this gap, we present a measurement of tracking and (targeted) advertising on websites directed at children. Motivated by lacking a comprehensive list of child-directed (i.e., targeted at children) websites, we first build a multilingual classifier based on web page titles and descriptions. Applying this classifier to over two million pages, we compile a list of two thousand child-directed websites. Crawling these sites from five vantage points, we measure the prevalence of trackers, fingerprinting scripts, and advertisements. Our crawler detects ads displayed on child-directed websites and determines if ad targeting is enabled by scraping ad disclosure pages whenever available. Our results show that around 90% of child-directed websites embed one or more trackers, and about 27% contain targeted advertisements--a practice that should require verifiable parental consent. Next, we identify improper ads on child-directed websites by developing an ML pipeline that processes both images and text extracted from ads. The pipeline allows us to run semantic similarity queries for arbitrary search terms, revealing ads that promote services related to dating, weight loss, and mental health; as well as ads for sex toys and flirting chat services. Some of these ads feature repulsive and sexually explicit imagery. In summary, our findings indicate a trend of non-compliance with privacy regulations and troubling ad safety practices among many advertisers and child-directed websites. To protect children and create a safer online environment, regulators and stakeholders must adopt and enforce more stringent measures.

cs.CY↗

arXiv2022

Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?

Organisations can use artificial intelligence to make decisions about people for a variety of reasons, for instance, to select the best candidates from many job applications. However, AI systems can have discriminatory effects when used for decision-making. To illustrate, an AI system could reject applications of people with a certain ethnicity, while the organisation did not plan such ethnicity discrimination. But in Europe, an organisation runs into a problem when it wants to assess whether its AI system accidentally discriminates based on ethnicity: the organisation may not know the applicants' ethnicity. In principle, the GDPR bans the use of certain 'special categories of data' (sometimes called 'sensitive data'), which include data on ethnicity, religion, and sexual preference. The proposal for an AI Act of the European Commission includes a provision that would enable organisations to use special categories of data for auditing their AI systems. This paper asks whether the GDPR's rules on special categories of personal data hinder the prevention of AI-driven discrimination. We argue that the GDPR does prohibit such use of special category data in many circumstances. We also map out the arguments for and against creating an exception to the GDPR's ban on using special categories of personal data, to enable preventing discrimination by AI systems. The paper discusses European law, but the paper can be relevant outside Europe too, as many policymakers in the world grapple with the tension between privacy and non-discrimination policy.

cs.CY↗

arXiv2022

Demystifying the Draft EU Artificial Intelligence Act

In April 2021, the European Commission proposed a Regulation on Artificial Intelligence, known as the AI Act. We present an overview of the Act and analyse its implications, drawing on scholarship ranging from the study of contemporary AI practices to the structure of EU product safety regimes over the last four decades. Aspects of the AI Act, such as different rules for different risk-levels of AI, make sense. But we also find that some provisions of the Draft AI Act have surprising legal implications, whilst others may be largely ineffective at achieving their stated goals. Several overarching aspects, including the enforcement regime and the risks of maximum harmonisation pre-empting legitimate national AI policy, engender significant concern. These issues should be addressed as a priority in the legislative process.

cs.CY↗