Aurelia Tamo-Larrieux
Title
Prof. Dr.
Last Name
Tamo-Larrieux
First name
Aurelia
Email
aurelia.tamo@unisg.ch
ORCID
Phone
+41 71 224 2800
23 results
Now showing 1 - 10 of 23
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scrutinizing Systemic Risks in Personalized Recommender Systems Through Sock-Puppet Auditing of VLOPs(ACM, 2026-01-30); ; ; ; Very Large Online Platforms (VLOPs) use personalized recommender systems to optimize their main performance metric: attention-based user engagement. In doing so, these systems might however amplify systemic risks by promoting controversial or polarizing content, thereby exacerbating issues such as misinformation, societal polarization, and the manipulation of civic discourse. To mitigate these risks, regulations such as the European Union's Digital Services Act (DSA) mandate increased data access and transparency, including for the auditing of personalized recommender systems. However, the data access provided by VLOPs remains limited-often restricted to specific user demographics, aggregate statistics, or curated datasets-hindering meaningful oversight. Consequently, new methods are needed to audit recommender systems effectively at the user level. In this paper, based on an analysis of the legal context and technical alternatives for data access, we present SOAP, the System for Observing and Analyzing Posts. SOAP is an open-source framework for auditing recommender systems using sock-puppet accounts. It enables fine-grained user-level analysis beyond the constrained data access typically provided by platforms. We detail SOAP's technical implementation and evaluate its ability to scrutinize systemic risks. Additionally, we tested SOAP in a workshop with over 100 participants and observed a measurable increase in participants' algorithmic literacy. This demonstrates SOAP's potential not only for research and regulatory auditing, but also as an educational framework to foster public awareness of algorithmic influence.Type:journal articleJournal:ACM Transactions on Recommender SystemsDOI:10.1145/3795516 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload(Taylor and Francis (United Kingdom), 2026-06-30); ; Privacy policies function as both legal documents and information sources for users, but their length and complexity often discourage engagement. In this paper, we investigate whether a personalised approach can address this issue by prioritising information that concerns individual users most while maintaining a policy’s legal compliance on disclosure. We first explored whether personal characteristics can be used to predict a person’s most concerned category and, hence, serve as a baseline for personalisation. We then conducted an eye-tracking experiment and interviews (n = 30) to understand the effectiveness of personalised reordering of privacy policies. In the interviews, many participants perceived personalised reordering as helpful, although others raised concerns about the invasion of privacy through this personalisation. The eye-tracking results indicate that personalised reordering leads to higher engagement for the first few sentences of a privacy policy. Based on our findings, we present design recommendations for creating legally compliant forms of privacy disclosures that encourage user engagement as well as discussions and implications on privacy disclosure compliance.Type:journal articleJournal:Behaviour & Information Technology - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Solid use case to empower and protect data subjects: Responsibilities under GDPR for governance of personal data stores(Elsevier BV, 2025-07); Decentralised data governance has emerged as an alternative model in response to the challenges of managing data and privacy in conventional centralised models. ‘Personal Data Stores’ (PDS) are at the forefront of this movement and provide forms of control over storage and management of data to the individual with the goal of empowering them. In this article, we argue how PDS, while being important technological innovations, are challenging to implement in the current regulatory landscape as the interpretation of responsibilities under the GDPR is woefully inadequate for decentralised systems. This represents a challenge to the decentralisation movement and makes it difficult to empower and protect individuals under the GDPR (data subjects) using PDS. A thorough understanding of the technological and legal situation and therefore an interdisciplinary approach is essential to make policymakers aware of any efforts that still need to be made to realise the decentralisation paradigm's goal. We therefore build upon research investigating GDPR compliance in decentralised data storage and management but do so through an interdisciplinary lens applied to an emerging application, Solid, that provides technical specifications for implementing it as the leading PDS implementation. By taking an interdisciplinary approach, we consider the interaction between the legal definitions from the GDPR and the implications of established case law with Solid's technical specifications and its possible implementations. We conclude with recommendations regarding the division of responsibilities for policymakers, authorities, market participants and technical developers to simultaneously protect and empower those involved in the use of PDS, particularly through Solid. Furthermore, the role of decentralised systems such as Solid is discussed, as well as the current unclear regulatory landscape surrounding it in the context of implementing the Data Governance Act (DGA). The implications for further AI development and within data spaces are also considered.Type:journal articleJournal:Computer Law & Security ReviewVolume:57Scopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Controlled Language Increases Comprehension of Law for People(Association for Computing Machinery (ACM), 2025-03-12); ; Type:journal articleJournal:ACM Journal on Computing and Sustainable SocietiesVolume:3Issue:1DOI:10.1145/3711128 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adoption of artificial intelligence in the judiciary: a comparison of 28 advanced democracies(Springer Science and Business Media LLC, 2025-07-24); Artificial Intelligence (AI) is increasingly used worldwide to make decisions, be it by public administrations, industry, banks, or insurers. One area with a particularly high impact on citizens is its use in judiciary processes. To this day, there has been little investigation into why countries decide to adopt AI in this area. In this article, we show that the mere promise of efficiency gains is not enough of an explanation for why countries adopt AI within judiciary processes. Instead, we show that the administrative burden (as measured by the number of days to trial), a government’s leaning within the political spectrum (namely towards the left), and the level of adoption of technology by governments in neighboring countries predict countries’ announcement to adopt AI in their judiciary. Taken together, our findings derived from a data analysis of 28 advanced democracies constitute an important step towards a better understanding of the factors shaping AI’s integration into judiciary processes.Type:journal articleJournal:Discover Artificial IntelligenceVolume:5Issue:1Scopus© Citations 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Responsible automatically processable regulation(Springer Science and Business Media LLC, 2024-03-28); ; Scopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How Distrust is Driving Artificial Intelligence Regulation in the European Union(2024-09-14); The emergence of new technologies often brings with it a complex interplay between their acceptance by society, gauging their risks, and whether it is warranted for the state to be involved-typically via new or amended regulation. However, what drives regulators and decision-makers to even consider the question of whether there is a need for involvement has remained under-studied. In this article, we propose viewing regulation as a process with five distinct phases: laissez-faire, awareness, politicisation, regulation and cool-off. A critical phase is the transition between awareness and politicisation, as the latter commonly leads to regulatory action. We look at the emergence of regulation for artificial intelligence, aviation, genetically modified organisms, disinformation and retail self-checkouts to show that there is a correlation between distrust and politicisation. We further show the probable causal link specifically for regulating artificial intelligence in the EU, and derive possible policy implications from this conclusion.Type:journal articleJournal:European Journal of Law and TechnologyVolume:15Issue:2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The challenge of open-texture in law(Springer Science and Business Media LLC, 2024-01-08); ; Van Dijck GijsAn important challenge when creating automatically processable laws concerns open-textured terms. The ability to measure open-texture can assist in determining the feasibility of encoding regulation and where additional legal information is required to properly assess a legal issue or dispute. In this article, we propose a novel conceptualisation of open-texture with the aim of determining the extent of open-textured terms in legal documents. We conceptualise open-texture as a lever whose state is impacted by three types of forces: internal forces (the words within the text themselves), external forces (the resources brought to challenge the definition of words), and lateral forces (the merit of such challenges). We tested part of this conceptualisation with 26 participants by investigating agreement in paired annotators. Five key findings emerged. First, agreement on which words are opentexture within a legal text is possible and statistically significant. Second, agreement is even high at an average inter-rater reliability of 0.7 (Cohen's kappa). Third, when there is agreement on the words, agreement on the Open-Texture Value is high. Fourth, there is a dependence between the Open-Texture Value and reasons invoked behind open-texture. Fifth, involving only four annotators can yield similar results compared to involving twenty more when it comes to only flagging clauses containing open-texture. We conclude the article by discussing limitations of our experiment and which remaining questions in real life cases are still outstanding.Type:journal articleJournal:Artificial Intelligence and LawScopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Regulating for trust: Can law establish trust in artificial intelligence?The current political and regulatory discourse frequently references the term “trustworthy artificial intelligence (AI).” In Europe, the attempts to ensure trustworthy AI started already with the High-Level Expert Group Ethics Guidelines for Trustworthy AI and have now merged into the regulatory discourse on the EU AI Act. Around the globe, policymakers are actively pursuing initiatives—as the US Executive Order on Safe, Secure, and Trustworthy AI, or the Bletchley Declaration on AI showcase—based on the premise that the right regulatory strategy can shape trust in AI. To analyze the validity of this premise, we propose to consider the broader literature on trust in automation. On this basis, we constructed a framework to analyze 16 factors that impact trust in AI and automation more broadly. We analyze the interplay between these factors and disentangle them to determine the impact regulation can have on each. The article thus provides policymakers and legal scholars with a foundation to gauge different regulatory strategies, notably by differentiating between those strategies where regulation is more likely to also influence trust on AI (e.g., regulating the types of tasks that AI may fulfill) and those where its influence on trust is more limited (e.g., measures that increase awareness of complacency and automation biases). Our analysis underscores the critical role of nuanced regulation in shaping the human-automation relationship and offers a targeted approach to policymakers to debate how to streamline regulatory efforts for future AI governance.Type:journal articleJournal:Regulation & GovernanceVolume:18Issue:3Scopus© Citations 42 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Typology of Automatically Processable RegulationType:journal articleJournal:Law, Innovation, and TechnologyVolume:14Issue:2
- «
- 1 (current)
- 2
- 3
- »