Aurelia Tamo-Larrieux
Title
Prof. Dr.
Last Name
Tamo-Larrieux
First name
Aurelia
Email
aurelia.tamo@unisg.ch
ORCID
Phone
+41 71 224 2800
21 results
Now showing 1 - 10 of 21
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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, 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, 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 3 - 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 38 - 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 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mapping the Issues of Automated Legal Systems: Why Worry About Automatically Processable Regulation?The field of computational law has increasingly moved into the focus of the scientific community, with recent research analysing its issues and risks. In this article, we seek to draw a structured and comprehensive list of societal issues that the deployment of automatically processable regulation could entail. We do this by systematically exploring attributes of the law that are being challenged through its encoding and by taking stock of what issues current projects in this field raise. This article adds to the current literature not only by providing a needed framework to structure arising issues of computational law but also by bridging the gap between theoretical literature and practical implementation. Key findings of this article are: (1) The primary benefit (efficiency vs. accessibility) sought after when encoding law matters with respect to the issues such an endeavor triggers; (2) Specific characteristics of a project—project type, degree of mediation by computers, and potential for divergence of interests—each impact the overall number of societal issues arising from the implementation of automatically processable regulation.Type:journal articleJournal:Artificial Intelligence and Law - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine Capacity of Judgment: An interdisciplinary approach for making machine intelligence transparent to end-usersIntelligent machines surprise us with unexpected behaviors, giving rise to the question of whether such machines exhibit autonomous judgment. With judgment comes (the allocation of) responsibility. While it can be dangerous or misplaced to shift responsibility from humans to intelligent machines, current frameworks to think about responsible and transparent distribution of responsibility between all involved stakeholders are lacking. A more granular understanding of the autonomy exhibited by intelligent machines is needed to promote a more nuanced public discussion and allow laypersons as well as legal experts to think about, categorize, and differentiate among the capacities of artificial agents when distributing responsibility. To tackle this issue, we propose criteria that would support people in assessing the Machine Capacity of Judgment (MCOJ) of artificial agents. We conceive MCOJ drawing from the use of Human Capacity of Judgment (HCOJ) in the legal discourse, where HCOJ criteria are legal abstractions to assess when decision-making and judgment by humans must lead to legally binding actions or inactions under the law. In this article, we show in what way these criteria can be transferred to machines.Type:journal articleJournal:Technology in SocietyVolume:71Scopus© Citations 4
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