Simon Mayer
160 results
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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, Reflexive anticipatory reasoning by BDI agents(Springer Science and Business Media LLC, 2025-01-02) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, FoodCoach: Fully Automated Diet Counseling(2025-02-11); ; Scopus© Citations 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, NeighboAR: Efficient Object Retrieval using Proximity-and Gaze-based Object Grouping with an AR SystemHumans only recognize a few items in a scene at once and memorize three to seven items in the short term. Such limitations can be mitigated using cognitive offloading (e.g., sticky notes, digital reminders). We studied whether a gaze-enabled Augmented Reality (AR) system could facilitate cognitive offloading and improve object retrieval performance. To this end, we developed NeighboAR, which detects objects in a user's surroundings and generates a graph that stores object proximity relationships and user's gaze dwell times for each object. In a controlled experiment, we asked N=17 participants to inspect randomly distributed objects and later recall the position of a given target object. Our results show that displaying the target together with the proximity object with the longest user gaze dwell time helps recalling the position of the target. Specifically, NeighboAR significantly reduces the retrieval time by 33%, number of errors by 71%, and perceived workload by 10%.Type:journal articleJournal:Proceedings of the ACM on Human-Computer InteractionVolume:8Issue:ETRADOI:10.1145/3655599Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Digital Companion Architecture for Ambient Intelligence(Association for Computing Machinery (ACM), 2024-05-13); Scopus© Citations 5 - 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