Emilia Gómez

This article revisits Williams’s concept of flow, Firth’s notion of algorithmic flow, and Picone et al.’s concept of Small Acts of Engagement (SAOE) to examine how users experience TikTok’s algorithmic flow. SAOE are understood as low-effort, everyday acts through which users negotiate their identities and shape their digital consumption. While SAOE have been a valuable lens for understanding user agency on earlier social media platforms, we argue that TikTok’s interface design and cultural framing introduce new challenges. The platform’s design, together with users’ perception of TikTok’s algorithmic flow as a televisual experience, reduces their SAOE to a minimal expression: a rhythm of watch, scroll, repeat , discouraging other forms of intentional, identity-driven engagement. Through a mixed-methods study combining interviews and analysis of participants’ donated TikTok data, we show how our participants experience TikTok as a place for effortless entertainment and continuous consumption, closely resembling the experience of traditional television flow.

Recommender systems exert a profound influence on digital information consumption. Although they excel at content personalisation, recommenders are increasingly investigated for disseminating irrelevant, unwanted, or harmful suggestions. Such exposure diminishes user satisfaction and exacerbates broader societal challenges, including the spread of misinformation, radicalisation, and the systemic erosion of trust. While existing platforms provide tools to limit the disclosure of problematic content, these mechanisms often lack efficacy and fail to adapt dynamically to user input. This article presents an intuitive, model-agnostic, and distribution-free framework that employs conformal risk control to provide statistical guarantees regarding unwanted content in personalised feeds. By leveraging simple feedback, our method ensures that the risk of encountering undesirable items is provably bounded. Furthermore, we address a common limitation of traditional conformal risk control, whereby risk mitigation often yields overly restrictive or smaller recommendation sets. We overcome this by incorporating implicit feedback from consumed items to expand the set of recommendations while maintaining robust risk constraints. Experimental evaluation conducted on datasets from two different real-world scenarios ( i.e., a short-video platform and a music streaming service) confirms that our approach achieves a controllable and effective reduction of unwanted recommendations in diverse domains with minimal computation overhead.

This work investigates how personalised Music Emotion Recognition (MER) systems may lead to sensitive profiling when applied to musically induced emotions in politically charged contexts. We focus on traditional Colombian music with explicit political content, including (1) vallenatos and social songs aligned with the left-wing guerrilla Fuerzas Armadas Revolucionarias de Colombia (FARC), and (2) corridos linked to sympathisers of the right-wing paramilitary group Autodefensas Unidas de Colombia (AUC). Using data from 49 participants with diverse political leanings, we train personalised machine learning models to predict induced emotional responses – particularly negative emotions. Our findings reveal that political identity plays a significant role in shaping emotional experiences of music with explicit political content, and that emotion recognition models can capture this variation to a certain extent. These results raise critical concerns about the potential misuse of emotion recognition technologies. What is often framed as a tool for wellbeing and emotional regulation could, in politically sensitive contexts, be repurposed for user profiling. This work highlights the ethical risks of deploying AI-driven emotion analysis without safeguards, particularly among populations that are politically or socially vulnerable. We argue that subjective emotional responses may constitute sensitive personal data, and that failing to account for their sociopolitical context could amplify harm and exclusion.

This study aims to comprehensively explore the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and their impact on testing procedures. The research focuses on essential requirements for trustworthy AI, including cybersecurity, transparency, robustness, and fairness. We first analyse the role of AI at the most relevant operational layers of AVs, and discuss the implications of the EU’s AI Act on AVs, highlighting the importance of the concept of a safety component. Using an expert opinion-based methodology, involving an interdisciplinary workshop with 21 academics and a subsequent in-depth analysis by a smaller group of experts, this study provides a state-of-the-art overview of the current landscape of vehicle regulation and standards, including ex-ante, post-hoc, and accident investigation processes, highlighting the need for new testing methodologies for both Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). The study also provides a detailed analysis of cybersecurity audits, explainability in AI decision-making processes and protocols for assessing the robustness and ethical behaviour of predictive systems in AVs. The analysis highlights significant challenges and suggests future directions for research and development of AI in AV technology, emphasising the need for multidisciplinary expertise. The study’s conclusions have relevant implications for the development of trustworthy AI systems, vehicle regulations, and the safe deployment of AVs.