PhD Defense – AI Transparency in Trustworthy AI: From Metaphor to Governance Tool in EU Technology Regulation
2025-06-10
Katarzyna Söderlund defends her doctoral thesis, “AI Transparency in Trustworthy AI: From Metaphor to Governance Tool in EU Technology Regulation,” at Lund University (LTH).
Abstract
Transparency has emerged as a fundamental component of ethical AI guidelines around the world. In the European Union (EU), it is recognised as one of the core principles for fostering Trustworthy AI, and serves as a cornerstone in building an ecosystem of trust within the AI governance framework.
However, to support these ambitious policy objectives, the concept of transparency must be translated into clearly defined and implementable measures. Thus, by employing a combination of legal-doctrinal and socio-legal approaches, this compilation thesis aims to contribute to a clarified understanding of the concept of AI transparency in the EU’s AI governance discourses. I examine the concept of AI transparency across four levels of abstraction: as a stand-alone objective, as a governance ideal, as a governance tool, and as a ‘floating signifier’. Focusing in particular on AI transparency as a governance ideal and as a governance tool in relation to the EU’s policymaking objective of Trustworthy AI, I analyse how AI transparency has been conceptualised, designed, and implemented for two stakeholder groups — individuals and oversight bodies — within the governance frameworks of the General Data Protection Regulation (GDPR), the Digital Services Act (DSA), and the Artificial Intelligence Act (AIA).
The main argument advanced in the thesis is that while AI transparency directed at individuals (understood as data subjects, service recipients, and natural persons) remains relevant, it is the effectiveness of oversight-oriented AI transparency that is crucial to the enforcement of the EU technology regulation and is, ultimately, foundational in the EU’s pursuit of Trustworthy AI. Although transparency is central to the EU’s vision for Trustworthy AI, its effectiveness depends on how legal obligations are interpreted, implemented, and enforced in practice.
Opponent
Associate Professor Lena Enqvist

PhD Defense: Fact and Ideology in the Machine: Modelling Knowledge and Belief in Neural Models from Text
2025-09-17 09:00 - 12:00 (Europe/Stockholm) | Chalmers University of Technology
Denitsa Saynova defends her doctoral thesis, “Fact and Ideology in the Machine: Modelling Knowledge and Belief in Neural Models from Text” on September 17 at Chalmers University of Technology.
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Abstract
This thesis explores questions of knowledge, language, and neural network models. Motivated by an increasing need for insight into complex political and social science phenomena, we study how methods within natural language processing (NLP) can help us gain such insight. With a particular focus on a model’s knowledge, how it is structured, and how we can access and assess it, we study two important aspects of NLP models.
First, we investigate their capabilities and limitations, focusing on how they can capture political and social signals. We use embedding models to capture and reveal distinctions in policy and ideology in Swedish political parties, discussing the strengths and drawbacks of the approach. We also investigate the presence of more complex social knowledge in large pre-trained language models. We prompt models to produce synthetic samples of responses to social science experiments and access if effects calculated from the synthetic data can be used to predict a study’s replicability. A central limitation we find in these studies is the lack of robustness, which we explore in depth by studying what influences model consistency in a more simplified setting, namely, recalling facts.
Second, we aim to bridge the gap between the model and the domain expert by developing and improving interpretability insights of model behaviour. We develop a method for aggregating class-level explanations for a text classifier and demonstrate its utility in the context of Swedish political texts. We also develop the understanding of how models store and access factual information. We propose a taxonomy of possible language model behaviours for fact completion and, based on our novel testing data set, examine internal knowledge structures using established mechanistic interpretability methods.
See full thesis.
Supervisors
Opponent
Dirk Hovy, Professor, Bocconi University

PhD Defense: Multisensory Interactions with Biophilic Flying Robots
2025-11-10 09:00 - 12:00
Ziming Wang defends his doctoral thesis, “Multisensory Interactions with Biophilic Flying Robots” at Chalmers University of University.
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Abstract
The relationship between nature and humanity has evolved throughout history and across technological epochs. This thesis advances the hypothesis that integrating natural characteristics into robot design can enrich human–computer interaction (HCI) by drawing on our deep-rooted familiarity with and affinity for the natural world. To investigate this proposition, I examined close-range interactions with flying robots under different proxemic conditions, employing a mixed-methods approach.
The thesis comprises four empirical studies, each probing a different pathway through which biophilic elements might shape human perception, interaction, and imagination. Study I examined how overlaying natural soundscapes such as birdsong and rainfall affected the perception of a noisy flying robot (N = 56). Study II explored nature narratives, particularly the conceptualization of indoor drones as animal-like companions through function framing (N = 60). Study III compared experiences with a bioinspired flapping-wing drone—foregrounding organic forms and biomimetic movement—against a similarly sized quadcopter (N = 56). Study IV staged a speculative dinner theater in which participants (N = 6) engaged with the provocative scenario of eating a biohybrid drone, highlighting hybrid living components as a design material. Across these studies, variations in spatial proximity (from very near to relatively far) and temporal framing (from near- to far-future scenarios) were integrated to reveal how context shapes engagement and experiences.
Collectively, the findings show that nature-inspired design elements can foster intuitive, relatable, and emotionally resonant interactions with flying robots, while also surfacing ethical and practical challenges. This thesis contributes empirical insights into how people respond to biophilic flying robots and argues for moving beyond surface-level biomimicry toward intentional, context-aware integration of natural elements. By treating nature not merely as aesthetic inspiration but as a lens for crafting meaningful, embodied interactions, we can design technologies that resonate more deeply with human experience—particularly in close-range, affective, and everyday settings.
See full thesis.
Supervisor
Morten Fjeld, Professor
Pedro Cardoso-Leite, Associate Professor
Opponent
Joseph Paradiso, Professor, Massachusetts Institute of Technology (MIT), USA

PhD Defense: Mapping Small Water Channels Using Machine Learning
2025-12-05 08:30 - 12:00
Mariana Busarello defends her doctoral thesis “Mapping small water channels using machine learning” on December 5 at Swedish University of Agricultural Sciences.
Abstract
Boreal landscapes are shaped by a dense network of natural streams, modified streams, and ditches. Together, they regulate hydrology, nutrient transport, and ecosystem function. Historically, streams were modified to accommodate log transportation, and drainage ditches were dug to improve food and timber production. Although new ditching has mostly stopped, historical changes to the drainage network still affect forestry and water management. Small streams and ditches are the landscape’s capillaries, but they remain poorly mapped despite their vital hydrological and ecological roles. This thesis addresses this gap in knowledge by developing a novel, national-scale framework for mapping small streams and ditches using high-resolution topographic data and machine learning techniques. Combining convolutional neural networks, XGBoost classification, uncertainty quantification, and drainage analyses, this work identifies geomorphological and hydrological indices that distinguish streams from ditches across the landscape. The highest-performing model shows that integrating digital elevation models with terrain indices and machine learning delineates the channel networks successfully for ditches (recall=76%, precision=88%) and moderately for natural streams (recall=58%, precision=56%). Furthermore, the produced uncertainty maps highlight low-certainty pixels from the background that can be used to potentially improve the mapping of streams in the future. To the best of our knowledge, this is the first study that can separate streams and ditches on maps across an entire nation. By providing consistent, scalable maps of small channels, this research supports restoration prioritization, sustainable forestry planning, and national reporting under EU and UN environmental frameworks. The methodology also offers a reproducible approach for characterizing coupled natural-artificial drainage systems in boreal and temperate regions worldwide.
See full thesis.
Supervisor
William Lidberg, Swedish University of Agricultural Sciences
Opponent
Chris Soulsby, University of Aberdeen
