Abstract

The financial services industry is experiencing a significant shift with the rise of blockchain technology and decentralized finance (DeFi), which challenges the industry’s high-cost centralized control approach. Distributed Ledger Technology (DLT), including permissionless blockchain and its novel organizational form, Decentralized Autonomous Organizations (DAOs), offer transformative potential through transparent, efficient, and decentralized financial transactions and governance structures. However, integrating these technologies into regulated financial services poses challenges due to the need to reconcile blockchain and DAOs’ decentralized nature with stringent regulatory requirements.

This dissertation, developed over a three-year PhD program, consolidates six publications into a comprehensive assessment framework for determining when DAOs and DeFi protocols are suitable for regulated financial services. It explores the effective implementation of DLT in regulated environments to reduce costs and enhance operational efficiencies. The research categorizes the publications by financial services domain, using prototyping and analysis to balance decentralization ideals with regulatory requirements. The dissertation identifies key properties of blockchain and DAOs, such as transparency, immutability, and decentralization, assessing their applicability within traditional financial systems. It offers practical tools for evaluating the suitability and compliance of DAOs in regulated financial services, mapping DAOs to organizational design theory, and highlighting their potential to reduce transaction costs and enhance trust.

Using Design Science Research (DSR) methodologies, the dissertation develops robust frameworks, applications, and taxonomies through qualitative data collection, thematic analysis, and formal methods. It provides empirical insights and tools for practitioners, including frameworks for assessing DAO governance structures and regulatory compliance. The dissertation concludes that while DAOs and DeFi protocols show promise, their application in regulated financial markets remains challenging due to regulatory complexities arising from centralized control preferences and norms.

The dissertation contributes to Information Systems (IS) research by presenting an evolution of DAOs over time and proposing an evaluation framework to assess and enhance the understanding of blockchain technology and DAOs in regulated financial services. Further, the individual publications contribute to their respective field by presenting applicable building blocks for financial services and domain-specific use cases for the DAO ecosystem.

The research provides insights and tools that deepen the understanding of DAOs’ potential and limitations in digital finance, offering a foundation for future research and practical applications. By incorporating law, economics, organizational theory, and IS perspectives, the dissertation highlights the need for new regulatory frameworks to accommodate DAOs and DLT. It suggests that while DAOs and DeFi protocols can reduce transaction costs and enhance transparency, their application in regulated finance is limited by compliance challenges and the need for complete decentralization.

The analysis suggests that ‘sufficient decentralization’ is a mirage and that complete decentralization on all three dimensions – technically, organizationally, and in service availability to the public – is the only viable option for DeFi in regulated financial markets, using the EU’s recent regulation for digital finance. While we have yet to see whether the new EU regulation and this high bar for decentralization will mean the end for DeFi and financial services DAOs in the EU, blockchain technology appears generally beneficial for finance involving untrusted parties, where intermediaries usually fulfill this role at a very high socioeconomic cost.

Supervisors

Principal Supervisor Omry Ross
Co-Supervisor Friedrich Henglein

Assessment Committee

Associate Professor Tijs Slaats, Computer Science
Professor Juho Lindman, University of Gothenburg
Assistant Professor Nina-Birte Schirrmacher, VU Amsterdam

See event here

Abstract

In recent years, robots have evolved from facilitating rigid automation in facto-ries to being present in our daily lives, appearing in places like hospitals, homes, and shops. As we move closer to a future where robots could be as common as smartphones, questions about how they will fit in our world arise. How do we enable their navigating in shared spaces without causing discom-fort or danger, and how do we ensure their acceptance?

This thesis explores these questions by focusing on two key aspects: Situational Awareness and Acceptability. For robots to function safely and be accepted in environments where they interact closely with people, they must not only avoid obstacles but also recignise social cues, respect personal space, and be-have in ways that humans find predictable and comfortable.

It includes new methods that allow robots to model context in social environ-ments: recognising people’s actions or gestures, detecting groups, and navi-gating without interrupting interactions. These methods enhance the robot’s situational awareness and socially acceptable navigation.

This work considers the ethical aspects of using robots in sensitive environ-mnents like care homes. As robots enter the lives of vulnerable populations, it is important to consider the emotional and ethical effects of their presence.

Moreover, it delves into the challenges encountered as the density of robots in-creases. Planning methods and adaptations, which take into consideration the uncertainties of the real world, are suggested, which, when adapted, will ena-ble robust and dependable navigation in real applications.

Ultimately, this thesis brings us closer to a future where robots move beyond structured and confined spaces to become a part of our everyday lives, as commonplace as any other modern technology.

Chairperson

Associate Professor Aljaz Kramberger, SDU Robotics,
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark

Supervisor

Associate Professor Leon Bodenhagen, SDU Robotics,
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark

Evaluation Committee

• Associate Professor Simon Faarvang Mathiesen (chair) SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark
• Professor Ole Madsen The Faculty of Engineering and Science, Aalborg University
• Professor Elizabeth Broadbent The University of Auckland

Abstract

In machine learning, researchers often rely on standard datasets to test and compare different methods. These datasets are like controlled experiments where conditions are kept consistent. However, my thesis takes a different approach by focusing on real-world applications, specifically detecting diseases and damages in grain kernels using images. This task is particularly challenging due to the natural variations in biological data, which are quite different from the controlled conditions of standard datasets. 

The main goal of my research is to improve how we detect diseases in grain kernels and make the process more transparent and understandable. To do this, I explore the use of knowledge graphs. Think of a knowledge graph as a network of information where different pieces of data are connected, much like a mind map. This can help enhance image classification (identifying what an image represents) and create specific data collections tailored to our needs. 

One of the significant challenges I tackle is applying explainability methods to data with biological variations. Explainability methods are techniques used to make AI models’ decisions more understandable to humans. I propose a workflow, which is a step-by-step guide, to help choose the best method for any given situation and test how well these methods handle minor, natural changes in the images. 

Finally, I delve into making machine learning models more understandable by linking their operations to high-level concepts. High-level concepts are broad ideas that humans can easily grasp, like “color” or “shape.” I also work on aligning the model’s information processing with human thinking, meaning I try to make the AI think in ways that are similar to how humans think. This could provide valuable insights into making AI models more aligned with how humans understand and interpret information.

Supervisors

Professor Lars Kai Hansen, DTU Compute
Professor Kim Steenstrup Pederesn, University of Copenhagen

Examiners

Associate Professor Michael Riis Andersen, DTU Computer
Professor Stefan Haufe, TU Berlin
Professor Michael Kampffmeyer, UiT The Artic University of Norway

Chair of defence

Associate Professor Tobias Søren Andersen

Everyone is welcome. 

Reception will be held in building 322, room 232 after the defence

A digital version of the PhD thesis can be obtained from the PhD School at phdschool@compute.dtu.dk up until the time of the defence.