Roel Dobbe
Biographic Data
| ID | 6505889 |
|---|---|
| NAME | Roel Dobbe |
| GIVEN NAMES | Roel |
| FAMILY NAME | Dobbe |
| SIGNATURE | DOBBE R |
| AFFILIATIONS | Delft University of Technology |
| ORCID | 0000-0003-4633-7023 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
The Many Faces of Indeterminacy in Interactive Deadbots
(IDBs) are presented as hyper-realistic avatars that use a person's likeness, voice, and personal data to simulate conversational interactions with them. Rapidly moving from a niche experiment to a mainstream phenomenon, IDBs are poised to reshape the ethical, social, legal, and governance landscapes surrounding death, mourning, and digital legacy. This paper examines the disruptive nature of IDB technology through a multidisciplinary lens, using…
The power of assumptions
Recent energy justice scholarship has argued for the need to reflect more explicitly on the normative assumptions that underpin claims to justice in energy systems. While such reflections increasingly inform energy policy, less attention has been paid to how these assumptions shape the design of algorithmic systems central to energy system planning and operations. This paper explores how normative assumptions in the design of algorithmic systems …
Adapting to limited grid capacity
As renewable energy and electrification expand rapidly, many electrical distribution grids experience grid congestion . This situation leads to long waiting lists for parties seeking a new grid connection or aiming to expand their existing grid connection. In addition to traditional grid enforcements, distribution system operators are developing ways to manage congestion by steering electricity supply and demand. As grid congestion limits the pre…
Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback
This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical cri…
Tracing Class and Capital in Critical AI Research
This article explores the rapidly developing field of Critical AI Studies and its relation to issues of class and capitalism through a hybrid approach based on distant reading of a newly collected corpus of 300 full-text scientific articles, the creation of which is itself a first attempt at properly delineating the field. We find that words related to issues of class are predominantly but not exclusively confined to a set of studies that make up…
Integral system safety for machine learning in the public sector
This paper introduces systems theory and system safety concepts to ongoing academic debates about the safety of Machine Learning (ML) systems in the public sector. In particular, we analyze the risk factors of ML systems and their respective institutional context, which impact the ability to control such systems. We use interview data to abductively show what risk factors of such systems are present in public professionals' perceptions and what f…
Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback
This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical cri…
Integral system safety for machine learning in the public sector
This paper introduces systems theory and system safety concepts to ongoing academic debates about the safety of Machine Learning (ML) systems in the public sector. In particular, we analyze the risk factors of ML systems and their respective institutional context, which impact the ability to control such systems. We use interview data to abductively show what risk factors of such systems are present in public professionals' perceptions and what f…
Tracing Class and Capital in Critical AI Research
This article explores the rapidly developing field of Critical AI Studies and its relation to issues of class and capitalism through a hybrid approach based on distant reading of a newly collected corpus of 300 full-text scientific articles, the creation of which is itself a first attempt at properly delineating the field. We find that words related to issues of class are predominantly but not exclusively confined to a set of studies that make up…
Integral system safety for machine learning in the public sector
This paper introduces systems theory and system safety concepts to ongoing academic debates about the safety of Machine Learning (ML) systems in the public sector. In particular, we analyze the risk factors of ML systems and their respective institutional context, which impact the ability to control such systems. We use interview data to abductively show what risk factors of such systems are present in public professionals' perceptions and what f…
Adapting to limited grid capacity
As renewable energy and electrification expand rapidly, many electrical distribution grids experience grid congestion . This situation leads to long waiting lists for parties seeking a new grid connection or aiming to expand their existing grid connection. In addition to traditional grid enforcements, distribution system operators are developing ways to manage congestion by steering electricity supply and demand. As grid congestion limits the pre…
Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback
This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical cri…
The Many Faces of Indeterminacy in Interactive Deadbots
(IDBs) are presented as hyper-realistic avatars that use a person's likeness, voice, and personal data to simulate conversational interactions with them. Rapidly moving from a niche experiment to a mainstream phenomenon, IDBs are poised to reshape the ethical, social, legal, and governance landscapes surrounding death, mourning, and digital legacy. This paper examines the disruptive nature of IDB technology through a multidisciplinary lens, using…
The power of assumptions
Recent energy justice scholarship has argued for the need to reflect more explicitly on the normative assumptions that underpin claims to justice in energy systems. While such reflections increasingly inform energy policy, less attention has been paid to how these assumptions shape the design of algorithmic systems central to energy system planning and operations. This paper explores how normative assumptions in the design of algorithmic systems …
Computer Science (4 works) · Artificial Intelligence (2 works) · Artificial Intelligence in Healthcare and Education (2 works) · Business (2 works) · Economics (2 works) · Ethics and Social Impacts of AI (2 works) · Injustice (2 works) · Knowledge management (2 works) · Law (2 works) · Normative (2 works)