Kristian González Barman
Biographic Data
| ID | 1292556 |
|---|---|
| NAME | Kristian González Barman |
| GIVEN NAMES | Kristian González |
| FAMILY NAME | Barman |
| SIGNATURE | BARMAN K G |
| AFFILIATIONS | Ghent University |
| ORCID | 0000-0001-7277-7351 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 11 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Reframing the responsibility gap in medical artificial intelligence: Insights from causal selection and authorship attribution
The increasing use of AI in healthcare has sparked debates about responsibility and accountability for AI-related errors. The difficulty in attributing moral responsibility for undesirable outcomes caused by increasingly autonomous (often opaque) AI systems has become a new focal point in the debate on ‘responsibility gaps’. We approach the problem of these gaps by offering a framework that combines causal selection principles from the philosophy…
Dating Apps and the Right to an Explanation
This article argues that in countries where dating apps have become the primary means of meeting romantic partners and promise to help users find love, individuals should be entitled to access certain information about how their algorithms function. Specifically, we advocate for a legal right to an explanation that addresses the following, not necessarily exhaustive, questions: (i) Is a given dating app, x , designed to gratuitously prolong users…
Is Automated Discovery Expanding Human Understanding? On the Role of Machine Learning in the Increase of Scientific Understanding
Reinforcement Learning from Human Feedback in LLMs: Whose Culture, Whose Values, Whose Perspectives
We argue for the epistemic and ethical advantages of pluralism in Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLMs). Drawing on social epistemology and pluralist philosophy of science, we suggest ways in which RHLF can be made more responsive to human needs and how we can address challenges along the way. The paper concludes with an agenda for change, i.e. concrete, actionable steps to improve LLM de…
Fortifying Trust: Can Computational Reliabilism Overcome Adversarial Attacks
Distinctively Mathematical Explanations of Game Outcomes
The debate on distinctively mathematical explanations relies heavily on examples such as strawberry distribution and bridge crossing.I argue that 'task-based' examples do not explain physical facts; rather, they explain outcomes of abstract games that follow predefined rules.The impossibility of these tasks stems from obeying the games' rules.Modality holds due to the way the explanandum is formulated, rather than due to nomological or mathematic…
Beyond transparency and explainability: On the need for adequate and contextualized user guidelines for LLM use
Distinctively generic explanations of physical facts
Accident Causation Models: The Good the Bad and the Ugly
The main aim of this paper is to evaluate the evolution of Accident Causation Models (ACMs) from the perspective of philosophy of science. I use insights from philosophy of science to provide an epistemological analysis of the ways in which engineering scientists judge the value of different types of ACMs and to offer normative reflection on these judgements. I review three widespread ACMs and clarify their epistemic value: sequential models, epi…
Quantum mechanical atom models, legitimate explanations and mechanisms
Thinking about laws in political science (and beyond)
There are several theses in political science that are usually explicitly called ‘laws’. Other theses are generally thought of as laws, but often without being explicitly labelled as such. Still other claims are well‐supported and arguably interesting, while no one would be tempted to call them laws. This situation raises philosophical questions: which theses deserve to be called laws and which not? And how should we decide about this? In this pa…
Distinctively generic explanations of physical facts
Thinking about laws in political science (and beyond)
There are several theses in political science that are usually explicitly called ‘laws’. Other theses are generally thought of as laws, but often without being explicitly labelled as such. Still other claims are well‐supported and arguably interesting, while no one would be tempted to call them laws. This situation raises philosophical questions: which theses deserve to be called laws and which not? And how should we decide about this? In this pa…
Quantum mechanical atom models, legitimate explanations and mechanisms
Thinking about laws in political science (and beyond)
There are several theses in political science that are usually explicitly called ‘laws’. Other theses are generally thought of as laws, but often without being explicitly labelled as such. Still other claims are well‐supported and arguably interesting, while no one would be tempted to call them laws. This situation raises philosophical questions: which theses deserve to be called laws and which not? And how should we decide about this? In this pa…
Accident Causation Models: The Good the Bad and the Ugly
The main aim of this paper is to evaluate the evolution of Accident Causation Models (ACMs) from the perspective of philosophy of science. I use insights from philosophy of science to provide an epistemological analysis of the ways in which engineering scientists judge the value of different types of ACMs and to offer normative reflection on these judgements. I review three widespread ACMs and clarify their epistemic value: sequential models, epi…
Beyond transparency and explainability: On the need for adequate and contextualized user guidelines for LLM use
Distinctively generic explanations of physical facts
Is Automated Discovery Expanding Human Understanding? On the Role of Machine Learning in the Increase of Scientific Understanding
Reinforcement Learning from Human Feedback in LLMs: Whose Culture, Whose Values, Whose Perspectives
We argue for the epistemic and ethical advantages of pluralism in Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLMs). Drawing on social epistemology and pluralist philosophy of science, we suggest ways in which RHLF can be made more responsive to human needs and how we can address challenges along the way. The paper concludes with an agenda for change, i.e. concrete, actionable steps to improve LLM de…
Fortifying Trust: Can Computational Reliabilism Overcome Adversarial Attacks
Distinctively Mathematical Explanations of Game Outcomes
The debate on distinctively mathematical explanations relies heavily on examples such as strawberry distribution and bridge crossing.I argue that 'task-based' examples do not explain physical facts; rather, they explain outcomes of abstract games that follow predefined rules.The impossibility of these tasks stems from obeying the games' rules.Modality holds due to the way the explanandum is formulated, rather than due to nomological or mathematic…
Reframing the responsibility gap in medical artificial intelligence: Insights from causal selection and authorship attribution
The increasing use of AI in healthcare has sparked debates about responsibility and accountability for AI-related errors. The difficulty in attributing moral responsibility for undesirable outcomes caused by increasingly autonomous (often opaque) AI systems has become a new focal point in the debate on ‘responsibility gaps’. We approach the problem of these gaps by offering a framework that combines causal selection principles from the philosophy…
Dating Apps and the Right to an Explanation
This article argues that in countries where dating apps have become the primary means of meeting romantic partners and promise to help users find love, individuals should be entitled to access certain information about how their algorithms function. Specifically, we advocate for a legal right to an explanation that addresses the following, not necessarily exhaustive, questions: (i) Is a given dating app, x , designed to gratuitously prolong users…
Computer Science (9 works) · Epistemology (9 works) · Philosophy (8 works) · Philosophy of science (7 works) · Psychology (5 works) · Sociology (4 works) · Artificial Intelligence (3 works) · Philosophy and History of Science (3 works) · Philosophy of technology (3 works) · Political science (3 works)