Jens Christian Bjerring
Datos Biográficos
| ID | 296589 |
|---|---|
| NOMBRE | Jens Christian Bjerring |
| NOMBRES | Jens Christian |
| APELLIDO | Bjerring |
| FIRMA | BJERRING J C |
| AFILIACIONES | Aarhus University |
| ORCID | 0000-0001-8755-6746 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 13 |
| TOTAL DE CITAS | 19 |
| TOTAL COMO AUTOR | 13 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2013 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 3 |
In Defense of Post Hoc Explanations in Medical AI
Since the early days of the explainable artificial intelligence movement, post hoc explanations have been praised for their potential to improve user understanding, promote trust, and reduce patient-safety risks in black box medical AI systems. Recently, however, critics have argued that the benefits of post hoc explanations are greatly exaggerated since they merely approximate, rather than replicate, the actual reasoning processes that black box…
Can large language models help solve the cost problem for the right to explanation
By now a consensus has emerged that people, when subjected to high-stakes decisions through automated decision systems, have a moral right to have these decisions explained to them. However, furnishing such explanations can be costly. So the right to an explanation creates what we call the cost problem: providing subjects of automated decisions with appropriate explanations of the grounds of these decisions can be costly for the companies and org…
Artificial intelligence and identity
Algorithms are used across a wide range of societal sectors such as banking, administration, and healthcare to make predictions that impact on our lives. While the predictions can be incredibly accurate about our present and future behavior, there is an important question about how these algorithms in fact represent human identity. In this paper, we explore this question and argue that machine learning algorithms represent human identity in terms…
Silencing in data science practices
This article examines the relationship between data science practices and epistemic injustice, with a particular focus on the phenomenon of silencing . Our practice-oriented analysis of the data science pipeline – data collection, cleaning, model training and implementation – reveals a vicious cycle of silencing that perpetuates and amplifies existing biases. We demonstrate how initial biases in data collection can lead to the development of mode…
Algorithmic decision-making
The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a d…
The value of responsibility gaps in algorithmic decision-making
Fragmentation, metalinguistic ignorance, and logical omniscience
Bayesianism for Non-ideal Agents
Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience : the assumption that an agent’s degrees of belief must be probabilistically coherent to be rational. It is widely agreed that this assumption is problematic if we want to reason about bounded rationality, logical learning, or other aspects of non-ideal epistemic agency. Yet, …
Artificial Intelligence and Patient-Centered Decision-Making
Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often …
Hyperintensional semantics
Granularity Problems
Possible-worlds accounts of mental or linguistic content are often criticized for being too coarse-grained. To make room for more fine-grained distinctions among contents, several authors have recently proposed extending the space of possible worlds by ‘impossible worlds’. We argue that this strategy comes with serious costs: we would effectively have to abandon most of the features that make the possible-worlds framework attractive. More general…
On the rationality of pluralistic ignorance
Impossible worlds and logical omniscience
Granularity Problems
Possible-worlds accounts of mental or linguistic content are often criticized for being too coarse-grained. To make room for more fine-grained distinctions among contents, several authors have recently proposed extending the space of possible worlds by ‘impossible worlds’. We argue that this strategy comes with serious costs: we would effectively have to abandon most of the features that make the possible-worlds framework attractive. More general…
On the rationality of pluralistic ignorance
Impossible worlds and logical omniscience
Hyperintensional semantics
Impossible worlds and logical omniscience
On the rationality of pluralistic ignorance
Granularity Problems
Possible-worlds accounts of mental or linguistic content are often criticized for being too coarse-grained. To make room for more fine-grained distinctions among contents, several authors have recently proposed extending the space of possible worlds by ‘impossible worlds’. We argue that this strategy comes with serious costs: we would effectively have to abandon most of the features that make the possible-worlds framework attractive. More general…
Hyperintensional semantics
Artificial Intelligence and Patient-Centered Decision-Making
Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often …
Bayesianism for Non-ideal Agents
Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience : the assumption that an agent’s degrees of belief must be probabilistically coherent to be rational. It is widely agreed that this assumption is problematic if we want to reason about bounded rationality, logical learning, or other aspects of non-ideal epistemic agency. Yet, …
The value of responsibility gaps in algorithmic decision-making
Fragmentation, metalinguistic ignorance, and logical omniscience
Algorithmic decision-making
The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a d…
Can large language models help solve the cost problem for the right to explanation
By now a consensus has emerged that people, when subjected to high-stakes decisions through automated decision systems, have a moral right to have these decisions explained to them. However, furnishing such explanations can be costly. So the right to an explanation creates what we call the cost problem: providing subjects of automated decisions with appropriate explanations of the grounds of these decisions can be costly for the companies and org…
Artificial intelligence and identity
Algorithms are used across a wide range of societal sectors such as banking, administration, and healthcare to make predictions that impact on our lives. While the predictions can be incredibly accurate about our present and future behavior, there is an important question about how these algorithms in fact represent human identity. In this paper, we explore this question and argue that machine learning algorithms represent human identity in terms…
Silencing in data science practices
This article examines the relationship between data science practices and epistemic injustice, with a particular focus on the phenomenon of silencing . Our practice-oriented analysis of the data science pipeline – data collection, cleaning, model training and implementation – reveals a vicious cycle of silencing that perpetuates and amplifies existing biases. We demonstrate how initial biases in data collection can lead to the development of mode…
In Defense of Post Hoc Explanations in Medical AI
Since the early days of the explainable artificial intelligence movement, post hoc explanations have been praised for their potential to improve user understanding, promote trust, and reduce patient-safety risks in black box medical AI systems. Recently, however, critics have argued that the benefits of post hoc explanations are greatly exaggerated since they merely approximate, rather than replicate, the actual reasoning processes that black box…
Computer Science (10 obras) · Philosophy (9 obras) · Epistemology (8 obras) · Ethics and Social Impacts of AI (6 obras) · Artificial Intelligence in Healthcare and Education (5 obras) · Explainable Artificial Intelligence (XAI (5 obras) · Political science (5 obras) · Sociology (5 obras) · Logic, Reasoning, and Knowledge (4 obras) · Metaphysics (4 obras)