Thomas Grote
Biographic Data
| ID | 1292376 |
|---|---|
| NAME | Thomas Grote |
| GIVEN NAMES | Thomas |
| FAMILY NAME | Grote |
| SIGNATURE | GROTE T |
| AFFILIATIONS | University of Tübingen |
| ORCID | 0000-0002-9832-6046 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 4 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Machine learning in healthcare and the methodological priority of epistemology over ethics
This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the gr…
A Minimalist Account of the Right To Explanation
Critiques of opaque machine learning models, used to guide consequential decisions, are gaining traction in moral philosophy. They are said to undermine informed self-advocacy, violate duties of consideration to decision-subjects, or reinforce existing power structures, among other things. Even though motivated by different normative frameworks, the received view is that the appropriate amelioration strategy is to make opaque machine learning mod…
A paradigm shift?—On the ethics of medical large language models
After a wave of breakthroughs in image‐based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. However, prominent researchers are claiming that another paradigm shift in medical ML is imminent—due to most recent staggering successes of large language models—from single‐purpose applications toward generalist models, driven by natural language. This article investigates the implications of this …
Computational psychiatry and the evolving concept of a mental disorder
As a discipline, psychiatry is in the process of finding the right set of concepts to organize research and guide treatment. Dissatisfaction with the status quo as expressed in standard manuals has animated a number of computational paradigms, each proposing to rectify the received concept of mental disorder. We explore how different computational paradigms: normative modeling, network theory and learning-theoretic approaches like reinforcement l…
Reliability in Machine Learning
Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning - as far as they are concerned with reliability
Beyond generalization
The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what exactly they mean by robustness. In this paper, we provide a conceptual analysis of the term robustness , with the aim to develop a common language, …
Predicting and explaining with machine learning models
Enabling Fairness in Healthcare Through Machine Learning
The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algor…
On Algorithmic Fairness in Medical Practice
The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear th…
Randomised controlled trials in medical AI
In recent years, there has been a surge of high-profile publications on applications of artificial intelligence (AI) systems for medical diagnosis and prognosis. While AI provides various opportunities for medical practice, there is an emerging consensus that the existing studies show considerable deficits and are unable to establish the clinical benefit of AI systems. Hence, the view that the clinical benefit of AI systems needs to be studied in…
Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions
This paper argues that machine learning (ML) and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal kn…
Trustworthy medical AI systems need to know when they don’t know
There is much to learn from Duran and Jongsma’s paper.1 One particularly important insight concerns the relationship between epistemology and ethics in medical artificial intelligence (AI). In clinical environments, the task of AI systems is to provide risk estimates or diagnostic decisions, which then need to be weighed by physicians. Hence, while the implementation of AI systems might give rise to ethical issues—for example, overtreatment, defe…
On the ethics of algorithmic decision-making in healthcare
In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institut…
Computational psychiatry and the evolving concept of a mental disorder
As a discipline, psychiatry is in the process of finding the right set of concepts to organize research and guide treatment. Dissatisfaction with the status quo as expressed in standard manuals has animated a number of computational paradigms, each proposing to rectify the received concept of mental disorder. We explore how different computational paradigms: normative modeling, network theory and learning-theoretic approaches like reinforcement l…
Reliability in Machine Learning
Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning - as far as they are concerned with reliability
Beyond generalization
The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what exactly they mean by robustness. In this paper, we provide a conceptual analysis of the term robustness , with the aim to develop a common language, …
Predicting and explaining with machine learning models
On the ethics of algorithmic decision-making in healthcare
In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institut…
Trustworthy medical AI systems need to know when they don’t know
There is much to learn from Duran and Jongsma’s paper.1 One particularly important insight concerns the relationship between epistemology and ethics in medical artificial intelligence (AI). In clinical environments, the task of AI systems is to provide risk estimates or diagnostic decisions, which then need to be weighed by physicians. Hence, while the implementation of AI systems might give rise to ethical issues—for example, overtreatment, defe…
Enabling Fairness in Healthcare Through Machine Learning
The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algor…
On Algorithmic Fairness in Medical Practice
The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear th…
Randomised controlled trials in medical AI
In recent years, there has been a surge of high-profile publications on applications of artificial intelligence (AI) systems for medical diagnosis and prognosis. While AI provides various opportunities for medical practice, there is an emerging consensus that the existing studies show considerable deficits and are unable to establish the clinical benefit of AI systems. Hence, the view that the clinical benefit of AI systems needs to be studied in…
Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions
This paper argues that machine learning (ML) and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal kn…
Beyond generalization
The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what exactly they mean by robustness. In this paper, we provide a conceptual analysis of the term robustness , with the aim to develop a common language, …
Predicting and explaining with machine learning models
A paradigm shift?—On the ethics of medical large language models
After a wave of breakthroughs in image‐based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. However, prominent researchers are claiming that another paradigm shift in medical ML is imminent—due to most recent staggering successes of large language models—from single‐purpose applications toward generalist models, driven by natural language. This article investigates the implications of this …
Computational psychiatry and the evolving concept of a mental disorder
As a discipline, psychiatry is in the process of finding the right set of concepts to organize research and guide treatment. Dissatisfaction with the status quo as expressed in standard manuals has animated a number of computational paradigms, each proposing to rectify the received concept of mental disorder. We explore how different computational paradigms: normative modeling, network theory and learning-theoretic approaches like reinforcement l…
Reliability in Machine Learning
Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning - as far as they are concerned with reliability
Machine learning in healthcare and the methodological priority of epistemology over ethics
This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the gr…
A Minimalist Account of the Right To Explanation
Critiques of opaque machine learning models, used to guide consequential decisions, are gaining traction in moral philosophy. They are said to undermine informed self-advocacy, violate duties of consideration to decision-subjects, or reinforce existing power structures, among other things. Even though motivated by different normative frameworks, the received view is that the appropriate amelioration strategy is to make opaque machine learning mod…
Computer Science (10 works) · Artificial Intelligence in Healthcare and Education (8 works) · Psychology (8 works) · Artificial Intelligence (7 works) · Epistemology (6 works) · Ethics in Clinical Research (6 works) · Healthcare cost, quality, practices (5 works) · Law (5 works) · Machine learning (5 works) · Political science (5 works)