Sune Holm
Biographic Data
| ID | 3650719 |
|---|---|
| NAME | Sune Holm |
| GIVEN NAMES | Sune |
| FAMILY NAME | Holm |
| SIGNATURE | HOLM S |
| AFFILIATIONS | University of Copenhagen |
| ORCID | 0000-0002-3812-7942 |
| VERIFIED | Yes |
| TOTAL WORKS | 21 |
| TOTAL CITATIONS | 17 |
| WORKS AS AUTHOR | 21 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
Fair by chance? On the use of algorithms in therapeutic decisions
Predictive tools made possible by advances in machine learning techniques may help clinicians make more accurate decisions about who should be allocated costly therapies, such as immunotherapy, which only work on a relatively low proportion of patients. In this article, I argue that a fair decision procedure must recognise each patients’ chance of responding well. To do so, the procedure should not apply a fixed threshold to probability scores. R…
Supportive but apprehensive
We explored attitudes among Danes toward a healthcare project under development, which includes artificial intelligence, healthcare surveillance, and big data – aimed to improve the detection of vaccine side effects. Similar to other studies in the field, we found a dual attitude of overall support for the project while being apprehensive about risky elements within it. We expanded on existing literature about such technologies by framing this as…
Ethical trade-offs in AI for mental health
It is expected that machine learning algorithms will enable better diagnosis, prognosis, and treatment in psychiatry. A central argument for deploying algorithmic methods in clinical decision-making in psychiatry is that they may enable not only faster and more accurate clinical judgments but also that they may provide a more objective foundation for clinical decisions. This article argues that the outputs of algorithms are never objective in the…
Algorithmic legitimacy in clinical decision-making
Machine learning algorithms are expected to improve referral decisions. In this article I discuss the legitimacy of deferring referral decisions in primary care to recommendations from such algorithms. The standard justification for introducing algorithmic decision procedures to make referral decisions is that they are more accurate than the available practitioners. The improvement in accuracy will ensure more efficient use of scarce health resou…
On the Justified Use of AI Decision Support in Evidence-Based Medicine
When is it justified to use opaque artificial intelligence (AI) output in medical decision-making? Consideration of this question is of central importance for the responsible use of opaque machine learning (ML) models, which have been shown to produce accurate and reliable diagnoses, prognoses, and treatment suggestions in medicine. In this article, I discuss the merits of two answers to the question. According to the Explanation View, clinicians…
Egalitarianism and Algorithmic Fairness
Should People Have a Right Not to Be Subjected to AI Profiling based on Publicly Available Data? A Comment on Ploug
Several studies have documented that when presented with data from social media platforms machine learning (ML) models can make accurate predictions about users, e.g., about whether they are likely to suffer health-related conditions such as depression, mental disorders, and risk of suicide. In a recent article, Ploug (Philos Technol 36:14, 2023) defends a right not to be subjected to AI profiling based on publicly available data. In this comment…
Discrimination, Fairness, and the Use of Algorithms
Statistical evidence and algorithmic decision-making
The use of algorithms to support prediction-based decision-making is becoming commonplace in a range of domains including health, criminal justice, education, social services, lending, and hiring. An assumption governing such decisions is that there is a property Y such that individual a should be allocated resource R by decision-maker D if a is Y. When there is uncertainty about whether a is Y, algorithms may provide valuable decision support by…
Handle with care
In this article I consider two pertinent questions that practitioners must consider when they deploy an algorithmic system as support in clinical shared decision‐making. The first question concerns how to interpret and assess the significance of different performance measures for clinical decision‐making. The second question concerns the professional obligations that practitioners have to communicate information about the quality of an algorithm'…
The Fairness in Algorithmic Fairness
On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls
Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidel…
Co-Design of a Trustworthy AI System in Healthcare
This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working w…
Constitutive Relevance in Interlevel Experiments
One reason for the popularity of Craver’s mutual manipulability (MM) account of constitutive relevance is that it seems to make good sense of the experimental practices and constitutive reasoning in the life sciences. Two recent papers (Baumgartner and Gebharter [2016]; Baumgartner and Casini [2017]) propose a theoretical alternative to (MM) in light of several important conceptual objections. Their alternative approach, the no de-coupling (NDC) …
Teleology and biocentrism
Teleological organisation
Bridging bioethics and biology
Is synthetic biology mechanical biology
Organism and artifact
Organism, machine, artifact
Biological Interests, Normative Functions, and Synthetic Biology
Biological Interests, Normative Functions, and Synthetic Biology
Organism and artifact
Organism, machine, artifact
Bridging bioethics and biology
Is synthetic biology mechanical biology
Teleology and biocentrism
Teleological organisation
Constitutive Relevance in Interlevel Experiments
One reason for the popularity of Craver’s mutual manipulability (MM) account of constitutive relevance is that it seems to make good sense of the experimental practices and constitutive reasoning in the life sciences. Two recent papers (Baumgartner and Gebharter [2016]; Baumgartner and Casini [2017]) propose a theoretical alternative to (MM) in light of several important conceptual objections. Their alternative approach, the no de-coupling (NDC) …
On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls
Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidel…
Co-Design of a Trustworthy AI System in Healthcare
This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working w…
Handle with care
In this article I consider two pertinent questions that practitioners must consider when they deploy an algorithmic system as support in clinical shared decision‐making. The first question concerns how to interpret and assess the significance of different performance measures for clinical decision‐making. The second question concerns the professional obligations that practitioners have to communicate information about the quality of an algorithm'…
The Fairness in Algorithmic Fairness
Algorithmic legitimacy in clinical decision-making
Machine learning algorithms are expected to improve referral decisions. In this article I discuss the legitimacy of deferring referral decisions in primary care to recommendations from such algorithms. The standard justification for introducing algorithmic decision procedures to make referral decisions is that they are more accurate than the available practitioners. The improvement in accuracy will ensure more efficient use of scarce health resou…
On the Justified Use of AI Decision Support in Evidence-Based Medicine
When is it justified to use opaque artificial intelligence (AI) output in medical decision-making? Consideration of this question is of central importance for the responsible use of opaque machine learning (ML) models, which have been shown to produce accurate and reliable diagnoses, prognoses, and treatment suggestions in medicine. In this article, I discuss the merits of two answers to the question. According to the Explanation View, clinicians…
Egalitarianism and Algorithmic Fairness
Should People Have a Right Not to Be Subjected to AI Profiling based on Publicly Available Data? A Comment on Ploug
Several studies have documented that when presented with data from social media platforms machine learning (ML) models can make accurate predictions about users, e.g., about whether they are likely to suffer health-related conditions such as depression, mental disorders, and risk of suicide. In a recent article, Ploug (Philos Technol 36:14, 2023) defends a right not to be subjected to AI profiling based on publicly available data. In this comment…
Discrimination, Fairness, and the Use of Algorithms
Statistical evidence and algorithmic decision-making
The use of algorithms to support prediction-based decision-making is becoming commonplace in a range of domains including health, criminal justice, education, social services, lending, and hiring. An assumption governing such decisions is that there is a property Y such that individual a should be allocated resource R by decision-maker D if a is Y. When there is uncertainty about whether a is Y, algorithms may provide valuable decision support by…
Ethical trade-offs in AI for mental health
It is expected that machine learning algorithms will enable better diagnosis, prognosis, and treatment in psychiatry. A central argument for deploying algorithmic methods in clinical decision-making in psychiatry is that they may enable not only faster and more accurate clinical judgments but also that they may provide a more objective foundation for clinical decisions. This article argues that the outputs of algorithms are never objective in the…
Supportive but apprehensive
We explored attitudes among Danes toward a healthcare project under development, which includes artificial intelligence, healthcare surveillance, and big data – aimed to improve the detection of vaccine side effects. Similar to other studies in the field, we found a dual attitude of overall support for the project while being apprehensive about risky elements within it. We expanded on existing literature about such technologies by framing this as…
Fair by chance? On the use of algorithms in therapeutic decisions
Predictive tools made possible by advances in machine learning techniques may help clinicians make more accurate decisions about who should be allocated costly therapies, such as immunotherapy, which only work on a relatively low proportion of patients. In this article, I argue that a fair decision procedure must recognise each patients’ chance of responding well. To do so, the procedure should not apply a fixed threshold to probability scores. R…
Computer Science (16 works) · Epistemology (14 works) · Philosophy (13 works) · Psychology (10 works) · Ethics and Social Impacts of AI (9 works) · Political science (9 works) · Artificial Intelligence in Healthcare and Education (8 works) · Law (8 works) · Artificial Intelligence (7 works) · Medicine (7 works)