Giorgia Pozzi
Biographic Data
| ID | 4620399 |
|---|---|
| NAME | Giorgia Pozzi |
| GIVEN NAMES | Giorgia |
| FAMILY NAME | Pozzi |
| SIGNATURE | POZZI G |
| AFFILIATIONS | Delft University of Technology |
| ORCID | 0000-0001-8928-5513 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Harmful epistemic dependence on medical machine learning and its moral implications
The advances in machine learning (ML)-based systems in medicine give rise to pressing epistemological and ethical questions. Clinical decisions are increasingly taken in highly digitised work environments, which we call artificial epistemic niches. By considering the case of ML systems in life-critical healthcare settings, we investigate (1) when users’ reliance on these systems can be characterised as epistemic dependence and (2) how this depend…
From ethics to epistemology and back again
In this paper, we discuss epistemic and ethical concerns brought about by machine learning (ML) systems implemented in medicine. We begin by fleshing out the logic underlying a common approach in the specialized literature (which we call the informativeness account ). We maintain that the informativeness account limits its analysis to the impact of epistemological issues on ethical concerns without assessing the bearings that ethical features hav…
Trust and Trustworthiness in AI
Achieving trustworthy AI is increasingly considered an essential desideratum to integrate AI systems into sensitive societal fields, such as criminal justice, finance, medicine, and healthcare, among others. For this reason, it is important to spell out clearly its characteristics, merits, and shortcomings. This article is the first survey in the specialized literature that maps out the philosophical landscape surrounding trust and trustworthines…
Machine learning for mental health diagnosis
Why we should talk about institutional (dis)trustworthiness and medical machine learning
Automated opioid risk scores
Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I…
Testimonial injustice in medical machine learning
Machine learning (ML) systems play an increasingly relevant role in medicine and healthcare. As their applications move ever closer to patient care and cure in clinical settings, ethical concerns about the responsibility of their use come to the fore. I analyse an aspect of responsible ML use that bears not only an ethical but also a significant epistemic dimension. I focus on ML systems’ role in mediating patient–physician relations. I thereby c…
Further remarks on testimonial injustice in medical machine learning
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Automated opioid risk scores
Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I…
Testimonial injustice in medical machine learning
Machine learning (ML) systems play an increasingly relevant role in medicine and healthcare. As their applications move ever closer to patient care and cure in clinical settings, ethical concerns about the responsibility of their use come to the fore. I analyse an aspect of responsible ML use that bears not only an ethical but also a significant epistemic dimension. I focus on ML systems’ role in mediating patient–physician relations. I thereby c…
Further remarks on testimonial injustice in medical machine learning
Machine learning for mental health diagnosis
Why we should talk about institutional (dis)trustworthiness and medical machine learning
From ethics to epistemology and back again
In this paper, we discuss epistemic and ethical concerns brought about by machine learning (ML) systems implemented in medicine. We begin by fleshing out the logic underlying a common approach in the specialized literature (which we call the informativeness account ). We maintain that the informativeness account limits its analysis to the impact of epistemological issues on ethical concerns without assessing the bearings that ethical features hav…
Trust and Trustworthiness in AI
Achieving trustworthy AI is increasingly considered an essential desideratum to integrate AI systems into sensitive societal fields, such as criminal justice, finance, medicine, and healthcare, among others. For this reason, it is important to spell out clearly its characteristics, merits, and shortcomings. This article is the first survey in the specialized literature that maps out the philosophical landscape surrounding trust and trustworthines…
Harmful epistemic dependence on medical machine learning and its moral implications
The advances in machine learning (ML)-based systems in medicine give rise to pressing epistemological and ethical questions. Clinical decisions are increasingly taken in highly digitised work environments, which we call artificial epistemic niches. By considering the case of ML systems in life-critical healthcare settings, we investigate (1) when users’ reliance on these systems can be characterised as epistemic dependence and (2) how this depend…
Artificial Intelligence in Healthcare and Education (7 works) · Psychology (7 works) · Epistemology (6 works) · Ethics in Clinical Research (6 works) · Philosophy (6 works) · Social Psychology (6 works) · Ethics and Social Impacts of AI (5 works) · Injustice (5 works) · Law (5 works) · Political science (5 works)