In this paper, we discuss the processes of racialisation on the example of biomedical research. We argue that applying the concept of racialisation in biomedical research can be much more precise, informative and suitable than currently used categories, such as race and ethnicity. For this purpose, we construct a model of the different processes affecting and co-shaping the racialisation of an individual, and consider these in relation to biomedical research, particularly to studies on hypertension. We finish with a discussion on (...) the potential application of our proposition to institutional guidelines on the use of racial categories in biomedical research. (shrink)

Philosophical Issues, Volume 31, Issue 1, Page 355-373, October 2021.

Smartphone apps might offer a low-threshold approach to the detection of mental health conditions, such as depression. Based on the gathering of ‘passive data,’ some apps generate a user’s ‘digital phenotype,’ compare it to those of users with clinically confirmed depression and issue a warning if a depressive episode is likely. These apps can, thus, serve as epistemic tools for affected users. From an ethical perspective, it is crucial to consider epistemic injustice to promote socially responsible innovations within digital mental (...) healthcare. In cases of epistemic injustice, people are wronged specifically as epistemic agents, i.e., agents of the production and distribution of knowledge. We suggest that epistemic agency relies on different resource- and uptake-related preconditions which can be impacted by the functionality of passive self-tracking apps. We consider how this can lead to different forms of epistemic injustice (testimonial, hermeneutical, and contributory injustice) and analyze the influence of the apps’ use on epistemic practices on an individual level, in mental healthcare settings, and on the structural level. (shrink)

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 grounds for many ethical problems that currently claim centre-stage in the bioethical debate. Accordingly, this paper argues that the best way (...) to make progress in remedying these ethical problems is to distil their epistemic core and to identify appropriate epistemic norms as guardrails. The viability of this approach will be highlighted based on four key issues: trust, responsibility, paternalism, and fairness. In that respect, the paper also contributes to a more pronounced view of the specific methodological challenges for ethical theorizing, when the point of reference are complex statistical models that make predictions, rather than individual agents, acting for reasons. (shrink)

In this paper we, an epistemologist and a machine learning scientist, argue that we need to pursue a novel area of philosophical research in AI – the ethics of belief for AI. Here we take the ethics of belief to refer to a field at the intersection of epistemology and ethics concerned with possible moral, practical, and other non-truth-related dimensions of belief. In this paper we will primarily be concerned with the normative question within the ethics of belief regarding what (...) agents – both human and artificial – ought to believe, rather than with questions concerning whether beliefs meet certain evaluative standards such as being true, being justified, constituting knowledge, etc. We suggest four topics in extant work in the ethics of (human) belief that can be applied to an ethics of AI belief: doxastic wronging by AI (morally wronging someone in virtue of beliefs held about them); morally owed beliefs (beliefs that agents are morally obligated to hold); pragmatic and moral encroachment (cases where the practical or moral features of a belief is relevant to its epistemic status, and in our case specifically to whether an agent ought to hold the belief); and moral responsibility for AI beliefs. We also indicate two relatively nascent areas of philosophical research that haven’t yet been generally recognized as ethics of AI belief research, but that do fall within this field of research in virtue of investigating various moral and practical dimensions of belief: the epistemic and ethical decolonization of AI; and epistemic injustice in AI. (shrink)

Menschen mit psychischer Erkrankung sterben statistisch gesehen früher als die Allgemeinbevölkerung. Ein Grund hierfür ist, dass sie eine schlechtere somatische Gesundheitsversorgung erhalten. Wir argumentieren, dass ableistische Netzwerke sozialer Bedeutung zu einer Abwertung der epistemischen Kompetenz von Menschen mit psychischer Erkrankung führen. Diese Abwertung kann mit dem Konzept der testimonialen Ungerechtigkeit erfasst werden. Testimoniale Ungerechtigkeit bezeichnet das ungerechtfertigte Herabstufen der Glaubwürdigkeit einer*s Sprecher*in aufgrund eines Vorurteils gegen ihre*seine soziale Identität. Wir analysieren ethische und epistemische Folgen testimonialer Ungerechtigkeit als wichtige Ursachen der (...) schlechteren Gesundheitsversorgung von Menschen mit psychischer Erkrankung. Testimoniale Ungerechtigkeit kann zu medizinischen Behandlungsfehlern führen und für Betroffene schwerwiegende gesundheitliche Folgen nach sich ziehen. Zudem kann sie zu einem Vertrauensverlust von Menschen mit psychischer Erkrankung in das Gesundheitssystem führen. Daher trägt testimoniale Ungerechtigkeit zur strukturellen Diskriminierung von Menschen mit psychischer Erkrankung bei. Vor diesem Hintergrund diskutieren wir, wie die somatische Gesundheitsversorgung unter ethischen Gesichtspunkten verbessert werden kann. (shrink)

In mental health ethics, it is generally assumed that coercive measures are sometimes justified when persons with mental illness endanger themselves or others. Coercive measures are regarded as ethically justified only when certain criteria are fulfilled: for example, the intervention must be proportional in relation to the potential harm. In this paper, we demonstrate shortcomings of this established ethical framework in cases where people with mental illness experience structural racism. By drawing on a case example from mental healthcare, we first (...) demonstrate that biases in assessing whether the coercive intervention is proportional are likely, for example, due to an overestimation of dangerousness. We then show that even if proportionality is assessed correctly, and the specific coercive intervention would thus be regarded as ethically justified according to the standard framework, coercion may still be ethically problematic. This is because the standard framework does not consider how situations in which coercive measures are applied arise. If structural racism causally contributes to such situations, the use of coercion can compound the prior injustice of racist discrimination. We conclude that the ethical analysis of coercion in mental healthcare should consider the possibility of discriminatory biases and practices and systematically take the influence of structural discrimination into account. (shrink)