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)

Background Symptom checker apps (SCAs) are mobile or online applications for lay people that usually have two main functions: symptom analysis and recommendations. SCAs ask users questions about their symptoms via a chatbot, give a list with possible causes, and provide a recommendation, such as seeing a physician. However, it is unclear whether the actual performance of a SCA corresponds to the users’ experiences. This qualitative study investigates the subjective perspectives of SCA users to close the empirical gap identified in (...) the literature and answers the following main research question: How do individuals (healthy users and patients) experience the usage of SCA, including their attitudes, expectations, motivations, and concerns regarding their SCA use? Methods A qualitative interview study was chosen to clarify the relatively unknown experience of SCA use. Semi-structured qualitative interviews with SCA users were carried out by two researchers in tandem via video call. Qualitative content analysis was selected as methodology for the data analysis. Results Fifteen interviews with SCA users were conducted and seven main categories identified: (1) Attitudes towards findings and recommendations, (2) Communication, (3) Contact with physicians, (4) Expectations (prior to use), (5) Motivations, (6) Risks, and (7) SCA-use for others. Conclusions The aspects identified in the analysis emphasise the specific perspective of SCA users and, at the same time, the immense scope of different experiences. Moreover, the study reveals ethical issues, such as relational aspects, that are often overlooked in debates on mHealth. Both empirical and ethical research is more needed, as the awareness of the subjective experience of those affected is an essential component in the responsible development and implementation of health apps such as SCA. Trial registration German Clinical Trials Register (DRKS): DRKS00022465. 07/08/2020. (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)

Digital phenotyping will potentially enable earlier detection and prediction of mental illness by monitoring human interaction with and through digital devices. Notwithstanding its promises, it is certain that a person’s digital phenotype will at times be at odds with their first-person testimony of their psychological states. In this paper, we argue that there are features of digital phenotyping in the context of psychiatry which have the potential to exacerbate the tendency to dismiss patients’ testimony and treatment preferences, which can be (...) instances of epistemic injustice. We first explain what epistemic injustice is, and why it is argued to be an extensive problem in health and disability settings. We then explain why epistemic injustice is more likely to apply with even greater force in psychiatric contexts, and especially where digital phenotyping may be involved. Finally, we offer some tentative suggestions of how epistemic injustice can be minimised in digital psychiatry. (shrink)

Doernberg and Truog (2023) provide an insightful analysis of the role of medical professionals in what they call spheres of morality. While their framework is useful for inquiring into the moral de...

In my paper entitled ‘Testimonial injustice in medical machine learning’,1 I argued that machine learning (ML)-based Prediction Drug Monitoring Programmes (PDMPs) could infringe on patients’ epistemic and moral standing inflicting a testimonial injustice.2 I am very grateful for all the comments the paper received, some of which expand on it while others take a more critical view. This response addresses two objections raised to my consideration of ML-induced testimonial injustice in order to clarify the position taken in the paper. The (...) first maintains that my critical stance toward ML-based PDMPs idealises standard medical practice. Moreover, it claims that the ML-induced testimonial injustice I discuss is not substantially different from situations in which it emerges in human–human interactions. The second claims that my analysis does not establish a link to issues of automation bias, even if these are to be considered the core of testimonial injustice in ML. In the following, I address each objection in turn. Gillett3 argues that my critical stance towards using risk prediction tools such as PDMPs implies the idealisation of standard (ie, non-ML-mediated) modes of clinical practice. Considering certain uses of ML in a different setting, that is, psychiatry, the author goes as far as claiming that ‘traditional models of clinical practice in psychiatry are far from a utopia, free from epistemic injustice, which Pozzi’s argument risks proposing’. Since this statement does not represent what I intend to suggest, I am glad to have the possibility to clarify …. (shrink)

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 analyze the shortcomings of an ML system currently deployed in the USA to predict patients’ likelihood (...) of opioid addiction and misuse (PDMP algorithmic platforms). Drawing on this analysis, I aim to show that the wrong inflicted on epistemic agents involved in and affected by these systems’ decision-making processes can be captured through the lenses of Miranda Fricker’s account of hermeneutical injustice. I further argue that ML-induced hermeneutical injustice is particularly harmful due to what I define as an automated hermeneutical appropriation from the side of the ML system. The latter occurs if the ML system establishes meanings and shared hermeneutical resources without allowing for human oversight, impairing understanding and communication practices among stakeholders involved in medical decision-making. Furthermore and very much crucially, an automated hermeneutical appropriation can be recognized if physicians are strongly limited in their possibilities to safeguard patients from ML-induced hermeneutical injustice. Overall, my paper should expand the analysis of ethical issues raised by ML systems that are to be considered epistemic in nature, thus contributing to bridging the gap between these two dimensions in the ongoing debate. (shrink)

In the article ‘Testimonial injustice in medical machine learning’, Pozzi argues that the prescription drug monitoring programme (PDMP) leads to testimonial injustice as physicians are more inclined to trust the PDMP’s risk scores over the patient’s own account of their medication history.1 Pozzi further develops this argument by discussing how credibility shifts from patients to machine learning (ML) systems that are supposedly neutral. As a result, a sense of distrust is now formed between patients and physicians. While there are merits (...) to Pozzi’s main argument of epistemic injustice caused by PDMPs, Pozzi mentions but ultimately glosses over the problem of automation bias. In this commentary, I will discuss automation bias and the affect it has on clinical decision making as well as a technical problem exacerbated by the usage of PDMPs that can potentially cause physical harms. It is reiterated in the article that the confidence physicians have in the PDMP’s risk scores over the patient’s testimony leads to misplaced trust in the ML systems. What Pozzi describes here is known as automation bias, which occurs when there is an over-reliance on ML systems. …. (shrink)

Artificial intelligence (AI) and machine learning (ML) systems increasingly purport to deliver knowledge about people and the world. Unfortunately, they also seem to frequently present results that repeat or magnify biased treatment of racial and other vulnerable minorities. This paper proposes that at least some of the problems with AI’s treatment of minorities can be captured by the concept of epistemic injustice. To substantiate this claim, I argue that (1) pretrial detention and physiognomic AI systems commit testimonial injustice because their (...) target variables reflect inaccurate and unjust proxies for what they claim to measure; (2) classification systems, such as facial recognition, commit hermeneutic injustice because their classification taxonomies, almost no matter how they are derived, reflect and perpetuate racial and other stereotypes; and (3) epistemic injustice better explains what is going wrong in these types of situations than does the more common focus on procedural (un)fairness. (shrink)

Pozzi provides a thought-provoking account of how machine-learning clinical prediction models (such as Prediction Drug Monitoring Programmes (PDMPs)) may exacerbate testimonial injustice.1 In this response, I generalise Pozzi’s concerns about PDMPs to traditional models of clinical practice and question the claim that inaccurate clinicians are necessarily preferential to inaccurate machine-learning models. I then explore Pozzi’s concern that such models may deprive patients of a right to ‘convey information’. I suggest that machine-learning tools may be used to enhance, rather than frustrate, (...) this right, through the perspective of hermeneutical justice. Pozzi objects to the introduction of machine-learning risk prediction tools in clinical care, due to their being ‘epistemically opaque’, often inaccurate, and a threat to patients’ testimonial justice. Through the example of psychiatry, I suggest this stance idealises traditional models of clinical practice. I propose that clinicians’ judgements are often equally vulnerable to opaque subjectivity, bias and epistemic injustice. For instance, the use of clinical observation or collateral interviewing might be conceptualised as a threat to testimonial justice, by neglecting patients’ own perspectives or voices. In practice, clinical risk prediction often follows inconsistent and somewhat subjective clinical heuristics, which are difficult to summarise or evaluate. Further, although Pozzi diminishes machine-learning …. (shrink)

Giorgia Pozzi’s feature article1 on the risks of testimonial injustice when using automated prediction drug monitoring programmes (PDMPs) turns the spotlight on a pressing and well-known clinical problem: physicians’ challenges to predict patient behaviour, so that treatment decisions can be made based on this information, despite any fallibility. Currently, as one possible way to improve prognostic assessments of patient behaviour, Machine Learning-driven clinical decision support systems (ML-CDSS) are being developed and deployed. To make her point, Pozzi discusses ML-CDSSs that are (...) supposed to deliver physicians an accurate estimation of the likelihood of narcotic, sedative and stimulant opioid misuse by a given patient (like, e.g., ‘NarxCare’). Regarding cases of deviating assessment between human evaluators and automated systems, the medico-ethical discussion has so far mainly focused on disagreement between clinicians and Machine Learning (ML) algorithms,2 for example in ‘second opinions’ that have been reconstructed as disagreements between physician testimony and ML-CDSS testimony.3 Examples such as those of PDMPs, by contrast, draw attention to situations in which patient testimony potentially contradicts ML-CDSS testimony. Pozzi examines decision-making situations in which ML-CDSS testimony factually overrides the testimony of the person affected. Thus, in the presented case of patient Kathryn, the testimony of her own physical and mental condition was insufficient to overturn the unfavourable outcome for …. (shrink)

Pozzi1 has thoroughly analysed testimonial injustices in the automated Prediction Drug Monitoring Programmes (PDMPs) case. Although Pozzi1 suggests that ‘the shift from an interpersonal to a structural dimension … bears a significant moral component’, her topical investigation does not further conceptualise the type of collective knowledge practices necessary to achieve epistemic justice. As Pozzi1 concludes: ‘this paper shows the limitations of systems such as automated PDMPs, it does not provide possible solutions’. In this commentary, we propose that an Ubuntu perspective—which, (...) so far, has received little attention in connection with epistemic injustice in medical machine learning (MML)—can fill this gap. This perspective can be used to mitigate some of the harmful effects of epistemic injustices by encouraging community involvement and forward-looking responsibility. It may be instructive to note first and foremost that the debate on epistemic justice and artificial intelligence in medicine has mostly been restricted to the Western World, that is, Europe and North America. There has been very little insight or accommodation of insights from the Global South. Ubuntu is a concept originating from Southern Africa that focuses on the close connections and mutual …. (shrink)

In their article, Sedlakova and Trachsel (2023) propose a holistic, ethical, and epistemic analysis of conversational artificial intelligence (CAI) in psychotherapeutic settings. They mainly descri...