Much research has focused on the effects of pathogenic mitochondrial mutations on health. Notwithstanding, the mechanisms regulating the link between these mutations and their effects remain elusive in several cases. Here, we propose that certain mitochondrial mutations may disrupt function of a set of mitochondrial‐transcribed small RNAs, perturbing communication between mitochondria and nucleus, leading to disease. Our hypothesis synthesises two lines of supporting evidence. First, several mitochondrial mutations cannot be directly linked to effects on energy production or protein synthesis. Second, (...) emerging studies have described the existence of small RNAs encoded by the mitochondria and proposed their involvement in RNA interference. We present a roadmap to testing this hypothesis. (shrink)

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 consider how ML systems may silence patients’ voices and relativise (...) the credibility of their opinions, which undermines their overall credibility status without valid moral and epistemic justification. More specifically, I argue that withholding credibilitydue tohow ML systems operate can be particularly harmful to patients and, apart from adverse outcomes, qualifies as a form of testimonial injustice. I make my case for testimonial injustice in medical ML by considering ML systems currently used in the USA to predict patients’ risk of misusing opioids (automated Prediction Drug Monitoring Programmes, PDMPs for short). I argue that the locus of testimonial injustice in ML-mediated medical encounters is found in the fact that these systems are treated asmarkers of trustworthinesson which patients’ credibility is assessed. I further show how ML-based PDMPs exacerbate and further propagate social inequalities at the expense of vulnerable social groups. (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)

The advancement of AI-based technologies, such as machine learning (ML) systems, for implementation in healthcare is progressing rapidly. Since these systems are used to support healthcare professionals in crucial medical practices, their role in medical decision-making needs to be epistemologically and ethically assessed. However, a central issue at the intersection of the ethics and epistemology of ML has been largely neglected. This pertains to the careful scrutiny of how ML systems can degrade individuals’ epistemic standing as receivers and conveyors of (...) knowledge and, thereby, perpetrate epistemic injustice. Since ML systems are powerful epistemic entities that are not easily contestable, and their decision-making rationale is often inaccessible, it is crucial to consider their role in creating imbalances in patients’ disfavor and the ways to mitigate such imbalances. This is especially important when it comes to interactions between patients and physicians, in which questions of credibility, trust, and understanding are central. Against this background, the overarching purpose of this dissertation is to fill this research gap by providing a framework to identify and, on occasion, mitigate epistemic injustices that are _ML-induced_, i.e., that emerge specifically due to the role that ML systems play in patient-physician interactions. (shrink)

Overconfidence is typically damaging to one’s reputation as a trustworthy source of information. Previous research shows that the reputational cost associated with conveying a piece of false information is higher for confident than unconfident speakers. When judging speaker trustworthiness, individuals do not exclusively rely on past accuracy but consider the extent to which speakers expressed a degree of confidence that matched the accuracy of their claims (their “confidence-accuracy calibration”). The present study experimentally examines the interplay between confidence, accuracy and a (...) third factor, namely evidence, in the assessment of speaker trustworthiness. Experiment 1 probes the hypothesis that overconfidence does not backfire when a confident but inaccurate claim is justified: the trustworthiness of a confident speaker who turns out to be wrong is restored if the confidence expressed is based on strong evidence (good confidence-evidence calibration). Experiment 2 investigates the hypothesis that confidence can backfire if a confident and accurate claim is not justified: the trustworthiness of a confident speaker who turns out to be right is damaged if the confidence expressed is based on weak evidence (bad confidence-evidence calibration). Our results support both hypotheses and thus suggest that “confidence-evidence calibration” plays a crucial role in the assessment of speaker trustworthiness. (shrink)

L’accueil familial de la protection de l’enfant traverse une crise importante. Comprendre cette crise passe par l’analyse critique du dispositif d’Aide sociale à l’enfance dans lequel s’inscrit l’accueil familial. Les évolutions du métier d’assistant familial ont déstabilisé le dispositif lorsqu’elles n’ont pas été accompagnées par des transformations des organisations et des pratiques. La loi de 2016 qui réoriente la protection de l’enfance sur les besoins de l’enfant est une opportunité pour l’accueil familial. À partir de l’analyse critique des pratiques en (...) protection de l’enfance, cet article propose les concepts de pourvoyance des besoins de l’enfant, de parentalité empêchée et de développement du pouvoir être parent pour penser les pratiques de l’accueil familial d’enfant. (shrink)

Lo scritto traduce il tentativo di rendere in forma dialogica alcuni dei principali ragionamenti emersi da un percorso di Dottorato di ricerca, in particolare ponendo lo sguardo sugli attuali cambiamenti nella pratica del leggerescrivere e provando a prefigurarne di nuovi. I due personaggi sulla scena interpretano altrettanti distinti approcci alla questione cardine: i nuovi dispositivi multimediali, i diversi supporti digitali renderanno obsoleto l'oggetto "libro" per come noi, uomini moderni, l'abbiamo fino ad oggi conosciuto? La nuova era segnerÀ necessariamente il dissolversi (...) della civiltÀ del libro? (shrink)

Deep brain stimulation of the subthalamic nucleus or the globus pallidus is an established treatment for Parkinson’s disease that yields a marked and lasting improvement of motor symptoms. Yet, DBS benefit on gait disturbances in PD is still debated and can be a source of dissatisfaction and poor quality of life. Gait disturbances in PD encompass a variety of clinical manifestations and rely on different pathophysiological bases. While gait disturbances arising years after DBS surgery can be related to disease progression, (...) early impairment of gait may be secondary to treatable causes and benefits from DBS reprogramming. In this review, we tackle the issue of gait disturbances in PD patients with DBS by discussing their neurophysiological basis, providing a detailed clinical characterization, and proposing a pragmatic programming approach to support their management. (shrink)

The present study investigated whether humans are more likely to trust people who are coordinated with them. We examined a well-known type of linguistic coordination, lexical entrainment, typically involving the elaboration of “conceptual pacts,” or partner-specific agreements on how to conceptualize objects. In two experiments, we manipulated lexical entrainment in a referential communication task and measured the effect of this manipulation on epistemic and practical trust. Our results showed that participants were more likely to trust a coordinated partner than an (...) uncoordinated one, but only when the latter broke previously established conceptual pacts. (shrink)

The diffusion of Kantian philosophy in the Russian culture was a singularly early event: even the Orthodox ecclesiastical Academies were involved in this process, despite the control exercised by the censorship in defense of the metaphysical and theological tradition. The history and sources of the "philosophy of ecclesiastical academies" are also examined through the analysis of the works of two authors (I.Ja. Vetrinskij and P.D. Jurkevic), who, with their reaction to the Kantian "revolution", attested to the deep awareness of the (...) necessity of a real confrontation with criticism. This investigation is completed by an appendix dedicated to the complex events of Kantian studies in the Russian language during the decades marked by the Soviet regime. (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 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 have on the epistemological evaluation of ML systems. We argue that according to this methodological approach, (...) epistemological issues are _instrumental_ to and _autonomous_ of ethical considerations. This means that the informativeness account considers epistemological evaluation uninfluenced and unregulated by an ethical counterpart. Using an example that does not square well into the _informativeness account_, we argue for ethical assessments that have a substantial influence on the epistemological assessment of ML and that such influence should not be understood as merely informative but rather regulatory. Drawing on the case analyzed, we claim that within the theoretical framework of the informativeness approach, forms of epistemic injustice—especially _epistemic objectification_—remain unaddressed. Our analysis should motivate further research investigating the regulatory role that ethical elements play in the epistemology of ML. (shrink)

Introduction In their contribution, Ugar and Malele 1 shed light on an often overlooked but crucial aspect of the ethical development of machine learning (ML) systems to support the diagnosis of mental health disorders. The authors restrain their focus on pointing to the danger of misdiagnosing mental health pathologies that do not qualify as such within sub-Saharan African communities and argue for the need to include population-specific values in these technologies’ design. However, an analysis of the nature of the harm (...) caused to said populations once their values remain unrecognised is not offered. Building on Ugar and Malele’s considerations, we add a further perspective to their analysis by showing the need to design considering intended values to avoid the occurrence of epistemic injustices. 2 First, we argue that failing to acknowledge the hermeneutical offerings of the populations interacting with these systems can qualify as _contributory injustice_.shrink)

Given the risks of misinformation, addressees need to calibrate their trust towards communicators effectively. One way to do this is to evaluate the evidence speakers rely on (their evidential warrant) or claim to have (their evidential claims) when providing testimony. We review key findings in experimental psychology and experimental pragmatics to uncover the mechanisms underlying this form of trust calibration. This will ultimately shed light on the psychological foundations of principles that guide the evaluation of testimony in legal contexts, such (...) as the law against hearsay. (shrink)

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)

Resumen: Alfonsina Storni es una escritora que se ha labrado un lugar dentro del canon literario argentino, por mucho tiempo bajo una imagen de escritora que anudaba sentimentalismo, fatalidad y poesía “femenina”, aunque más recientemente la crítica literaria de orientación feminista haya desmontado esa imagen. Este trabajo se interesa, no obstante, por los años en que Alfonsina Storni publicaba sus primeros poemarios y comenzaba a pugnar por un lugar en el incipiente campo literario, dominantemente masculino. El objetivo es visualizar, desde (...) una perspectiva de género, las prácticas culturales emergentes, dominantes y residuales que contribuyeron o condicionaron su camino hacia la profesionalización como escritora, en un contexto de cambios por los procesos modernizadores y de avances del movimiento feminista.: Alfonsina Storni is a writer who has forged a position within the Argentinian literary canon, under the image of a writer who revokes sentimentalism, fatality and “feminine” poetry. Recently, the feminist-oriented literary criticism has dismantled this image finding out other areas in her literary production. This work is focused on the years in which Alfonsina Storni published her first collections of poems and started struggling for a certain position in the emerging literary field, which was mainly under male domain. Taking a gender perspective, the aim is to visualize emerging dominant, and residual cultural practices that contributed or determined her path towards becoming a professional writer, in a context of changes due to modernizing processes and the feminist movement advance. (shrink)

The social aspects of causality in medicine and healthcare have been emphasized in recent debates in the philosophy of science as crucial factors that need to be considered to enable, among others, appropriate interventions in public health. Therefore, it seems central to recognize the bearing of social causes (broadly understood, e.g., social inequalities and socio-economic status) in bringing about certain concrete pathologies. Being aware of the relevance of social causes in medicine and healthcare is particularly important in the face of (...) the role that artificial intelligence-based systems (such as machine learning algorithms) are increasingly playing in these high-stakes fields. In fact, these systems bear the dangerous potential of concealing relevant social causes. This is highly problematic not only because it reinforces issues of distributive injustice but also because it can pave the way for issues of epistemic injustice. The central aim of this chapter is to make a first effort to point out possible connections between the importance of recognizing social causes in medicine and healthcare and forms of epistemic injustice. (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...

The study of the history of ideas is usually devoted to big problems and to concluded debates. We have attempted to analyze a current theory whose fate and explanatory power is still not determined. The term microparadigm is used to define a currently and widely accepted theory limited in time and in the field of application, compared to the greater problems usually investigated by historians of science. Among the characteristics defining a microparadigm we found: 1) the status of an accepted (...) theory with creative scientific power; 2) the presence of anomalies and unproven inferences; 3) a limited field of application; 4) the peculiarity of interaction with non-scientific ideas. In this context, we discuss the rise of the current microparadigm concerning the pathogenesis and biology of lymphoid neoplasms. We show that the current view of the neoplastic lymphoid cells as populations frozen at a particular differentiative stage has been creative for the last 10 years, thus allowing the generation of a large body of data that would not have been collected within the previous view of the leukemic cell as completely “anarchic”. This paradigm, although containing some anomalies, has survived the impact of molecular biology in hematology and is still creative. We think that microparadigms are widely distributed in everyday science and that an analysis of them is as useful for active scientist as is the study of macroparadigms, which by themselves cannot be representative of everyday science. Finally, the study of microparadigms while they are still operative can be useful to evidence the weakness of the theory and to suggest where new data should be sought. (shrink)

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 trustworthiness in AI. To achieve our goals, we proceed as follows. We start by discussing philosophical positions on trust and (...) trustworthiness, focusing on interpersonal accounts of trust. This allows us to explain why trust, in its most general terms, is to be understood as reliance plus some “extra factor”. We then turn to the first part of the definition provided, i.e., reliance, and analyze two opposing approaches to establishing AI systems’ reliability. On the one hand, we consider transparency and, on the other, computational reliabilism. Subsequently, we focus on debates revolving around the “extra factor”. To this end, we consider viewpoints that most actively resist the possibility and desirability of trusting AI systems before turning to the analysis of the most prominent advocates of it. Finally, we take up the main conclusions of the previous sections and briefly point at issues that remain open and need further attention. (shrink)