Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that (...) human error is the main cause of the 57 % of road accidents and a contributing factor in most of them. In this study, 20 young subjects were involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, were acted. A Workload Index based on the Electroencephalographic (EEG), i.e. brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking technology and subjective measures were also employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to Eye-Tracking and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research. (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)

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)

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)

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 dependence turns into what we refer to as harmful epistemic dependence of clinical professionals on medical ML. The (...) latter occurs when the impossibility of critically assessing the soundness of a system’s output _in situ_ implies a moral obligation to comply with its recommendation since a failure to do so constitutes a moral risk that cannot be justified then and there. We analyse the epistemic and moral consequences of harmful epistemic dependence on the status of medical professionals. We conclude by assessing how a suitable design of the epistemic niche can address the problem. (shrink)

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)

Volume 26, Issue 2, February 2026, Page 86-88.

The integration of AI systems in medical care magnifies questions related to how physicians should work with such systems to ensure the best patient outcomes. A particularly thorny issue is related to dealing with situations of possible disagreement between an AI system’s recommendation and the course of medical action envisaged by a human clinician. The current academic debate has so far suggested three possible ways of dealing with such clinician-AI disagreements. First, by considering when clinicians are justified in deferring to (...) the AI output (what we call the deference approach), second when the human user overrules the AI system’s output in cases of disagreement (the overruling approach), and lastly when a second human opinion is deemed necessary to resolve disagreements (the second opinion approach). In this paper, we aim to spell out the shortcomings of these three approaches for dealing with clinician-AI disagreement and offer a more nuanced perspective on such disagreements. We argue that differentiation between types of disagreements, taking into account the role attributed to AI in medical practice, is essential before determining how clinician-AI disagreements should be dealt with. By drawing on a case that exemplifies how multifaceted medical decision-making is, we point out the normative implications of possible clinician-AI disagreements ensuing from it. We highlight the distinctive uncertainties inherent to medical decision-making, showing that disagreements in these contexts are not merely unavoidable but can even be epistemically valuable. Ultimately, by considering the epistemic positions of clinicians and AI systems, our analysis raises important questions for the epistemology of disagreement that need timely attention. (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)

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...

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 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)

Este trabajo analiza en el poema Astronomica de M. Manilio el enfrentamiento entre el latín y el griego en la nomenclatura de términos técnicos de la astrología y de algunas constelaciones. Se contrasta este enfrentamiento con la caracterización ideológica de la lengua nacional y se concluye que, más allá de la dependencia lingüística de fuentes griegas para la elaboración de su poema, Manilio escribe una obra netamente romana donde la variedad de términos y procedimientos utilizados para traducir los conceptos muestra (...) la superación del tópico tradicional de la egestas linguae latinae. (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)

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)

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)

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)

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)

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)

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)