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)

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)

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)

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 United States is one of the most charitable nations, yet comprises some of the most materialistic citizens in the world. Interestingly, little is known about how the consumer trait of materialism, as well as the opposing moral trait of gratitude, influences charitable giving. We address this gap in the literature by theorizing and empirically testing that the effects of these consumer traits on charitable behavior can be explained by diverse motivations. We discuss the theoretical implications, along with implications for (...) charitable organizations, and offer suggestions for future research. (shrink)

We propose an extension to Mahr & Csibra's (M&C's) theory. For successful episodic memory formation, potentially relevant aspects of a situation need to be identified and encodedonlineand retained for prospective interactions.To be maximally convincing, the communicator not only has to encode not justanycontextual detail, but also has to track informationin relation tosocial partners.

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)

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)

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)

Violence constitutes an important element of reflection for political philosophy. Two proposed theories of violence are discussed here. One takes violence to be the condition of politics. This is Carl Schmitt’s proposal. He says that politics is always conflictive: political space is a battle cam..

In this writing it is presented Rawl’s proposal about the possibility of civil disobedience in the face of a democratic authority legitimately established. Rawlsian civil disobedience shows its antiauthoritarian claims depending on the maintenance of a fair order, and social society is the way of..

A life colonization is produced due to systemic demands that as a sign of the present-day life invades it with its logical functioning throwing moral and ethical values out either in private life or in public opinion. It is also important to point out a great increase of many pathologies in private..

Notwithstanding universities’ many laudable aims, incidents of serious bullying, academic harassment and sexual harassment in academic settings are reported with increasing regularity globally. However, the human rights violations involved in bullying and academic harassment have not received attention by the literature. In this article, we pierce the veil of silence surrounding university environments and provide a systematic account of the breaches of international and European human rights law involved in academic bullying and harassment. By adopting a socio-legal lens, we shed (...) light onto such practices and tactics, the breaches of specific human rights and norms and the reluctance of states and regulators to intervene in what is perceived to be universities’ ‘sovereign’ sphere of jurisdiction in order to prevent human rights’ violations and to protect the victims of human rights abuse. We call for a multi-faceted and multi-agency approach to tackle academic bullying and harassment, and make a number of institutional and policy recommendations to ensure universities’ compliance with human rights standards and the effective protection of academics under attack. (shrink)

The relationship between food and religion is a lived activity formed by the dynamics of both tradition and adaption. Religious commitments to food are influenced by various factors, ranging from personal spirituality and experiences to social patterns of belonging, ethical, polit­ical and doctrinal convictions. _Challah_, _gefilte_ _fish_, _blintzes_ – these are just a few of the traditional Finnish Jewish meals that are still prepared by members of the community. The originally Eastern European dishes are one of the last living links (...) that connect assimilated Finnish Jews with their Orthodox Jewish ancestors mainly from Russia, Poland, Belarus, and Lithuania. The current paper aims to present the conceptions and reflections relating to boundaries of identity connected with the multi-ethnic culinary traditions of Jews living in Finland as well as their ways of coping with the requirements of _kashrut_. The article is based on ethnographic data from interviews as well as personal encounters, informal conversations and home visits. (shrink)

In this article, we will concentrate on the two true norm definitions that have existed throughout the history of the Deontic Logic: that offered by Professor Kalinowski and that proposed by the semantics of possible worlds. The former is based on Tarski’s definition of the true proposition, but it has the drawback of depending on a concrete, philosophical theory concerning the nature of norms. The latter, widely accepted nowadays, presents difficulties which we will analyse, using as a reference, K. Solt’s (...) critique. Despite its limitations, it offers the indisputable advantage of being independant of any concrete, philosophical option, and indicates, in our opinion, the correct way of proceeding. (shrink)

v. 1. Nae Ionescu -- v. 2. Mircea Eliade -- v. 3. Octav Onicescu -- v. 4, pt. 1-2. Noica şi Securitatea.