Painful Virtue, Marginalisation, and Resistance

This paper argues a potentially controversial thesis in virtue ethics, i.e., in situations of oppression and marginalisation, it is better to be a person of atypical virtue, one who has struggled to resist oppressive circumstances, than it is to be a traditionally defined virtuous agent. As such, those who have been through a tragic dilemma (or several) are more important for successful resistance movements than their traditionally defined counterparts. This paper does not romanticise oppressive situations or their influence on some individuals developing virtuous actions and behaviours. Instead, it acknowledges that these are tragic circumstances that permanently affect some individuals for the rest of their lives. However, the argument here is that these individuals can utilise their experiences as reasons to continue resisting until a time comes where future generations will not need to experience such tragic circumstances. To demonstrate the applicability of this argument, this paper will consider the struggles of queer individuals in a Canadian context. This is achieved by demonstrating how those individuals who led the fight for queer rights used their experiences of marginalisation in early resistance movements. It then shifts focus to address current issues in Canadian queer lives

What Kind of Artificial Intelligence Should We Want for Use in Healthcare Decision-Making Applications?

The prospect of including artificial intelligence (AI) in clinical decision-making is an exciting next step for some areas of healthcare. This article provides an analysis of the available kinds of AI systems, focusing on macro-level characteristics. This includes examining the strengths and weaknesses of opaque systems and fully explainable systems. Ultimately, the article argues that “grey box” systems, which include some combination of opacity and transparency, ought to be used in healthcare settings.La perspective d’inclure l’intelligence artificielle (IA) dans la prise de décision clinique est une prochaine étape passionnante pour certains secteurs des soins de santé. Cet article propose une analyse des types de systèmes d’IA disponibles, en se concentrant sur les caractéristiques de niveau macro. Il examine notamment les forces et les faiblesses des systèmes opaques et des systèmes entièrement explicables. En fin de compte, l’article soutient que les systèmes de type « boîte grise », qui combinent opacité et transparence, devraient être utilisés dans le domaine des soins de santé

Ethics and healthcare artificial intelligence : some problems and solutions for advanced diagnostic systems

The use of artificial intelligence for diagnostic purposes is a significant topic of discussion in the present-day healthcare field. Developers working on this technology intend it to outperform human clinicians and therefore remove some of the burden from clinicians so that they can spend more time developing relationships of care with their patients. I focus on three ethical questions I believe should be answered prior to their widespread onboarding in healthcare systems. I first investigate what kind of artificial intelligence we ought to want for diagnostic systems in healthcare. I analyse three high-level categories of artificial intelligence: opaque black box systems, robustly transparent explainable systems, and semi-transparent grey box systems. I start by outlining the characteristics of these systems, culminating in the development of a novel definition for the black box problem. I then take this information and analyse each kind of high-level system. I defend the position that the best kind of systems for healthcare applications are grey boxes due to their customizability and semi-transparent nature. Second, I examine what obligations clinicians ought to have to their patients whenever they employ an artificial intelligence for diagnostics. I separate this chapter into three sets of obligations, one for each of the categories from chapter one, as a clinician may not always have the option to work exclusively with grey systems. By providing three lists, I ensure that clinicians have a minimum starting point regardless of what kind of system they employ. Finally, I address the implicitly articulated concerns surrounding whether the general push for more advanced AI in healthcare jeopardizes a patient’s ability to provide informed consent to a procedure or treatment plan. These concerns are raised nearly ubiquitously in the literature, yet to date no comprehensive analysis regarding the issue exists. I argue that the concerns over consent are actually due to a false dilemma in how we discuss the kinds of artificial intelligence in the literature. I then demonstrate how this false dilemma can be defeated through appeals to grey box systems and using human clinician secondary readers.Arts, Faculty ofPhilosophy, Department ofGraduat

Painful Virtue, Marginalisation, and Resistance

This paper argues a potentially controversial thesis in virtue ethics, i.e., in situations of oppression and marginalisation, it is better to be a person of atypical virtue, one who has struggled to resist oppressive circumstances, than it is to be a traditionally defined virtuous agent. As such, those who have been through a tragic dilemma (or several) are more important for successful resistance movements than their traditionally defined counterparts. This paper does not romanticise oppressive situations or their influence on some individuals developing virtuous actions and behaviours. Instead, it acknowledges that these are tragic circumstances that permanently affect some individuals for the rest of their lives. However, the argument here is that these individuals can utilise their experiences as reasons to continue resisting until a time comes where future generations will not need to experience such tragic circumstances. To demonstrate the applicability of this argument, this paper will consider the struggles of queer individuals in a Canadian context. This is achieved by demonstrating how those individuals who led the fight for queer rights used their experiences of marginalisation in early resistance movements. It then shifts focus to address current issues in Canadian queer lives

What Kind of Artificial Intelligence Should We Want for Use in Healthcare Decision-Making Applications?

The prospect of including artificial intelligence (AI) in clinical decision-making is an exciting next step for some areas of healthcare. This article provides an analysis of the available kinds of AI systems, focusing on macro-level characteristics. This includes examining the strengths and weaknesses of opaque systems and fully explainable systems. Ultimately, the article argues that “grey box” systems, which include some combination of opacity and transparency, ought to be used in healthcare settings.La perspective d’inclure l’intelligence artificielle (IA) dans la prise de décision clinique est une prochaine étape passionnante pour certains secteurs des soins de santé. Cet article propose une analyse des types de systèmes d’IA disponibles, en se concentrant sur les caractéristiques de niveau macro. Il examine notamment les forces et les faiblesses des systèmes opaques et des systèmes entièrement explicables. En fin de compte, l’article soutient que les systèmes de type « boîte grise », qui combinent opacité et transparence, devraient être utilisés dans le domaine des soins de santé