Definition of the problem Few concepts in recent ethical debates have enjoyed as much popularity as the concept of discrimination. However, a comparative discussion of the concept, including its conceptual nuances and its ethical significance for health care, has so far been lacking. The aim of this paper is to develop a nuanced understanding of discrimination based on the philosophical and sociological literature against the background of ethically relevant medical and nursing scenarios. Methods Using practical examples from health care, we (...) discuss direct, indirect, and statistical discrimination; we examine the importance of intentions and the role of collective actors. In addition, we analyze which social groups may experience discrimination and the respective importance of forms of epistemic injustice. Conclusions We argue that despite its complexity, the concept offers significant added value because it allows us to highlight forms of disadvantage that go beyond the violation of individual specific rights or interests. As a knowledge-based system of great societal influence, health care exhibits some characteristics that can foster discrimination. Acknowledging this fact is essential in order to effectively address discrimination. (shrink)

Kaum ein Begriff der ethischen Debatten der letzten Jahre hat eine solche Konjunktur erlebt wie der Begriff der Diskriminierung. Eine vergleichende Erörterung des Konzepts einschließlich seiner begrifflichen Nuancen und seiner ethischen Bedeutung für das Gesundheitswesen fehlte jedoch bislang. Ziel dieses Beitrags ist die Entwicklung eines differenzierten Verständnisses von Diskriminierung auf der Basis der philosophischen und soziologischen Literatur vor dem Hintergrund ethisch relevanter medizinischer und pflegerischer Szenarios. Anhand von praktischen Beispielen aus dem Gesundheitswesen erörtern wir die Besonderheiten direkter, indirekter und statistischer (...) Diskriminierung, wir untersuchen die Bedeutung von Absichten und die Rolle von kollektiven Akteuren. Zudem diskutieren wir, welche sozialen Gruppen diskriminiert werden können und welche Rolle Formen epistemischer Ungerechtigkeit dabei spielen. Argumentiert wird, dass das Konzept trotz seiner Komplexität einen signifikanten Mehrwert bietet, weil es erlaubt, Formen der Benachteiligung aufzuzeigen, die über die Verletzung einzelner spezifischer Rechte oder Interessen hinausgehen. Als wissensbasiertes System von großem gesellschaftlichen Einfluss weist das Gesundheitswesen einige Eigenschaften auf, die Diskriminierung begünstigen können. Dies zu erkennen ist unerlässlich, um Diskriminierung wirksam entgegentreten zu können. (shrink)

This paper concerns the question of what makes disability discrimination morally objectionable. When I refer to disability discrimination, I am focusing solely on a failure or denial of reasonable accommodations to a disabled person. I argue a failure to provide reasonable accommodations is wrong when and because it violates principles of relational equality. To do so, I examine four accounts of wrongful discrimination found in the literature and apply these theories to disability discrimination. I argue that all of these accounts (...) leave us wanting because they have implausible implications or other limitations. I then defend my relational egalitarian theory of wrongful disability discrimination. I argue why principles of relational equality can explain the wrongness of failing to accommodate a disabled person. I will then illustrate how my account can identify plausible instances of disability discrimination while avoiding implausible implications. Finally, I discuss some further complications with my account while highlighting its benefits. Importantly, my argument can capture the distinctive wrongness of disability law and policy’s reasonable accommodation mandates. (shrink)

It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that the (...) resulting predictive model does not use race as an explicit predictor. However, if race is correlated with measured recidivism in the training data, the ML tool may ‘learn’ a perfect proxy for race. If such a proxy is found, the exclusion of race would do nothing to weaken the correlation between risk (mis)classifications and race. Is this a problem? We argue that, on some explanations of the wrongness of discrimination, it is. On these explanations, the use of an ML tool that perfectly proxies race would (likely) be more wrong than the use of a traditional tool that imperfectly proxies race. Indeed, on some views, use of a perfect proxy for race is plausibly as wrong as explicit racial profiling. We end by drawing out four implications of our arguments. (shrink)