This paper aims at showing the need for a sound ethical and anthropological foundation of economics and business, and argues the importance of a correct understanding of human values and human nature for the sake of economics and of businesses themselves. It is suggested that the ethical-anthropological side of economics and business can be grasped by taking Aristotle’s virtue ethics and Amartya Sen’s capability approach (CA) as major reference points. We hold that an “Aristotelian economics of virtues”, connected with the CA’s notion of human richness, can promote the shift to the concept of personhood, and can lead to a more “humanized” business, by fostering human flourishing, the enhancement of human capabilities, and the pursuit of a more humane development for each and every person

This paper aims at developing the Capability Approach's (CA) underlying philosophical anthropology and ethics by focusing on the work of its major exponents, Amartya Sen and Martha Nussbaum. I first discuss CA's critique of happiness as subjective well-being and defend the idea of 'flourishing' which ultimately refers to the Aristotelian concept of eudaimonia. I then focus on the notions of 'good' and 'well-being' and address the problem of the compatibility between a substantive notion of the Good (expressed through universal moral values) and individual preferences. I thus tackle the issue of adaptive preferences (which is investigated both from a methodological and an ethical perspective) and suggest that the process of adaptation should be thought in the dynamic frame of the constitution of the self. Therefore, in the second half of the paper I investigate the CA's idea of personhood and focus on some important assumptions behind its underlying anthropological model - above all the notion of 'human richness'. As a result, I first point out the dynamic dimension of personhood, according to which individuals are 'becoming themselves' in search of self-realisation and construction of their identities. Second, I highlight its relational dimension, according to which every one is the expression of the anthropological richness and at the same time represents the highest possibility of richness for every other one.adaptive preferences, Amartya Sen, Aristotle, capabilities, capability approach, ethics, good, identity, Karl Marx, Martha Nussbaum, personhood, philosophical anthropology, richness, well-being,

The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral philosophy and using them to conceptualise fairness as an ethical value and to redefine fairness in HMLA accordingly. To achieve our goal, following a first section aimed at clarifying the background, methodology and structure of the paper, in the second section, we provide an overview of the discussion of the AI ethics principle of fairness in HMLA and show that the concept of fairness underlying this debate is framed in purely distributive terms and overlaps with non-discrimination, which is defined in turn as the absence of biases. After showing that this framing is inadequate, in the third section, we pursue an ethical inquiry into the concept of fairness and argue that fairness ought to be conceived of as an ethical value. Following a clarification of the relationship between fairness and non-discrimination, we show that the two do not overlap and that fairness requires much more than just non-discrimination. Moreover, we highlight that fairness not only has a distributive but also a socio-relational dimension. Finally, we pinpoint the constitutive components of fairness. In doing so, we base our arguments on a renewed reflection on the concept of respect, which goes beyond the idea of equal respect to include respect for individual persons. In the fourth section, we analyse the implications of our conceptual redefinition of fairness as an ethical value in the discussion of fairness in HMLA. Here, we claim that fairness requires more than non-discrimination and the absence of biases as well as more than just distribution; it needs to ensure that HMLA respects persons both as persons and as particular individuals. Finally, in the fifth section, we sketch some broader implications and show how our inquiry can contribute to making HMLA and, more generally, AI promote the social good and a fairer society.

Fairness is one of the most prominent values in the Ethics and Artificial Intelligence (AI) debate and, specifically, in the discussion on algorithmic decision-making (ADM). However, while the need for fairness in ADM is widely acknowledged, the very concept of fairness has not been sufficiently explored so far. Our paper aims to fill this gap and claims that an ethically informed re-definition of fairness is needed to adequately investigate fairness in ADM. To achieve our goal, after an introductory section aimed at clarifying the aim and structure of the paper, in section “Fairness in algorithmic decision-making” we provide an overview of the state of the art of the discussion on fairness in ADM and show its shortcomings; in section “Fairness as an ethical value”, we pursue an ethical inquiry into the concept of fairness, drawing insights from accounts of fairness developed in moral philosophy, and define fairness as an ethical value. In particular, we argue that fairness is articulated in a distributive and socio-relational dimension; it comprises three main components: fair equality of opportunity, equal right to justification, and fair equality of relationship; these components are grounded in the need to respect persons both as persons and as particular individuals. In section “Fairness in algorithmic decision-making revised”, we analyze the implications of our redefinition of fairness as an ethical value on the discussion of fairness in ADM and show that each component of fairness has profound effects on the criteria that ADM ought to meet. Finally, in section “Concluding remarks”, we sketch some broader implications and conclude.