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This book investigates the relationship between our present and future selves. It focuses specifically on diachronic self-regarding decisions: choices involving our earlier and later selves, in which the earlier self makes a decision for the later self. The author connects the scientific understanding of the neurobehavioral processes at the core of individuals' perceptions of their future selves with the philosophical reflection on individuals' moral relationship with their future selves. She delineates a descriptive theory of the perception of the future self that is based on empirical evidence and that systematizes and integrates the current theoretical literature. She then argues for the morality of prudence and interprets diachronic self-regarding decisions as decisions between two agents— the earlier and later selves—that belong to the realm of intergenerational ethics, which regulates the relationship between contemporary people and future generations. Finally, the author provides a moral theory of prudence based on respect for one's agency. This theory identifies what the present and the future selves owe to one another in diachronic self-regarding decisions. Moral Choices for Our Future Selves will be of interest to scholars and students working in ethics, moral psychology, philosophy of mind, and cognitive science.

"This book investigates the relationship between our present and future selves. It focuses specifically on diachronic self-regarding decisions: choices involving our earlier and later selves, in which the earlier self makes a decision for the later self. The author connects the scientific understanding of the neurobehavioral processes at the core of individuals' perceptions of their future selves with the philosophical reflection on individuals' moral relationship with their future selves. She delineates a descriptive theory of the perception of the future self that is based on empirical evidence and that systematizes and integrates the current theoretical literature. She then argues for the morality of prudence and interprets diachronic self-regarding decisions as decisions between two agents-the earlier and later selves-that belong to the realm of intergenerational ethics, which regulates the relationship between contemporary people and future generations. Finally, the author provides a moral theory of prudence based on respect for one's agency. This theory identifies what the present and the future selves owe to one another in diachronic self-regarding decisions. Moral Choices for Our Future Selves will be of interest to researchers and students working in ethics, moral psychology, philosophy of mind, and cognitive science"--

AbstractRecommendation systems (RSs) predict what the user likes and recommend it to them. While at the onset of RSs, the latter was designed to maximize the recommendation accuracy (i.e., accuracy was their only goal), nowadays many RSs models include diversity in recommendations (which thus is a further goal of RSs). In the computer science community, the introduction of diversity in RSs is justified mainly through economic reasons: diversity increases user satisfaction and, in niche markets, profits.I contend that, first, the economic justification of diversity in RSs risks reducing it to an empirical matter of preference; second, diversity is ethically relevant as it supports two autonomy rights of the user: the right to an open present and the right to be treated as an individual. So far, diversity in RSs has been morally defended only in the case of RSs of news and scholarly content: diversity is held to have a depolarizing effect in a democratic society and the scientific community and make the users more autonomous in their news choices. I provide a justification of diversity in RSs that embraces all kinds of RSs (i.e., a holistic moral defense) and is based on a normative principle founded on the agency of the user, which I call the right to be an exception to predictions. Such a right holds that the proper treatment of a RS user qua agent forbids providing them with recommendations based only on their past or similar users' choices.

AbstractTrust and monitoring are traditionally antithetical concepts. Describing trust as a property of a relationship of reliance, we introduce a theory of trust and monitoring, which uses mathematical models based on two classes of functions, including q-exponentials, and relates the levels of trust to the costs of monitoring. As opposed to several accounts of trust that attempt to identify the special ingredient of reliance and trust relationships, our theory characterizes trust as a quantitative property of certain relations of reliance that can be quantified and expressed as a scalar quantity. Our theory is applicable to both human–human and human–artificial agent interactions, as it is agnostic with respect to the concrete realization of trustworthiness properties, and is compatible with many views differing on which properties contribute to trust and trustworthiness. Finally, as our mathematical models make the quantitative features of trust measurable, they provide empirical studies on trust with a rigorous methodology for its measurement.

Abstract
Real engines of the artificial intelligence (AI) revolution, machine learning (ML) models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo's concept of "e-trust," we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs.