Real engines of the artificial intelligence revolution, machine learning 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. (shrink)

In his recent article ‘Limits of trust in medical AI,’ Hatherley argues that, if we believe that the motivations that are usually recognised as relevant for interpersonal trust have to be applied to interactions between humans and medical artificial intelligence, then these systems do not appear to be the appropriate objects of trust. In this response, we argue that it is possible to discuss trust in medical artificial intelligence, if one refrains from simply assuming that trust describes human–human interactions. To (...) do so, we consider an account of trust that distinguishes trust from reliance in a way that is compatible with trusting non-human agents. In this account, to trust a medical AI is to rely on it with little monitoring and control of the elements that make it trustworthy. This attitude does not imply specific properties in the AI system that in fact only humans can have. This account of trust is applicable, in particular, to all cases where a physician relies on the medical AI predictions to support his or her decision making. (shrink)

In this paper we argue that transparency of machine learning algorithms, just as explanation, can be defined at different levels of abstraction. We criticize recent attempts to identify the explanation of black box algorithms with making their decisions (post-hoc) interpretable, focusing our discussion on counterfactual explanations. These approaches to explanation simplify the real nature of the black boxes and risk misleading the public about the normative features of a model. We propose a new form of algorithmic transparency, that consists in (...) explaining algorithms as an intentional product, that serves a particular goal, or multiple goals (Daniel Dennet’s design stance) in a given domain of applicability, and that provides a measure of the extent to which such a goal is achieved, and evidence about the way that measure has been reached. We call such idea of algorithmic transparency “design publicity.” We argue that design publicity can be more easily linked with the justification of the use and of the design of the algorithm, and of each individual decision following from it. In comparison to post-hoc explanations of individual algorithmic decisions, design publicity meets a different demand (the demand for impersonal justification) of the explainee. Finally, we argue that when models that pursue justifiable goals (which may include fairness as avoidance of bias towards specific groups) to a justifiable degree are used consistently, the resulting decisions are all justified even if some of them are (unavoidably) based on incorrect predictions. For this argument, we rely on John Rawls’s idea of procedural justice applied to algorithms conceived as institutions. (shrink)

The widespread use of algorithms for prediction-based decisions urges us to consider the question of what it means for a given act or practice to be discriminatory. Building upon work by Kusner and colleagues in the field of machine learning, we propose a counterfactual condition as a necessary requirement on discrimination. To demonstrate the philosophical relevance of the proposed condition, we consider two prominent accounts of discrimination in the recent literature, by Lippert-Rasmussen and Hellman respectively, that do not logically imply (...) our condition and show that they face important objections. Specifically, Lippert-Rasmussen’s definition proves to be over-inclusive, as it classifies some acts or practices as discriminatory when they are not, whereas Hellman’s account turns out to lack explanatory power precisely insofar as it does not countenance a counterfactual condition on discrimination. By defending the necessity of our counterfactual condition, we set the conceptual limits for justified claims about the occurrence of discriminatory acts or practices in society, with immediate applications to the ethics of algorithmic decision-making. (shrink)

Other-perspective taking, distancing, time discounting as well as risk and loss aversion highly affect decision-making. Even though they influence each other, so far these cognitive processes have been unrelated or only partly related to each other in neuroscience. This article proposes a philosophical interpretation of these cognitive processes that is elaborated in the updated theory of Adam Smith’s prudence. The UTSP is inspired by Smith’s account of prudence and is in line with the neuroscientific and behavioral studies on OPT, distancing, (...) time discounting as well as risk and loss aversion. The UTSP represents a framework aiming to interpret and connect these cognitive processes and providing a consistent and empirically sound account of a “Smithianly” prudent style of decision-making. The two pillars of the UTSP are the shift of perspective in space and time and loss aversion. On the basis of this theory, a normative updated theory of Smithian prudence is outlined. The latter is useful for moral philosophy for two reasons: firstly, according to preliminary evidence, it is effective in guiding the agent in decisional contexts in which her well-being is at stake and she can affect other people with her actions. Secondly, since the NUTSP is based on neuroscientific findings on decision-making, it has a sound empirical basis that prevents a source of alienation for the agent. (shrink)

Recommendation 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. (shrink)

Trust 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. (shrink)

In the initial online publication, the second author’s given name should have been displayed in full and in the running head the names were not properly abbreviated.

Moral dilemmas have long been debated in moral philosophy without reaching a definitive consensus. The majority of value pluralists attribute their origin to the incommensurability of moral values, i.e. the statement that, since moral values are many and different in nature, they may conflict and cannot be compared. Neuroscientific studies on the neural common currency show that the comparison between allegedly incompatible alternatives is a practical possibility, namely it is the basis of the way in which the agent evaluates choice (...) options. Indeed, both in economic and moral decision-making, the value of options is represented and directly compared in the ventromedial prefrontal cortex. Therefore, we contend that moral dilemmas do not originate from value incommensurability and, on the basis of the neuroscientific discoveries on the neural currency, we derive the implications for the philosophical debate on moral dilemmas. We also provide a possible connection between the experience of moral dilemmas and their neural representation: one of the causes of the individual’s indecision is the neural tie, i.e. the condition in which two options have the same value at neural level, and her regret could be due to the motivational force of the rejected option that is still signalled by affective processes in the brain. We apply this interpretation and the common currency hypothesis to vocational decisions and propose that, although from the agent’s perspective the options are qualitatively different, they may be nevertheless equivalent at neural level. This can be seen as a reason for downgrading the importance commonly attributed to the risk of making the “wrong choice”. (shrink)