In response to the many social impacts of automated mobility, in September 2020 the European Commission published Ethics of Connected and Automated Vehicles, a report in which recommendations on road safety, privacy, fairness, explainability, and responsibility are drawn from a set of eight overarching principles. This paper presents the results of an interdisciplinary research where philosophers and engineers joined efforts to operationalize the guidelines advanced in the report. To this aim, we endorse a function-based working approach to support the implementation (...) of values and recommendations into the design of automated vehicle technologies. Based on this, we develop methodological tools to tackle issues related to personal autonomy, explainability, and privacy as domains that most urgently require fine-grained guidance due to the associated ethical risks. Even though each tool still requires further inquiry, we believe that our work might already prove the productivity of the function-based approach and foster its adoption in the CAV scientific community. (shrink)

An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, what is more properly called (...) ‘fair’ or ‘unfair’ is a decision system, not a prediction model. This is because fairness is about the consequences on human lives, created by a decision, not by a prediction. In this paper, we clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. As well as discussing this relationship both from a conceptual and a practical point of view, we propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making. In our framework, we specify different roles, namely the ‘prediction-modeler’ and the ‘decision-maker,’ and the information required from each of them for being able to implement fairness of the system. Our framework allows for deriving distinct responsibilities for both roles and discussing some insights related to ethical and legal requirements. Our contribution is twofold. First, we offer a new perspective shifting the focus from an abstract concept of algorithmic fairness to the concrete context-dependent nature of algorithmic decision-making, where different actors exist, can have different goals, and may act independently. In addition, we provide a conceptual framework that can help structure prediction-based decision problems with respect to fairness issues, identify responsibilities, and implement fairness governance mechanisms in real-world scenarios. (shrink)

This article tries to shed light on the difference between human autonomy and AI-driven machine autonomy. The breadth of the studies concerning this topic is constantly increasing, and for this reason, this discussion is very narrow and limited in its extent. Indeed, its hypothesis is that it is possible to distinguish two kinds of autonomy by analysing the way humans and robots narrate stories and the types of stories that, respectively, result from their capability of narrating stories on their own. (...) To approach the question of autonomy and its different interpretations in robots and humans, using narratives and stories as a lens, I proceed through four steps. First, I provide a brief summary concerning the state of the art with regard to issues concerning autonomy and AI. Second, I focus on specific issues concerning AI that are applied to storytelling. Third, I progressively approach narrative autonomy through what could seem a useless digression by addressing the problem of human creativity in relation to machine creativity. Fourth, I hypothesize a radical difference between narrative autonomy—which is proper to humans—and storytelling capability, which can be observed in both humans and machines. (shrink)

Currently, the autonomy of artificial systems, robotic systems in particular, is certainly one of the most debated issues, both from the perspective of technological development and its social impact and ethical repercussions. While theoretical considerations often focus on scenarios far beyond what can be concretely hypothesized from the current state of the art, the term autonomy is still used in a vague or too general way. This reduces the possibilities of a punctual analysis of such an important issue, thus leading (...) to often polarized positions. The intent of this paper is to clarify what is meant by artificial autonomy, and what are the prerequisites that can allow the attribution of this characteristic to a robotic system. Starting from some concrete examples, we will try to indicate a way towards artificial autonomy that can hold together the advantages of developing adaptive and versatile systems with the management of the inevitable problems that this technology poses both from the viewpoint of safety and ethics. Our proposal is that a real artificial autonomy, especially if expressed in the social context, can only be achieved through interdependence with other social actors, through continuous exchanges and interactions which, while allowing robots to explore the environment, guarantee the emergence of shared practices, behaviors, and ethical principles, which otherwise could not be imposed with a top-down approach, if not at the price of giving up the same artificial autonomy. (shrink)

There is a growing body of scholarship on the ethics of autonomous vehicles. Yet the ethical discourse has mostly been focusing on the behavior of the vehicle in accident scenarios. This paper offers a different ethical prism: the implications of the autonomous vehicle for human well-being. As such, it contributes to the growing discourse on the wider societal and moral implications of the autonomous vehicle. The paper is premised on the neo-Aristotelian approach which holds that as human beings, our well-being (...) depends on developing and exercising our innate human capacities: to know, understand, love, be sociable, imagine, create and use our bodies and use our willpower. To develop and exercise these capacities, our environments need to provide a range of opportunities which will trigger the development and exercise of the capacities. The main argument advanced in the paper is that one plausible future of the autonomous vehicle—a future of single-rider autonomous vehicles—may effectively reduce the opportunities to develop and exercise our capacities to know, be sociable and use our willpower. It will therefore be bad for human well-being, and this provides us with a moral reason to resist this plausible future and search for alternative ones. (shrink)

Although related studies have examined the impact of different images of artificial intelligence products on consumer evaluation, exploring the impact on consumer purchase decisions from the perspective of algorithmic decision autonomy remains under-explored. Based on the self-determination theory, this research discusses the influence of the agent decision-making role played by different AI algorithmic decision autonomy on consumer purchase decisions. The results of the 3 studies indicate that algorithmic decision autonomy has an inverted U-shaped effect on consumer’s purchase decisions, consumer’s self-efficacy (...) mediates the relationship between algorithmic decision autonomy and purchase decisions, and consumer’s power distance moderates the relationship between algorithmic decision autonomy, self-efficacy, and purchase decisions. The research results can provide references for marketers, retailers, algorithm designers, and other parties to formulate algorithm marketing strategies, make AI algorithm decisions better serve consumers, and achieve value co-creation with consumers. (shrink)