The rapid development of AI in recent years has brought the problem of AI's moral status to the fore. In this article, we combine Afro-communitarian ethics with a cognitive perspective and argue that some AI can hold a moral status to the extent that it can be both a subject and an object of communion. Further, different kinds of AI have different degrees of moral status, depending on their communal capacities. To demonstrate this, we show that AI can engage in (...) moral behavior and be morally disposed to act in certain ways. We defend that this theory deserves consideration along with other theories of moral status. (shrink)

We outline a four-step process for ML/AI developers to align development choices with multiple values, by adapting a widely-utilized framework from bioethics: (1) identify the values that matter, (2) specify identified values, (3) find solution spaces that allow for maximal alignment with identified values, and 4) make hard choices if there are unresolvable trade-offs between the identified values. Key to this approach is identifying resolvable trade-offs between values (Step 3). We survey ML/AI methods that could be used to this end, (...) identifying approaches at each stage of the development process. All steps should be guided by community engagement. The framework outlines what it means to build a value-aligned ML/AI system, providing development teams with practical guidance to maximize the chances their work has desirable impacts. (shrink)

A number of concerns have been recently raised regarding the possibility of human agents to effectively maintain control over intelligent and (partially) autonomous artificial systems. These issues have been deemed to raise “responsibility gaps.” To address these gaps, several scholars and other public and private stakeholders converged towards the idea that, in deploying intelligent technology, a meaningful form of human control (MHC) should be at all times exercised over autonomous intelligent technology. One of the main criticisms of the general idea (...) of MHC is that it could be inherently problematic to have high degrees of control and high degrees of autonomy at the same time, as the two dimensions appear to be inversely related. Several ways to respond to this argument and deal with the dilemma between control and autonomy have been proposed in the literature. In this paper, we further contribute to the philosophical effort to overcome the trade-off between automation and human control, and to open up some space for moral responsibility. We will use the instrument of conceptual engineering to investigate whether and to what extent removing the element of direct causal intervention from the concept of control can preserve the main functions of that concept, specifically focusing on the extent it can act as a foundation of moral responsibility. We show that at least one philosophical account of MHC is indeed a conceptually viable theory to absolve the fundamental functions of control, even in the context of completely autonomous artificial systems. (shrink)

A central question in AI ethics concerns the moral status of robots. This article argues against the idea that they have moral status. It proceeds by defending the assumption that consciousness is necessary for welfare subjectivity. Since robots most likely lack consciousness, and welfare subjectivity is necessary for moral status, it follows that robots lack moral status. The assumption that consciousness is necessary for welfare subjectivity appears to be in tension with certain widely accepted theories of wellbeing, especially versions of (...) Desire Satisfaction Theory and Objective List Theory. However, instead of elevating non-conscious robots to welfare subjects, this tension should lead us to reject versions of these theories that have this implausible implication. (shrink)

While most contemporary proposals of ethics for machines draw upon principle-based ethics, a number of recent studies attempt to build machines capable of acting virtuously. This paper discusses the promises and limitations of building virtue-ethical machines. Taking inspiration from various philosophical traditions—including Greek philosophy (Aristotle), Chinese philosophy (Zhuangzi), phenomenology (Hubert and Stuart Dreyfus) and contemporary virtue theory (Julia Annas)—we argue for a novel model of machine ethics we call the “skillful-expert model.” This model sharply distinguishes human virtues and their machine (...) analogues and rejects the direct transposition of human virtues to machines, as suggested by most proposals for machine virtues. Instead, we propose that human skills are better candidates for machine virtues. (shrink)

Human emotional expressions can communicate the emotional state of the expresser, but they can also communicate appeals to perceivers. For example, sadness expressions such as crying request perceivers to aid and support, and anger expressions such as shouting urge perceivers to back off. Some contemporary artificial intelligence (AI) systems can mimic human emotional expressions in a (more or less) realistic way, and they are progressively being integrated into our daily lives. How should we respond to them? Do we have reasons (...) to reply to the appeals made by AI emotional expressions? In this paper, we examine the conditions under which AI emotional expressions could give us prudential or even moral reasons to change our behavior. We argue that these conditions do not depend on whether the emotional expression is genuine or not, but rather on the presence of features some of which can be implemented in emotive AI given our current level of technological development. We extract recommendations and warnings for the development of emotive AI. (shrink)

Emotive artificial intelligences are physically or virtually embodied entities whose behavior is driven by artificial intelligence, and which use expressions usually associated with emotion to enhance communication. These entities are sometimes thought to be deceptive, insofar as their emotive expressions are not connected to genuine underlying emotions. In this paper, I argue that such entities are indeed deceptive, at least given a sufficiently broad construal of deception. But, while philosophers and other commentators have drawn attention to the deceptive threat of (...) emotive artificial intelligences, I argue that such entities also pose an overlooked skeptical threat. In short, the widespread existence of emotive signals disconnected from underlying emotions threatens to encourage skepticism of such signals more generally, including emotive signals used by human persons. Thus, while designing artificially intelligent entities to use emotive signals is thought to facilitate human-AI interaction, this practice runs the risk of compromising human-human interaction. (shrink)

In this article, I investigate whether algorithmic fairness is an inconsistent concept (the inconsistency thesis). Drawing on the work of Kevin Scharp, inconsistent concepts can apply and disapply at the same time (2.). It is shown that paradigmatic issues of algorithmic fairness fit this description (3.). Similarities and differences to received views (4.) and alternatives to the inconsistency thesis are considered (5.). Suggestions are articulated on how the inconsistency thesis might hold ground nevertheless, or at the very least denotes a (...) distinctive option in argumentative space whose status and implications merit further evaluation. (shrink)

We propose a conceptualization and implementation of AI ethics via the capability approach. We aim to show that conceptualizing AI ethics through the capability approach has two main advantages for AI ethics as a discipline. First, it helps clarify the ethical dimension of AI tools. Second, it provides guidance to implementing ethical considerations within the design of AI tools. We illustrate these advantages in the context of AI tools in medicine, by showing how ethics-based auditing of AI tools in medicine (...) can greatly benefit from our capability-based approach. (shrink)

The literature on the ethics of machine learning in healthcare contains a great deal of work on algorithmic fairness. But a focus on fairness has not been matched with sufficient attention to the relationship between machine learning and distributive justice in healthcare. A significant number of clinical prediction models have been developed which could be used to inform the allocation of scarce healthcare resources. As such, philosophical theories of distributive justice are relevant when considering the ethics of their design and (...) implementation. This paper considers the relationship between machine learning in healthcare and distributive justice with a focus on four aspects of algorithmic design and deployment: the choice of target variable, the model's socio-technical context, the choice of input variables, and the membership of the datasets that models are trained and validated on. Procedural recommendations for how these considerations should be accounted for in the design and implementation of such models follow. (shrink)

The application of AI in healthcare has dramatically changed the practice of medicine. In particular, AI has been implemented in a variety of roles that previously required human physicians. Due to AI's ability to outperform humans in these roles, the concern has been raised that AI will completely replace human physicians in the future. In this paper, it is argued that human physician's ability to embellish the truth is necessary to prevent injury or grief to patients, or to protect patients’ (...) feelings. Such embellishments are usually defined as “white lies,” which not only include intentionally providing false, incomplete, or misleading information to patients, but also withholding information. White lies are not uncommon in medical practice, especially in some particular cultures, such as Asia; and to some particular patients, such as children. Here, white lies are defended based on the position of weak paternalism. And although medical AI may surpass human physicians in diagnostic accuracy, efficiency, and treatment, they cannot intentionally lie to patients, which is necessary to prevent injury or grief or to protect patients’ feelings in some cases. From this perspective, it is further argued that AI cannot completely replace human physicians. (shrink)