Mark Coeckelbergh (Int J Soc Robot 1:217–221, 2009) argues that robot ethics should investigate what interaction with robots can do to humans rather than focusing on the robot’s moral status. We should ask what robots do to our sociality and whether human–robot interaction can contribute to the human good and human flourishing. This paper extends Coeckelbergh’s call and investigate what it means to live with disembodied AI-powered agents. We address the following question: Can the human–AI interaction contribute to our moral (...) development? We present an empirically informed philosophical analysis of how the AI personal assistant Siri changes its users’ way of life, based on the responses obtained from 20 semi-directive individual interviews with Siri users. We identify changes in the users’ social interaction associated with the adoption of Siri. These changes include: (1) the indirect effect of reducing opportunities of human interaction, (2) the second-order effect of diminished expectations toward each other in a community, and (3) the acquired preference to obtain hassle-free interaction with Siri over human interaction. We examine them in relation to concerns that are voiced in the current debates over the rise of AI, namely the suspicion that humans could become overly reliant on AI (Danaher 2019) and the worry that social AI could impede on moral development (Fröding and Peterson, Ethics Inf Technol 23:207–214, 2012; Li, Ethics Inf Technol 23:543–550, 2021). We analyze the ethical costs that come from these changes in light of virtue ethics and address potential objections along the way. We end by offering directions for thinking about how to live with AI personal assistant while preserving favorable conditions for moral development. (shrink)

This chapter examines the possibility of using AI technologies to improve human moral reasoning and decision-making, especially in the context of purchasing and consumer decisions. We characterize such AI technologies as artificial ethics assistants (AEAs). We focus on just one part of the AI-aided moral improvement question: the case of the individual who wants to improve their morality, where what constitutes an improvement is evaluated by the individual’s own values. We distinguish three broad areas in which an individual might think (...) their own moral reasoning and decision-making could be improved: one’s actions, character, or other evaluable attributes fall short of one’s values and moral beliefs; one sometimes misjudges or is uncertain about what the right thing to do is in particular situations, given one’s values; one is uncertain about some fundamental moral questions or recognizes a possibility that some of one’s core moral beliefs and values are mistaken. We sketch why one might think that AI tools could be used to support moral improvement in those areas, and describe two types of assistance: preparatory assistance, including advice and training supplied in advance of moral deliberation; and on-the-spot assistance, including on-the-spot advice and facilitation of moral functioning over the course of moral deliberation. Then, we turn to some of the ethical issues that AEAs might raise, looking in particular at three under-appreciated problems posed by the use of AI for moral self-improvement: namely, reliance on sensitive moral data; the inescapability of outside influences on AEAs; and AEA usage prompting the user to adopt beliefs and make decisions without adequate reasons. (shrink)

This article offers a critical inquiry of contemporary neural network models as an instance of machine learning, from an interdisciplinary perspective of AI studies and performativity. It shows the limits on the architecture of these network systems due to the misemployment of ‘natural’ performance, and it offers ‘context’ as a variable from a performative approach, instead of a constant. The article begins with a brief review of machine learning-based natural language processing systems and continues with a concentration on the relevant (...) model of recurrent neural networks, which is applied in most commercial research such as Facebook AI Research. It demonstrates that the logic of performativity is not brought into account in all recurrent nets, which is an integral part of human performance and languaging, and it argues that recurrent network models, in particular, fail to grasp human performativity. This logic works similarly to the theory of performativity articulated by Jacques Derrida in his critique of John L. Austin’s concept of the performative. Applying Jacques Derrida’s work on performativity, and linguistic traces as spatially organized entities that allow for this notion of performance, the article argues that recurrent nets fall into the trap of taking ‘context’ as a constant, of treating human performance as a ‘natural’ fix to be encoded, instead of performative. Lastly, the article applies its proposal more concretely to the case of Facebook AI Research’s Alice and Bob. (shrink)

The literature on ethics and user attitudes towards AVs discusses user concerns in relation to automation; however, we show that there are additional relevant issues at stake. To assess adolescents’ attitudes regarding the ‘car of the future’ as presented by car manufacturers, we conducted two studies with over 400 participants altogether. We used a mixed methods approach in which we combined qualitative and quantitative methods. In the first study, our respondents appeared to be more concerned about other aspects of AVs (...) than automation. Instead, their most commonly raised concerns were the extensive use of AI, recommender systems, and related issues of autonomy, invasiveness and personal privacy. The second study confirmed that several AV impacts were negatively perceived. The responses were, however, ambivalent. This confirms previous research on AV attitudes. On one hand, the AV features were perceived as useful, while on the other hand, their impacts were negatively assessed. We followed theoretical insights form futures studies and responsible research and innovation, which helped to identify that there are additional user concerns than what has been previously discussed in the literature on public attitudes and ethics of AVs, as well what has been envisioned by car manufactures. (shrink)

In a landmark essay, Andreas Matthias claimed that current developments in autonomous, artificially intelligent systems are creating a so-called responsibility gap, which is allegedly ever-widening and stands to undermine both the moral and legal frameworks of our society. But how severe is the threat posed by emerging technologies? In fact, a great number of authors have indicated that the fear is thoroughly instilled. The most pessimistic are calling for a drastic scaling-back or complete moratorium on AI systems, while the optimists (...) aim to show that the gap can be bridged nonetheless. Contrary to both camps, I argue against the prevailing assumption that there is a technology-based responsibility gap. I show how moral responsibility is a dynamic and flexible process, one that can effectively encompass emerging technological entities. (shrink)

This paper presents an in-depth literature review on the driving forces and barriers for achieving operational excellence through artificial intelligence. Artificial intelligence is a technological concept spanning operational management, philosophy, humanities, statistics, mathematics, computer sciences, and social sciences. AI refers to machines mimicking human behavior in terms of cognitive functions. The evolution of new technological procedures and advancements in producing intelligence for machines creates a positive impact on decisions, operations, strategies, and management incorporated in the production process of goods and (...) services. Businesses develop various methods and solutions to extract meaningful information, such as big data, automatic production capabilities, and systematization for business improvement. The progress in organizational competitiveness is apparent through improvements in firm’s decisions, resulting in increased operational efficiencies. Innovation with AI has enabled small businesses to reduce operating expenses and increase revenues. The focused literature review reveals the driving forces for achieving operational excellence through AI are improvement in computing abilities of machines, development of data-based AI, advancements in deep learning, cloud computing, data management, and integration of AI in operations. The barriers are mainly cultural constraints, fear of the unknown, lack of employee skills, and strategic planning for adopting AI. The current paper presents an analysis of articles focused on AI adoption in production and operations. We selected articles published between 2015 and 2020. Our study contributes to the literature reviews on operational excellence, artificial intelligence, driving forces for AI, and AI barriers in achieving operational excellence. (shrink)

As commercial virtual assistants become an integrated part of almost every smart device that we use on a daily basis, including but not limited to smartphones, speakers, personal computers, watches, TVs, and TV sticks, there are pressing questions that call for the study of how participants perceive commercial virtual assistants and what relational roles they assign to them. Furthermore, it is crucial to study which characteristics of commercial virtual assistants are perceived as important for establishing affective interaction with commercial virtual (...) assistants. By conducting 26 interviews and performing content analysis of the interview transcripts, this study investigates how the participants in the study perceive, engage, and interact with a variety of commercial virtual assistants. The results lead to better understanding of whether forms of attachment are established or if some sort of relationship is produced between humans and commercial virtual assistants. Key takeaways from our results indicate that, in their current state, the lack of humanlike characteristics in commercial virtual assistants prevents users from forming an emotional attachment to commercial virtual assistants, but this does not deter them from using anthropomorphic language to describe commercial virtual assistants. Yet, our results reveal that users expect commercial virtual assistants’ attributes to be more humanlike in the future. (shrink)

Sequential transformative design of research :224–235, 2015; Groleau et al. in J Mental Health 16:731–741, 2007; Robson and McCartan in Real world research: a resource for users of social research methods in applied settings, Wiley, Chichester, 2016) allows testing a group of theoretical assumptions about the connections of artificial intelligence with culture and education. In the course of research, semiotics ensures the description of self-organizing systems of cultural signs and symbols in terms of artificial intelligence as a special set of (...) algorithms. This approach helps to consider the arguments proposed by Searle :417–457, 1980) against ‘strong’ artificial intelligence. Searle :417–457, 1980) believes that artificial or machine intelligence cannot fully emulate the processes of the human mind. Machine intelligence shows own inevitable weakness. This is non-autonomous tool for computations and data operating. In fact, this tool cannot provide insight into real cognitive conditions. After Lotman and Uspensky, authors expand the meaning of artificial intelligence. The authors identify a cultural type of ‘strong’ artificial intelligence or ‘self-increase of Logos’ in terms by Lotman and Uspensky. The interpretation of human intelligence as imitation of machine intelligence makes possible such immersion of artificial intelligence in culture. The authors reveal a case of self-organizing autonomous generation, encoding, decoding, reception, storage, and transmission of social information in the field of physical training. From the empirical studies it is clear that the organization of collective activities without external control ensures the development of positive emotions and social orientations. Interest in autonomous behavior provides the formation of educational and cognitive motives. As a special set of algorithms, these motives are the most promising and favorable for personal development. (shrink)

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of (...) individuals correlate in practice with different modes of human–computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia. (shrink)

The abundance of literature on ethical concerns regarding artificial intelligence (AI) highlights the need to systematize, integrate, and categorize existing efforts through a systematic literature review. The article aims to investigate prevalent concerns, proposed solutions, and prominent ethical approaches within the field. Considering 309 articles from the beginning of the publications in this field up until December 2021, this systematic literature review clarifies what the ethical concerns regarding AI are, and it charts them into two groups: (i) ethical concerns that (...) arise from the design of AI and (ii) ethical concerns that arise from human–AI interactions. The analysis of the obtained sample highlights the most recurrent ethical concerns. Finally, it exposes the main proposals of the literature to handle the ethical concerns according to the main ethical approaches. It interprets the findings to lay the foundations for future research on the ethics of AI. (shrink)

Value-sensitive design theorists propose that a range of values that should inform how future social robots are engineered. This article explores a new value: digital well-being, and proposes that the next generation of social robots should be designed to facilitate this value in those who use or come into contact with these machines. To do this, I explore how the morphology of social robots is closely connected to digital well-being. I argue that a key decision is whether social robots are (...) designed as embodied or disembodied. After exploring the merits of both approaches, I conclude that, on balance, there are persuasive reasons why disembodied social robots may well fare better with respect to the value of digital well-being. (shrink)

Recommender Systems (RecSys) have been around since the early days of the Internet, helping users navigate the vast ocean of information and the increasingly available options that have been available for us ever since. The range of tasks for which one could use a RecSys is expanding as the technical capabilities grow, with the disruption of Machine Learning representing a tipping point in this domain, as in many others. However, the increase of the technical capabilities of AI-powered RecSys did not (...) come with a thorough consideration of their ethical implications and, despite being a well-established technical domain, the potential impacts of RecSys on their users are still under-assessed. This paper aims at filling this gap in regards to one of the main impacts of RecSys: personal autonomy. We first describe how technology can affect human values and a suitable methodology to identify these effects and mitigate potential harms: Value Sensitive Design (VSD). We use VSD to carry out a conceptual investigation of personal autonomy in the context of a generic RecSys and draw on a nuanced account of procedural autonomy to focus on two components: competence and authenticity. We provide the results of our inquiry as a value hierarchy and apply it to the design of a speculative RecSys as an example. (shrink)

The idea that technologies can change moral beliefs and practices is an old one. But how, exactly, does this happen? This paper builds on an emerging field of inquiry by developing a synoptic taxonomy of the mechanisms of techno-moral change. It argues that technology affects moral beliefs and practices in three main domains: decisional (how we make morally loaded decisions), relational (how we relate to others) and perceptual (how we perceive situations). It argues that across these three domains there are (...) six primary mechanisms of techno-moral change: (i) adding options; (ii) changing decision-making costs; (iii) enabling new relationships; (iv) changing the burdens and expectations within relationships; (v) changing the balance of power in relationships; and (vi) changing perception (information, mental models and metaphors). The paper also discusses the layered, interactive and second-order effects of these mechanisms. (shrink)

Deep learning AI systems have proven a wide capacity to take over human-related activities such as car driving, medical diagnosing, or elderly care, often displaying behaviour with unpredictable consequences, including negative ones. This has raised the question whether highly autonomous AI may qualify as morally responsible agents. In this article, we develop a set of four conditions that an entity needs to meet in order to be ascribed moral responsibility, by drawing on Aristotelian ethics and contemporary philosophical research. We encode (...) these conditions and generate a flowchart that we call the Moral Responsibility Test. This test can be used as a tool both to evaluate whether an entity is a morally responsible agent and to inform human moral decision-making over the influencing variables of the context of action. We apply the test to the case of Artificial Moral Advisors and conclude that this form of AI cannot qualify as morally responsible agents. We further discuss the implications for the use of AMAs as moral enhancement and show that using AMAs to offload human responsibility is inadequate. We argue instead that AMAs could morally enhance users if they are interpreted as enablers for moral knowledge of the contextual variables surrounding human moral decision-making, with the implication that such a use might actually enlarge human moral responsibility. (shrink)

What ethical principles should autonomous machines follow? How do we implement these principles, and how do we evaluate these implementations? These are some of the critical questions Vivek Nallur asks in his essay “Landscape of Machine Implemented Ethics (2020).” He provides a broad, insightful survey of answers to these questions, especially focused on the implementation question. In this commentary, I will first critically summarize the main themes and conclusions of Nallur’s essay and then expand upon the landscape that Nallur presents (...) by suggesting additional approaches to machine ethics. The approaches I discuss reflect normative ethical theories and need not be applied only to machines, although machine ethics is the focus here. The overall goal is to open up further questions and research possibilities as society searches for the best approach to machine ethics. (Due to an unfortunate miscommunication with the copy editor an important reference was omitted from this article. The reference that should be included is: Nallur, V. (2020). Landscape of Machine Implemented Ethics. Science and Engineering Ethics 26 (5):2381-2399.). (shrink)

What is the ethical impact of artificial intelligence assistants on human lives, and specifically how much do they threaten our individual autonomy? Recently, as part of forming an ethical framework for thinking about the impact of AI assistants on our lives, John Danaher claims that if the external automaticity generated by the use of AI assistants threatens our autonomy and is therefore ethically problematic, then the internal automaticity we already live with should be viewed in the same way. He takes (...) advantage of this paradox of internal automaticity to downplay the threats of external automaticity to our autonomy. We respond in this paper by challenging the legitimacy of the paradox. While Danaher assumes that internal and external automaticity are roughly equivalent, we argue that there are reasons why we should accept a large degree of internal automaticity, that it is actually essential to our sense of autonomy, and as such it is ethically good; however, the same does not go for external automaticity. Therefore, the similarity between the two is not as powerful as the paradox presumes. In conclusion, we make practical recommendations for how to better manage the integration of AI assistants into society. (shrink)

It is commonly assumed that a virtual life would be less meaningful (perhaps even meaningless). As virtual reality technologies develop and become more integrated into our everyday lives, this poses a challenge for those that care about meaning in life. In this chapter, it is argued that the common assumption about meaninglessness and virtuality is mistaken. After clarifying the distinction between two different visions of virtual reality, four arguments are presented for thinking that meaning is possible in virtual reality. Following (...) this, four objections are discussed and rebutted. The chapter concludes that we can be cautiously optimistic about the possibility of meaning in virtual worlds. (shrink)

This study concerns the sociotechnical bases of human autonomy. Drawing on recent literature on AI ethics, philosophical literature on dimensions of autonomy, and on independent philosophical scrutiny, we first propose a multi-dimensional model of human autonomy and then discuss how AI systems can support or hinder human autonomy. What emerges is a philosophically motivated picture of autonomy and of the normative requirements personal autonomy poses in the context of algorithmic systems. Ranging from consent to data collection and processing, to computational (...) tasks and interface design, to institutional and societal considerations, various aspects related to sociotechnical systems must be accounted for in order to get the full picture of potential effects of AI systems on human autonomy. It is clear how human agents can, for example, via coercion or manipulation, hinder each other’s autonomy, or how they can respect each other’s autonomy. AI systems can promote or hinder human autonomy, but can they literally respect or disrespect a person’s autonomy? We argue for a philosophical view according to which AI systems—while not moral agents or bearers of duties, and unable to literally respect or disrespect—are governed by so-called “ought-to-be norms.” This explains the normativity at stake with AI systems. The responsible people (designers, users, etc.) have duties and ought-to-do norms, which correspond to these ought-to-be norms. (shrink)