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IntroductionIn recent years, the ethical implications of traffic decision-making, particularly in the context of autonomous vehicles (AVs), have garnered significant attention. While much of the existing research has focused on high-stakes moral dilemmas, such as those exemplified by the trolley problem, everyday traffic situations—characterized by mundane, low-stakes decisions—remain underexplored. MethodsThis study addresses this gap by empirically investigating the applicability of the Agent-Deed-Consequences (ADC) model in the moral judgment of low-stakes traffic scenarios. Using a vignette approach, we surveyed professional philosophers to examine how their moral judgments are influenced by the character of the driver (Agent), their adherence to traffic rules (Deed), and the outcomes of their actions (Consequences). ResultsOur findings support the primary hypothesis that each component of the ADC model significantly influences moral judgment, with positive valences in agents, deeds, and consequences leading to greater moral acceptability. We additionally explored whether participants’ normative ethical leanings–classified as deontological, utilitarian, or virtue ethics–influenced how they weighted ADC components. However, no moderating effects of moral preference were observed. The results also reveal interaction effects among some components, illustrating the complexity of moral reasoning in traffic situations. DiscussionThe study’s implications are crucial for the ethical programming of AVs, suggesting that these systems should be designed to navigate not only high-stakes dilemmas but also the nuanced moral landscape of everyday driving. Our work creates a foundation for stakeholders to integrate human moral judgments into AV decision-making algorithms. Future research should build on these findings by including a more diverse range of participants and exploring the generalizability of the ADC model across different cultural contexts. 

Abstract Successful implementation of autonomous vehicle (AV) technology is not only an engineering challenge but also a social, political, and ethical one. As AVs become commonplace and begin affecting people’s daily lives in a more profound way, media coverage of the social and ethical considerations of these technologies will follow suit. We seek to analyze and categorize the media’s portrayal of the social and ethical issues surrounding AVs to better understand how these issues shape public debate. Our research employs a qualitative thematic analysis of existing public discourse, identifying six areas of interest: (1) social and ethical issues; (2) ethical frameworks; (3) recommendations; (4) tone; (5) type of AV technology; and (6) sensationalism. These themes were derived through a combination of inductive and deductive analysis, informed by existing literature on AV ethics and media framing. Through this approach, we aimed to identify salient considerations in AV technology development and inform future AV systems decisions. Our contributions to the study of public visibility seek to enhance product development and the implementation of AVs, which will largely be steered by public discourse. 

Smart cities are an emerging technology that is receiving new ethical attention due to recent advancements in artificial intelligence. This paper provides an overview of smart city ethics while simultaneously performing novel theorization about the definition of smart cities and the complicated relationship between (smart) cities, ethics, and politics. We respond to these ethical issues by providing an innovative representation of the agent-deed-consequence (ADC) model in symbolic terms through deontic logic. The ADC model operationalizes human moral intuitions underpinning virtue ethics, deontology, and utilitarianism. With the ADC model made symbolically representable, human moral intuitions can be built into the algorithms that govern autonomous vehicles, social robots in healthcare settings, and smart city projects. Once the paper has introduced the ADC model and its symbolic representation through deontic logic, it demonstrates the ADC model’s promise for algorithmic ethical decision-making in four dimensions of smart city ethics, using examples relating to public safety and waste management. We particularly emphasize ADC-enhanced ethical decision-making in (economic and social) sustainability by advancing an understanding of smart cities and human-AI teams (HAIT) as group agents. The ADC model has significant merit in algorithmic ethical decision-making, especially through its elucidation in deontic logic. Algorithmic ethical decision-making, if structured by the ADC model, successfully addresses a significant portion of the perennial questions in smart city ethics, and smart cities built with the ADC model may in fact be a significant step toward resolving important social dilemmas of our time. 

The imminent deployment of autonomous vehicles requires algorithms capable of making moral decisions in relevant traffic situations. Some scholars in the ethics of autonomous vehicles hope to align such intelligent systems with human moral judgment. For this purpose, studies like the Moral Machine Experiment have collected data about human decision-making in trolley-like traffic dilemmas. This paper first argues that the trolley dilemma is an inadequate experimental paradigm for investigating traffic moral judgments because it does not include agents’ character-based considerations and is incapable of facilitating the investigation of low-stakes mundane traffic scenarios. In light of the limitations of the trolley paradigm, this paper presents an alternative experimental framework that addresses these issues. The proposed solution combines the creation of mundane traffic moral scenarios using virtual reality and the Agent-Deed-Consequences (ADC) model of moral judgment as a moral-psychological framework. This paradigm shift potentially increases the ecological validity of future studies by providing more realism and incorporating character considerations into traffic actions. 

Integrating artificial intelligence (AI) technologies into law enforcement has become a concern of contemporary politics and public discourse. In this paper, we qualitatively examine the perspectives of AI technologies based on 20 semi-structured interviews of law enforcement professionals in North Carolina. We investigate how integrating AI technologies, such as predictive policing and autonomous vehicle (AV) technology, impacts the relationships between communities and police jurisdictions. The evidence suggests that police officers maintain that AI plays a limited role in policing but believe the technologies will continue to expand, improving public safety and increasing policing capability. Conversely, police officers believe that AI will not necessarily increase trust between police and the community, citing ethical concerns and the potential to infringe on civil rights. It is thus argued that the trends toward integrating AI technologies into law enforcement are not without risk. Policymaking guided by public consensus and collaborative discussion with law enforcement professionals must aim to promote accountability through the application of responsible design of AI in policing with an end state of providing societal benefits and mitigating harm to the populace. Society has a moral obligation to mitigate the detrimental consequences of fully integrating AI technologies into law enforcement. 

Introduction Moral judgment is of critical importance in the work context because of its implicit or explicit omnipresence in a wide range of work-place practices. The moral aspects of actual behaviors, intentions, and consequences represent areas of deep preoccupation, as exemplified in current corporate social responsibility programs, yet there remain ongoing debates on the best understanding of how such aspects of morality (behaviors, intentions, and consequences) interact. The ADC Model of moral judgment integrates the theoretical insights of three major moral theories (virtue ethics, deontology, and consequentialism) into a single model, which explains how moral judgment occurs in parallel evaluation processes of three different components: the character of a person (Agent-component); their actions (Deed-component); and the consequences brought about in the situation (Consequences-component). The model offers the possibility of overcoming difficulties encountered by single or dual-component theories. Methods We designed a 2 × 2 × 2-between-subjects design vignette experiment with a Germany-wide sample of employed respondents ( N = 1,349) to test this model. Results Results showed that the Deed-component affects willingness to cooperate in the work context, which is mediated via moral judgments. These effects also varied depending on the levels of the Agent- and Consequences-component. Discussion Thereby, the results exemplify the usefulness of the ADC Model in the work context by showing how the distinct components of morality affect moral judgment.