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1. Applying the Agent-Deed-Consequence (ADC) Model to Smart City Ethics

   https://doi.org/10.3390/a18100625  

   Shussett, Daniel; Dubljević, Veljko (October 2025, Algorithms)

   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. 

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2. Morality on the road: the ADC model in low-stakes traffic vignettes

   https://doi.org/10.3389/fpsyg.2025.1508763  

   Pflanzer, Michael; Cecchini, Dario; Cacace, Sam; Dubljević, Veljko (June 2025, Frontiers in Psychology)

   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. 

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3. Fostering Ethical AI Integration in First-Year Writing: A Case Study on Human-Tool Collaboration in Artificial Intelligence Literacy

   https://doi.org/10.1080/01930826.2025.2468136  

   Michalak, Russell; Ellixson, Devon (April 2025, Journal of Library Administration)

   As artificial intelligence (AI) tools become integral to education, fostering ethical engagement and academic integrity is paramount. This column explores the integration of AI tools, including Grammarly, Scholarcy, and ImageFX, into a first-year writing program to promote ethical AI literacy. Grounded in Lo’s CLEAR framework, structured exercises emphasized critical thinking, intellectual agency, and human-AI collaboration. The study highlights the potential for AI to enhance learning while addressing challenges such as equity, accessibility, and ethical considerations. Findings demonstrate how intentional integration of AI tools fosters creativity, self-reflection, and responsible decision-making, providing a scalable model for incorporating AI into curricula while preserving student agency and integrity. 

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4. Ten questions concerning Large Language Models (LLMs) for building applications

   https://doi.org/10.1016/j.buildenv.2026.114260  

   Ma, Nan; Labib, Rania; Amor, Robert; Chong, Adrian; Fan, Cheng; Forth, Kasimir; Fu, Xiaoqin; Fuchs, Stefan; Hong, Tianzhen; Klimenkova, Nina; et al (March 2026, Building and Environment)

   Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation. 

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5. Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare

   https://doi.org/10.1038/s41746-023-00965-x  

   Oniani, David; Hilsman, Jordan; Peng, Yifan; Poropatich, Ronald K.; Pamplin, Jeremy C.; Legault, Gary L.; Wang, Yanshan (December 2023, npj Digital Medicine)

   Abstract In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the “GREAT PLEA” ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Autonomy for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice. 

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