Urban Digital Twins (UDTs) have become the new buzzword for researchers, planners, policymakers, and industry experts when it comes to designing, planning, and managing sustainable and efficient cities. It encapsulates the last iteration of the technocratic and ultra-efficient, post-modernist vision of smart cities. However, while more applications branded as UDTs appear around the world, its conceptualization remains ambiguous. Beyond being technically prescriptive about what UDTs are, this article focuses on their aspects of interaction and operationalization in connection to people in (...) cities, and how enhanced by metaverse ideas they can deepen societal divides by offering divergent urban experiences based on different stakeholder preferences. Therefore, firstly this article repositions the term UDTs by comparing existing concrete and located applications that have a focus on interaction and participation, including some that may be closer to the concept of UDT than is commonly assumed. Based on the components found separately in the different studied cases, it is possible to hypothesize about possible future, more advanced realizations of UDTs. This enables us to contrast their positive and negative societal impacts. While the development of new immersive interactive digital worlds can improve planning using collective knowledge for more inclusive and diverse cities, they pose significant risks not only the common ones regarding privacy, transparency, or fairness, but also social fragmentation based on urban digital multiplicities. The potential benefits and challenges of integrating this multiplicity of UDTs into participatory urban governance emphasize the need for human-centric approaches to promote socio-technical frameworks able to mitigate risks as social division. (shrink)

In a bid to boost their below-replacement fertility levels, some countries, such as China, India, Iran, and Japan, have launched state-sponsored dating apps, with more potentially following. However, the use of dating apps as tools for social engineering has been largely neglected by political theorists and public policy experts. This article fills this gap. While acknowledging the risks and historical baggage of social engineering, the article provides a qualified defense of using these apps for three purposes: raising below-replacement birth rates, (...) increasing social cohesion, and preventing certain genetic diseases. (shrink)

Despite the widespread use of automated decision-making (ADM) systems, they are often developed without involving the public or those directly affected, leading to concerns about systematic biases that may perpetuate structural injustices. Existing formal fairness approaches primarily focus on statistical outcomes across demographic groups or individual fairness, yet these methods reveal ambiguities and limitations in addressing fairness comprehensively. This paper argues for a holistic approach to algorithmic fairness that integrates procedural fairness, considering both decision-making processes and their outcomes. Procedural fairness (...) emphasizes the importance of fair decision-making procedures, which aligns with theories of relational justice that stress the quality of social relations and power dynamics. We highlight the need for substantive procedural fairness to ensure better outcomes and address forward-looking responsibilities. Additionally, we propose leveraging Public Engagement, a core dimension within the well-established Responsible Research and Innovation framework, to enhance procedural fairness in ADM systems. Our contribution underscores the value of Public Engagement in fostering fairer ADM processes, thereby expanding the current focus beyond technical outcome-based approaches to encompass broader procedural considerations. (shrink)

Who is responsible for a harm caused by AI, or a machine or system that relies on artificial intelligence? Given that current AI is neither conscious nor sentient, it’s unclear that AI itself is responsible for it. But given that AI acts independently of its developer or user, it’s also unclear that the developer or user is responsible for the harm. This gives rise to the so-called responsibility gap: cases where AI causes a harm, but no one is responsible for (...) it. Two central questions in the literature are whether responsibility gaps exist, and if yes, whether they’re morally problematic in a way that counts against developing or using AI. While some authors argue that responsibility gaps exist, and they’re morally problematic, some argue that they don’t exist. In this paper, I defend a novel position. First, I argue that current AI doesn’t generate a new kind of concern about responsibility that the older technologies don’t. Then, I argue that responsibility gaps exist but they’re unproblematic. (NOTE: Email me for a copy.). (shrink)

This paper examines the ongoing challenges of interdisciplinary collaboration in Machine Ethics (ME), particularly the integration of ethical decision-making capacities into AI systems. Despite increasing demands for ethical AI, ethicists often remain on the sidelines, contributing primarily to metaethical discussions without directly influencing the development of moral machines. This paper revisits concerns highlighted by Tolmeijer et al. (2020), who identified the pitfall that computer scientists may misinterpret ethical theories without philosophical input. Using the MACHIAVELLI moral benchmark and the Delphi artificial (...) moral agent as case studies, we analyze how these challenges persist. Our analysis indicates that the creators of MACHIAVELLI and Delphi “copy” ethical concepts and embed them in LLMs without questioning or challenging these concepts themselves sufficiently. If an ethical concept causes friction with the computer code, they only reduce and simplify the ethical concept in order to stay as close as possible to the original. We propose that ME should expand its focus to include both interdisciplinary efforts that embed existing ethical work into AI, and transdisciplinary research that fosters new interpretations of ethical concepts. Interdisciplinary and transdisciplinary approaches are crucial for creating AI systems that are not only effective but also socially responsible. To enhance collaboration between ethicists and computer scientists, we recommend the use of Socratic Dialogue as a methodological tool, promoting deeper understanding of key terms and more effective integration of ethics in AI development. (shrink)

In this article, firstly, cooperative interaction between robots and humans is discussed; specifically, the possibility of human/robot joint action and (relatedly) the possibility of robots occupying institutional roles alongside humans. The discussion makes use of concepts developed in social ontology. Secondly, certain key moral (or ethical—these terms are used interchangeably here) issues arising from this cooperative action are discussed, specifically issues that arise from robots performing (including qua role occupants) morally significant actions jointly with humans. Such morally significant human/robot joint (...) actions, supposing they exist, could potentially range from humans and robots jointly caring for the infirm through to jointly killing enemy combatants. (shrink)

Research under real-world conditions is crucial to the development and deployment of robust AI systems. Exposing large language models to complex use settings yields knowledge about their performance and impact, which cannot be obtained under controlled laboratory conditions or through anticipatory methods. This epistemic need for real-world research is exacerbated by large-language models’ opaque internal operations and potential for emergent behavior. However, despite its epistemic value and widespread application, the ethics of real-world AI research has received little scholarly attention. To (...) address this gap, this paper provides an analysis of real-world research with LLMs and generative AI, assessing both its epistemic value and ethical concerns such as the potential for interpersonal and societal research harms, the increased privatization of AI learning, and the unjust distribution of benefits and risks. This paper discusses these concerns alongside four moral principles influencing research ethics standards: non-maleficence, beneficence, respect for autonomy, and distributive justice. I argue that real-world AI research faces challenges in meeting these principles and that these challenges are exacerbated by absent or imperfect current ethical governance. Finally, I chart two distinct but compatible ways forward: through ethical compliance and regulation and through moral education and cultivation. (shrink)

The paper develops a conception of the good life within a digitalized society. Martha Nussbaum’s capability theory offers an adequate normative framework for that purpose as it systematically integrates the analysis of flourishing human lives with a normative theory of justice. The paper argues that a theory of good digital lives should focus on everyday life, on the impact digitalization has on ordinary actions, routines and corresponding practical knowledge. Based on Nussbaum’s work, the paper develops a concept of digital capabilities. (...) Digital capabilities are combined capabilities: To possess a digital capability, an individual must acquire certain skills and abilities (internal capabilities) and needs access to devices and external infrastructures like internet connections. If societies as a whole and everyday environments are digitalized to a certain degree, the possession of specific digital capabilities is a crucial precondition for a flourishing life. The paper likewise analyzes challenges that are connected to digital capabilities. Digital structures are constantly changing. In consequence, digital capabilities are never acquired once and for all, but always precarious and in danger of being lost—with serious consequences for individual everyday lives in digitalized environments. As digital capabilities are crucial for leading a good life, people are entitled to develop and maintain them. They describe demands of justice. Using the examples of filling in an online form and digital education, the paper finally illustrates the size of institutional changes that are necessary to meet these demands. (shrink)

Explanations are conceived to ensure the trustworthiness of AI systems. Yet, relying solemnly on algorithmic solutions, as provided by explainable artificial intelligence (XAI), might fall short to account for sociotechnical risks jeopardizing their factuality and informativeness. To mitigate these risks, we delve into the complex landscape of ethical risks surrounding XAI systems and their generated explanations. By employing a literature review combined with rigorous thematic analysis, we uncover a diverse array of technical risks tied to the robustness, fairness, and evaluation (...) of XAI systems. Furthermore, we address a broader range of contextual risks jeopardizing their security, accountability, reception alongside other cognitive, social, and ethical concerns of explanations. We advance a multi-layered risk assessment framework, where each layer advances strategies for practical intervention, management, and documentation of XAI systems within organizations. Recognizing the theoretical nature of the framework advanced, we discuss it in a conceptual case study. For the XAI community, our multifaceted investigation represents a path to practically address XAI risks while enriching our understanding of the ethical ramifications of incorporating XAI in decision-making processes. (shrink)

With the rapid expansion of parameters in large language models (LLMs) and the application of Reinforcement Learning with Human Feedback (RLHF), there has been a noticeable growth in the moral competence of LLMs. However, several questions warrant further exploration: Is it really possible for LLMs to fully align with human values through RLHF? How can the current moral growth be philosophically contextualized? We identify similarities between LLMs’ moral growth and Deweyan ethics in terms of the discourse of human moral development. (...) We then attempt to use Dewey’s theory on an experimental basis to examine and further explain the extent to which the current alignment pathway enables the development of LLMs. A beating experiment serves as the foundational case for analyzing LLMs’ moral competence across various parameters and stages, including basic moral cognition, moral dilemma judgment, and moral behavior. The results demonstrate that the moral competence of the GPT series has seen a significant improvement, and Dewey’s Impulse-Habit-Character theory of moral development can be used to explain this: the moral competence of LLMs has been enhanced through experience-based learning, supported by human feedback. Nevertheless, LLMs’ moral development through RLHF remains constrained and does not reach the character stage described by Dewey, possibly due to their lack of self-consciousness. This fundamental difference between humans and LLMs underscores both the limitations of LLMs’ moral growth and the challenges of applying RLHF for AI alignment. It also emphasizes the need for external societal governance and legal regulation. (shrink)