Visual social media platforms have become primary venues for political discourse, yet we know little about how moralization operates differently across platforms and topics. Analyzing 2,027,595 TikToks and 1,126,972 Instagram posts during the 2024 US presidential election, we demonstrate that issues are not necessarily inherently moralized, but a product of audience demographics, platform architecture, and partisan framing. Using temporal supply-demand analysis and moral foundations scoring (eMFD), we examine the dynamics of key electoral issues. Three key findings emerge. First, moralization patterns diverge dramatically by platform: TikTok's algorithm enabled viral spread of moralized abortion and immigration content despite lower supply, while Instagram amplified economic discourse that aligned supply and demand. Second, traditionally "pragmatic" economic issues became moralized-cryptocurrency discourse invoked loyalty and authority foundations more strongly than any other topic, framing regulation as government overreach. Third, platforms responded to different events: TikTok surged after Harris's nomination across all topics (96% reduction in supply volatility), while Instagram spiked around cryptocurrency policy developments. Semantic network analysis reveals TikTok's circular topology enables cross-cutting exposure while Instagram's fragmented structure isolates Harris from economic discourse. These findings demonstrate that understanding political moralization requires examining platform-specific ecosystems where architecture, demographics, and content strategy interact to determine which issues get moralized and how moral content spreads.
We present an ethical decision-making framework that refines a pre-trained reinforcement learning (RL) model using a task-agnostic ethical layer. Following initial training, the RL model undergoes ethical fine-tuning, where human feedback is replaced by feedback generated from a large language model (LLM). The LLM embodies consequentialist, deontological, virtue, social justice, and care ethics as moral principles to assign belief values to recommended actions during ethical decision-making. An ethical layer aggregates belief scores from multiple LLM-derived moral perspectives using Belief Jensen-Shannon Divergence and Dempster-Shafer Theory into probability scores that also serve as the shaping reward, steering the agent toward choices that align with a balanced ethical framework. This integrated learning framework helps the RL agent navigate moral uncertainty in complex environments and enables it to make morally sound decisions across diverse tasks. Our approach, tested across different LLM variants and compared with other belief aggregation techniques, demonstrates improved consistency, adaptability, and reduced reliance on handcrafted ethical rewards. This method is especially effective in dynamic scenarios where ethical challenges arise unexpectedly, making it well-suited for real-world applications.
The MEVIR 2 framework innovates and improves how we understand trust decisions in our polarized information landscape. Unlike classical models assuming ideal rationality, MEVIR 2 recognizes that human trust emerges from three interacting foundations: how we process evidence procedurally, our character as epistemic agents virtue theory, and our moral intuitions shaped by both evolutionary cooperation MAC model and cultural values Extended Moral Foundations Theory. This explains why different people find different authorities, facts, and tradeoffs compelling. MEVIR 2's key innovation introduces "Truth Tribes" TTs-stable communities sharing aligned procedural, virtue, and moral epistemic profiles. These arent mere ideological groups but emergent clusters with internally coherent "trust lattices" that remain mutually unintelligible across tribal boundaries. The framework incorporates distinctions between Truth Bearers and Truth Makers, showing disagreements often stem from fundamentally different views about what aspects of reality can make propositions true. Case studies on vaccination mandates and climate policy demonstrate how different moral configurations lead people to select different authorities, evidential standards, and trust anchors-constructing separate moral epistemic worlds. The framework reinterprets cognitive biases as failures of epistemic virtue and provides foundations for designing decision support systems that could enhance metacognition, make trust processes transparent, and foster more conscientious reasoning across divided communities. MEVIR 2 thus offers both descriptive power for understanding polarization and normative guidance for bridging epistemic divides.
In this work, we propose Perspective Reasoning for Integrated Synthesis and Mediation (PRISM), a multiple-perspective framework for addressing persistent challenges in AI alignment such as conflicting human values and specification gaming. Grounded in cognitive science and moral psychology, PRISM organizes moral concerns into seven "basis worldviews", each hypothesized to capture a distinct dimension of human moral cognition, ranging from survival-focused reflexes through higher-order integrative perspectives. It then applies a Pareto-inspired optimization scheme to reconcile competing priorities without reducing them to a single metric. Under the assumption of reliable context validation for robust use, the framework follows a structured workflow that elicits viewpoint-specific responses, synthesizes them into a balanced outcome, and mediates remaining conflicts in a transparent and iterative manner. By referencing layered approaches to moral cognition from cognitive science, moral psychology, and neuroscience, PRISM clarifies how different moral drives interact and systematically documents and mediates ethical tradeoffs. We illustrate its efficacy through real outputs produced by a working prototype, applying PRISM to classic alignment problems in domains such as public health policy, workplace automation, and education. By anchoring AI deliberation in these human vantage points, PRISM aims to bound interpretive leaps that might otherwise drift into non-human or machine-centric territory. We briefly outline future directions, including real-world deployments and formal verifications, while maintaining the core focus on multi-perspective synthesis and conflict mediation.
Large language models (LLMs) are increasingly used in high-stakes settings, where explaining uncertainty is both technical and ethical. Probabilistic methods are often opaque and misaligned with expectations of transparency. We propose a framework based on rule-based moral principles for handling uncertainty in LLM-generated text. Using insights from moral psychology and virtue ethics, we define rules such as precaution, deference, and responsibility to guide responses under epistemic or aleatoric uncertainty. These rules are encoded in a lightweight Prolog engine, where uncertainty levels (low, medium, high) trigger aligned system actions with plain-language rationales. Scenario-based simulations benchmark rule coverage, fairness, and trust calibration. Use cases in clinical and legal domains illustrate how moral reasoning can improve trust and interpretability. Our approach offers a transparent, lightweight alternative to probabilistic models for socially responsible natural language generation.
Public-sector bureaucracies seek to reap the benefits of artificial intelligence (AI), but face important concerns about accountability and transparency when using AI systems. In particular, perception or actuality of AI agency might create ethics sinks - constructs that facilitate dissipation of responsibility when AI systems of disputed moral status interface with bureaucratic structures. Here, we reject the notion that ethics sinks are a necessary consequence of introducing AI systems into bureaucracies. Rather, where they appear, they are the product of structural design decisions across both the technology and the institution deploying it. We support this claim via a systematic application of conceptions of moral agency in AI ethics to Weberian bureaucracy. We establish that it is both desirable and feasible to render AI systems as tools for the generation of organizational transparency and legibility, which continue the processes of Weberian rationalization initiated by previous waves of digitalization. We present a three-point Moral Agency Framework for legitimate integration of AI in bureaucratic structures: (a) maintain clear and just human lines of accountability, (b) ensure humans whose work is augmented by AI systems can verify the systems are functioning correctly, and (c) introduce AI only where it doesn't inhibit the capacity of bureaucracies towards either of their twin aims of legitimacy and stewardship. We suggest that AI introduced within this framework can not only improve efficiency and productivity while avoiding ethics sinks, but also improve the transparency and even the legitimacy of a bureaucracy.
Vijay Keswani, Vincent Conitzer, Walter Sinnott-Armstrong
et al.
A growing body of work in Ethical AI attempts to capture human moral judgments through simple computational models. The key question we address in this work is whether such simple AI models capture {the critical} nuances of moral decision-making by focusing on the use case of kidney allocation. We conducted twenty interviews where participants explained their rationale for their judgments about who should receive a kidney. We observe participants: (a) value patients' morally-relevant attributes to different degrees; (b) use diverse decision-making processes, citing heuristics to reduce decision complexity; (c) can change their opinions; (d) sometimes lack confidence in their decisions (e.g., due to incomplete information); and (e) express enthusiasm and concern regarding AI assisting humans in kidney allocation decisions. Based on these findings, we discuss challenges of computationally modeling moral judgments {as a stand-in for human input}, highlight drawbacks of current approaches, and suggest future directions to address these issues.
This study examines the impact of moral framing on fundraising outcomes, including both monetary and social support, by analyzing a dataset of 14,088 campaigns posted on GoFundMe. We focused on three moral frames: care, fairness, and (ingroup) loyalty, and measured their presence in campaign appeals. Our results show that campaigns in the Emergency category are most influenced by moral framing. Generally, negatively framing appeals by emphasizing harm and unfairness effectively attracts more donations and comments from supporters. However, this approach can have a downside, as it may lead to a decrease in the average donation amount per donor. Additionally, we found that loyalty framing was positively associated with receiving more donations and messages across all fundraising categories. This research extends existing literature on framing and communication strategies related to fundraising and their impact. We also propose practical implications for designing features of online fundraising platforms to better support both fundraisers and supporters.
Vamshi Krishna Bonagiri, Sreeram Vennam, Manas Gaur
et al.
A Large Language Model (LLM) is considered consistent if semantically equivalent prompts produce semantically equivalent responses. Despite recent advancements showcasing the impressive capabilities of LLMs in conversational systems, we show that even state-of-the-art LLMs are highly inconsistent in their generations, questioning their reliability. Prior research has tried to measure this with task-specific accuracy. However, this approach is unsuitable for moral scenarios, such as the trolley problem, with no "correct" answer. To address this issue, we propose a novel information-theoretic measure called Semantic Graph Entropy (SGE) to measure the consistency of an LLM in moral scenarios. We leverage "Rules of Thumb" (RoTs) to explain a model's decision-making strategies and further enhance our metric. Compared to existing consistency metrics, SGE correlates better with human judgments across five LLMs. In the future, we aim to investigate the root causes of LLM inconsistencies and propose improvements.
Takeshi Kato, Yosuke Tanabe, Mohammad Rezoanul Hoque
Reducing wealth inequality and increasing utility are critical issues. This study reveals the effects of redistribution and consumption morals on wealth inequality and utility. To this end, we present a novel approach that couples the dynamic model of capital, consumption, and utility in macroeconomics with the interaction model of joint business and redistribution in econophysics. With this approach, we calculate the capital (wealth), the utility based on consumption, and the Gini index of these inequality using redistribution and consumption thresholds as moral parameters. The results show that: under-redistribution and waste exacerbate inequality; conversely, over-redistribution and stinginess reduce utility; and a balanced moderate moral leads to achieve both reduced inequality and increased utility. These findings provide renewed economic and numerical support for the moral importance known from philosophy, anthropology, and religion. The revival of redistribution and consumption morals should promote the transformation to a human mutual-aid economy, as indicated by philosopher and anthropologist, instead of the capitalist economy that has produced the current inequality. The practical challenge is to implement bottom-up social business, on a foothold of worker coops and platform cooperatives as a community against the state and the market, with moral consensus and its operation.
Many studies have identified particular features of artificial intelligences (AI), such as their autonomy and emotion expression, that affect the extent to which they are treated as subjects of moral consideration. However, there has not yet been a comparison of the relative importance of features as is necessary to design and understand increasingly capable, multi-faceted AI systems. We conducted an online conjoint experiment in which 1,163 participants evaluated descriptions of AIs that varied on these features. All 11 features increased how morally wrong participants considered it to harm the AIs. The largest effects were from human-like physical bodies and prosociality (i.e., emotion expression, emotion recognition, cooperation, and moral judgment). For human-computer interaction designers, the importance of prosociality suggests that, because AIs are often seen as threatening, the highest levels of moral consideration may only be granted if the AI has positive intentions.
Models like Delphi have been able to label ethical dilemmas as moral or immoral with astonishing accuracy. This paper challenges accuracy as a holistic metric for ethics modeling by identifying issues with translating moral dilemmas into text-based input. It demonstrates these issues with contrast sets that substantially reduce the performance of classifiers trained on the dataset Moral Stories. Ultimately, we obtain concrete estimates for how much specific forms of data misrepresentation harm classifier accuracy. Specifically, label-changing tweaks to the descriptive content of a situation (as small as 3-5 words) can reduce classifier accuracy to as low as 51%, almost half the initial accuracy of 99.8%. Associating situations with a misleading social norm lowers accuracy to 98.8%, while adding textual bias (i.e. an implication that a situation already fits a certain label) lowers accuracy to 77%. These results suggest not only that many ethics models have substantially overfit, but that several precautions are required to ensure that input accurately captures a moral dilemma. This paper recommends re-examining the structure of a social norm, training models to ask for context with defeasible reasoning, and filtering input for textual bias. Doing so not only gives us the first concrete estimates of the average cost to accuracy of misrepresenting ethics data, but gives researchers practical tips for considering these estimates in research.
ABSTRACT The authors analyze the biblical roots of human responsibility for the earthly environment, and the forms of moral despoilment in the Bible that are later applied to environmental destruction. They then take the reader on an ecotheological journey of the Inner Planets Earth’s Moon and Mars. For each location, authors explore (1) the planetary science, (2) human adaptation to those conditions, and (3) the future roles of religion, theology, and ecotheology. Religions and theologies borrowed from earthly populations will play important roles in helping to manage human off-world settlements, and in providing hope, education, social constraints, and values for governance.
Student formation is a key missional goal for Catholic colleges and universities. In many instances this involves requiring coursework in theology and ethics that exposes students to the essentials of the tradition and key themes of Christian life such as vocation, marriage, family, work, and society. And yet, historically we fail to consider the changing landscape of life in community, especially for women and other gender minorities. The personhood of women and reproductive autonomy, both topics that have been tangled in the culture war of the abortion debate, is a particular challenge to the goal of formation. The US Supreme Court decision overturning Roe v. Wade provides a fresh opportunity to examine how we might better attend to student formation in ways that more accurately and faithfully incorporate women’s personhood and reproductive autonomy.
Intersectional approaches aim to uncover the multidimensionality of multiply burdened victimhood rather than a single-axis analysis. When intersected with Asian and Asian American postcolonial experiences and perspectives, intersectionality exposes the global reach and colonial origins of white imperialism and ideological, systemic, and mutational nature of white supremacist logic of dominance connected to global capitalism, neocolonialism, class-ism, ableism, caste-ism, racism, etc. While limited and inadequate by itself, there is a family resemblance of intersectional method in the Catholic theological and intellectual tradition that is inclusive of ordinary and marginalized voices and secular disciplines. For Catholic theologians and the church, then, intersectional approach can be instrumental in dismantling white supremacy within the church as an institution and people. Indeed, the intersectional nature of Jesus’ ministry, personhood, and location further solidifies the case for using intersectional theology for ecclesial and personal introspection, growth, and development.
The development of students' scientific thinking in the field of akidah akhlak (moral theology) is very urgent, and for that process, a science-based project learning method is needed. This study uses a phenomenological approach to explore the involvement of faith and identity processes of madrasah aliyah teachers in developing science-based project learning methods, involving twenty moral theology teachers, conducted in-depth interviews to reveal the narrative of teachers' practice in using science-based project learning methods. Thematic analysis of two-group interviews with 20 teachers showed that teachers' personal beliefs provided a religiously-motivated narrative framework that facilitated the interpretation of one's experiences. The involvement of personal faith and religiosity, identity processes when teaching, plays a role in the development of science-based project learning methods on moral theology. The application of Islamic principles and faith is the main bond in the development of science-based project learning methods and attribution of identity from God-given personality to learning in moral theology. Identity processes, faith, and scientific thinking of students develop when following the learning of moral theology. In conclusion, this exploratory study shows that faith and identity processes in personal can improve science-based project learning methods. In the future, large-scale research could provide further evidence to reconsider the role of religious education in teacher training as an important factor in developing science-based project learning methods for teachers of moral theology.
Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski
et al.
Massively multilingual sentence representations are trained on large corpora of uncurated data, with a very imbalanced proportion of languages included in the training. This may cause the models to grasp cultural values including moral judgments from the high-resource languages and impose them on the low-resource languages. The lack of data in certain languages can also lead to developing random and thus potentially harmful beliefs. Both these issues can negatively influence zero-shot cross-lingual model transfer and potentially lead to harmful outcomes. Therefore, we aim to (1) detect and quantify these issues by comparing different models in different languages, (2) develop methods for improving undesirable properties of the models. Our initial experiments using the multilingual model XLM-R show that indeed multilingual LMs capture moral norms, even with potentially higher human-agreement than monolingual ones. However, it is not yet clear to what extent these moral norms differ between languages.