Hasil untuk "Moral theology"

Mayank Goel, Aritra Das, Paras Chopra

We build a custom transformer model to study how neural networks make moral decisions on trolley-style dilemmas. The model processes structured scenarios using embeddings that encode who is affected, how many people, and which outcome they belong to. Our 2-layer architecture achieves 77% accuracy on Moral Machine data while remaining small enough for detailed analysis. We use different interpretability techniques to uncover how moral reasoning distributes across the network, demonstrating that biases localize to distinct computational stages among other findings.

Aryan Kasat, Smriti Singh, Aman Chadha et al.

Do large language models reason morally, or do they merely sound like they do? We investigate whether LLM responses to moral dilemmas exhibit genuine developmental progression through Kohlberg's stages of moral development, or whether alignment training instead produces reasoning-like outputs that superficially resemble mature moral judgment without the underlying developmental trajectory. Using an LLM-as-judge scoring pipeline validated across three judge models, we classify more than 600 responses from 13 LLMs spanning a range of architectures, parameter scales, and training regimes across six classical moral dilemmas, and conduct ten complementary analyses to characterize the nature and internal coherence of the resulting patterns. Our results reveal a striking inversion: responses overwhelmingly correspond to post-conventional reasoning (Stages 5-6) regardless of model size, architecture, or prompting strategy, the effective inverse of human developmental norms, where Stage 4 dominates. Most strikingly, a subset of models exhibit moral decoupling: systematic inconsistency between stated moral justification and action choice, a form of logical incoherence that persists across scale and prompting strategy and represents a direct reasoning consistency failure independent of rhetorical sophistication. Model scale carries a statistically significant but practically small effect; training type has no significant independent main effect; and models exhibit near-robotic cross-dilemma consistency producing logically indistinguishable responses across semantically distinct moral problems. We posit that these patterns constitute evidence for moral ventriloquism: the acquisition, through alignment training, of the rhetorical conventions of mature moral reasoning without the underlying developmental trajectory those conventions are meant to represent.

Simon Rosen, Siddarth Singh, Ebenezer Gelo et al.

Evaluating moral alignment in agents navigating conflicting, hierarchically structured human norms is a critical challenge at the intersection of AI safety, moral philosophy, and cognitive science. We introduce Morality Chains, a novel formalism for representing moral norms as ordered deontic constraints, and MoralityGym, a benchmark of 98 ethical-dilemma problems presented as trolley-dilemma-style Gymnasium environments. By decoupling task-solving from moral evaluation and introducing a novel Morality Metric, MoralityGym allows the integration of insights from psychology and philosophy into the evaluation of norm-sensitive reasoning. Baseline results with Safe RL methods reveal key limitations, underscoring the need for more principled approaches to ethical decision-making. This work provides a foundation for developing AI systems that behave more reliably, transparently, and ethically in complex real-world contexts.

Lisa Dargasz

Reinforcement Learning is a machine learning methodology that has demonstrated strong performance across a variety of tasks. In particular, it plays a central role in the development of artificial autonomous agents. As these agents become increasingly capable, market readiness is rapidly approaching, which means those agents, for example taking the form of humanoid robots or autonomous cars, are poised to transition from laboratory prototypes to autonomous operation in real-world environments. This transition raises concerns leading to specific requirements for these systems - among them, the requirement that they are designed to behave ethically. Crucially, research directed toward building agents that fulfill the requirement to behave ethically - referred to as artificial moral agents(AMAs) - has to address a range of challenges at the intersection of computer science and philosophy. This study explores the development of reason-based artificial moral agents (RBAMAs). RBAMAs are build on an extension of the reinforcement learning architecture to enable moral decision-making based on sound normative reasoning, which is achieved by equipping the agent with the capacity to learn a reason-theory - a theory which enables it to process morally relevant propositions to derive moral obligations - through case-based feedback. They are designed such that they adapt their behavior to ensure conformance to these obligations while they pursue their designated tasks. These features contribute to the moral justifiability of the their actions, their moral robustness, and their moral trustworthiness, which proposes the extended architecture as a concrete and deployable framework for the development of AMAs that fulfills key ethical desiderata. This study presents a first implementation of an RBAMA and demonstrates the potential of RBAMAs in initial experiments.

Guangliang Liu, Haitao Mao, Bochuan Cao et al.

Large Language Models (LLMs) are able to improve their responses when instructed to do so, a capability known as self-correction. When instructions provide only a general and abstract goal without specific details about potential issues in the response, LLMs must rely on their internal knowledge to improve response quality, a process referred to as intrinsic self-correction. The empirical success of intrinsic self-correction is evident in various applications, but how and why it is effective remains unknown. Focusing on moral self-correction in LLMs, we reveal a key characteristic of intrinsic self-correction: performance convergence through multi-round interactions; and provide a mechanistic analysis of this convergence behavior. Based on our experimental results and analysis, we uncover the underlying mechanism of convergence: consistently injected self-correction instructions activate moral concepts that reduce model uncertainty, leading to converged performance as the activated moral concepts stabilize over successive rounds. This paper demonstrates the strong potential of moral self-correction by showing that it exhibits a desirable property of converged performance.

Jackson Trager, Francielle Vargas, Diego Alves et al.

Ensuring the moral reasoning capabilities of Large Language Models (LLMs) is a growing concern as these systems are used in socially sensitive tasks. Nevertheless, current evaluation benchmarks present two major shortcomings: a lack of annotations that justify moral classifications, which limits transparency and interpretability; and a predominant focus on English, which constrains the assessment of moral reasoning across diverse cultural settings. In this paper, we introduce MFTCXplain, a multilingual benchmark dataset for evaluating the moral reasoning of LLMs via multi-hop hate speech explanation using the Moral Foundations Theory. MFTCXplain comprises 3,000 tweets across Portuguese, Italian, Persian, and English, annotated with binary hate speech labels, moral categories, and text span-level rationales. Our results show a misalignment between LLM outputs and human annotations in moral reasoning tasks. While LLMs perform well in hate speech detection (F1 up to 0.836), their ability to predict moral sentiments is notably weak (F1 < 0.35). Furthermore, rationale alignment remains limited mainly in underrepresented languages. Our findings show the limited capacity of current LLMs to internalize and reflect human moral reasoning

Giuseppe Russo, Debora Nozza, Paul Röttger et al.

People increasingly rely on Large Language Models (LLMs) for moral advice, which may influence humans' decisions. Yet, little is known about how closely LLMs align with human moral judgments. To address this, we introduce the Moral Dilemma Dataset, a benchmark of 1,618 real-world moral dilemmas paired with a distribution of human moral judgments consisting of a binary evaluation and a free-text rationale. We treat this problem as a pluralistic distributional alignment task, comparing the distributions of LLM and human judgments across dilemmas. We find that models reproduce human judgments only under high consensus; alignment deteriorates sharply when human disagreement increases. In parallel, using a 60-value taxonomy built from 3,783 value expressions extracted from rationales, we show that LLMs rely on a narrower set of moral values than humans. These findings reveal a pluralistic moral gap: a mismatch in both the distribution and diversity of values expressed. To close this gap, we introduce Dynamic Moral Profiling (DMP), a Dirichlet-based sampling method that conditions model outputs on human-derived value profiles. DMP improves alignment by 64.3% and enhances value diversity, offering a step toward more pluralistic and human-aligned moral guidance from LLMs.

Gustavo Irrazábal

Thirty years ago, Pope John Paul II’s _Veritatis Splendor_ confronted what it considered a moral crisis in which freedom, understood as autonomy without limits, led to the denial of the objective truth, especially the truth of revelation and natural law. Therefore, it strongly reaffirmed the relationship of faith to moral law and the doctrine of intrinsically evil acts, unlawful in all circumstances. The text rejected so-called “pastoral solutions,” which postulated exceptions to moral absolutes by exaggerating the singularity of concrete situations. Pope Francis’s magisterium addresses a different problem: the inherent complexity and fragility of the human condition in this world. For this reason, he is primarily concerned with the dangers of rigorism and legalism, as can be seen in the exhortation _Gaudete et Exsultate_. The declaration _Dignitas Infinita_, with its unconditional condemnation of acts that violate human dignity, may open a way for overcoming the tension between the teachings of the two pontiffs and contributing to a renewal of moral theology and pastoral praxis which avoids the danger of both rigorism and overburdening personal consciences for lack of clear normative references.

Elizaveta Tennant, Stephen Hailes, Mirco Musolesi

Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents: a promising solution is the use of learning from experience, i.e., Reinforcement Learning. In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents. Many of the existing studies rely on simulated social dilemma environments to study the interactions of independent learning agents; however, they tend to ignore the moral heterogeneity that is likely to be present in societies of agents in practice. For example, at different points in time a single learning agent may face opponents who are consequentialist (i.e., focused on maximizing outcomes over time), norm-based (i.e., conforming to specific norms), or virtue-based (i.e., considering a combination of different virtues). The extent to which agents' co-development may be impacted by such moral heterogeneity in populations is not well understood. In this paper, we present a study of the learning dynamics of morally heterogeneous populations interacting in a social dilemma setting. Using an Iterated Prisoner's Dilemma environment with a partner selection mechanism, we investigate the extent to which the prevalence of diverse moral agents in populations affects individual agents' learning behaviors and emergent population-level outcomes. We observe several types of non-trivial interactions between pro-social and anti-social agents, and find that certain types of moral agents are able to steer selfish agents towards more cooperative behavior.

Torben Swoboda, Lode Lauwaert

The neutrality thesis holds that technology cannot be laden with values. This long-standing view has faced critiques, but much of the argumentation against neutrality has focused on traditional, non-smart technologies like bridges and razors. In contrast, AI is a smart technology increasingly used in high-stakes domains like healthcare, finance, and policing, where its decisions can cause moral harm. In this paper, we argue that artificial intelligence, particularly artificial agents that autonomously make decisions to pursue their goals, challenge the neutrality thesis. Our central claim is that the computational models underlying artificial agents can integrate representations of moral values such as fairness, honesty and avoiding harm. We provide a conceptual framework discussing the neutrality thesis, values, and AI. Moreover, we examine two approaches to designing computational models of morality, artificial conscience and ethical prompting, and present empirical evidence from text-based game environments that artificial agents with such models exhibit more ethical behavior compared to agents without these models. The findings support that AI can embody moral values, which contradicts the claim that all technologies are necessarily value-neutral.

Tessa Stanier, Hagyeong Shin

This study investigates whether division on political topics is mapped with the distinctive patterns of language use. We collect a total 145,832 Reddit comments on the abortion debate and explore the languages of subreddit communities r/prolife and r/prochoice. With consideration of the Moral Foundations Theory, we examine lexical patterns in three ways. First, we compute proportional frequencies of lexical items from the Moral Foundations Dictionary in order to make inferences about each group's moral considerations when forming arguments for and against abortion. We then create n-gram models to reveal frequent collocations from each stance group and better understand how commonly used words are patterned in their linguistic context and in relation to morality values. Finally, we use Latent Dirichlet Allocation to identify underlying topical structures in the corpus data. Results show that the use of morality words is mapped with the stances on abortion.

Vamshi Krishna Bonagiri, Sreeram Vennam, Priyanshul Govil et al.

Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally inconsistent in their generations, questioning their reliability (and trustworthiness in general). Prior works in LLM evaluation focus on developing ground-truth data to measure accuracy on specific tasks. However, for moral scenarios that often lack universally agreed-upon answers, consistency in model responses becomes crucial for their reliability. To address this issue, we propose an information-theoretic measure called Semantic Graph Entropy (SaGE), grounded in the concept of "Rules of Thumb" (RoTs) to measure a model's moral consistency. RoTs are abstract principles learned by a model and can help explain their decision-making strategies effectively. To this extent, we construct the Moral Consistency Corpus (MCC), containing 50K moral questions, responses to them by LLMs, and the RoTs that these models followed. Furthermore, to illustrate the generalizability of SaGE, we use it to investigate LLM consistency on two popular datasets -- TruthfulQA and HellaSwag. Our results reveal that task-accuracy and consistency are independent problems, and there is a dire need to investigate these issues further.

Muhammad Shahrul Zaim bin Ahmad, Kazuhiro Takemoto

The rapid advancement of Large Language Models (LLMs) and their potential integration into autonomous driving systems necessitates understanding their moral decision-making capabilities. While our previous study examined four prominent LLMs using the Moral Machine experimental framework, the dynamic landscape of LLM development demands a more comprehensive analysis. Here, we evaluate moral judgments across 52 different LLMs, including multiple versions of proprietary models (GPT, Claude, Gemini) and open-source alternatives (Llama, Gemma), to assess their alignment with human moral preferences in autonomous driving scenarios. Using a conjoint analysis framework, we evaluated how closely LLM responses aligned with human preferences in ethical dilemmas and examined the effects of model size, updates, and architecture. Results showed that proprietary models and open-source models exceeding 10 billion parameters demonstrated relatively close alignment with human judgments, with a significant negative correlation between model size and distance from human judgments in open-source models. However, model updates did not consistently improve alignment with human preferences, and many LLMs showed excessive emphasis on specific ethical principles. These findings suggest that while increasing model size may naturally lead to more human-like moral judgments, practical implementation in autonomous driving systems requires careful consideration of the trade-off between judgment quality and computational efficiency. Our comprehensive analysis provides crucial insights for the ethical design of autonomous systems and highlights the importance of considering cultural contexts in AI moral decision-making.

Guangliang Liu, Haitao Mao, Jiliang Tang et al.

Large Language Models (LLMs) are capable of producing content that perpetuates stereotypes, discrimination, and toxicity. The recently proposed moral self-correction is a computationally efficient method for reducing harmful content in the responses of LLMs. However, the process of how injecting self-correction instructions can modify the behavior of LLMs remains under-explored. In this paper, we explore the effectiveness of moral self-correction by answering three research questions: (1) In what scenarios does moral self-correction work? (2) What are the internal mechanisms of LLMs, e.g., hidden states, that are influenced by moral self-correction instructions? (3) Is intrinsic moral self-correction actually superficial in terms of reduced immorality in hidden states? We argue that self-correction can help LLMs find a shortcut to more morally correct output, rather than truly reducing the immorality stored in hidden states. Through empirical investigation with tasks of language generation and multi-choice question answering, we conclude:(i) LLMs exhibit good performance across both tasks, and self-correction instructions are particularly beneficial when the correct answer is already top-ranked; (ii) The morality levels in intermediate hidden states are strong indicators as to whether one instruction would be more effective than another; (iii) Based on our analysis of intermediate hidden states and task case studies of self-correction behaviors, we are first to propose the hypothesis that intrinsic moral self-correction is in fact superficial.

Merve Nur Tekeci Çakar, Mehmet Akif Ceyhan

Bu çalışmada, Almanya’da İslamofobi’nin artışında etkenler tespit edilmekte, bu etkenlerden biri olarak görülen Neo-selefî yapıların bu etkideki rolü analiz edilmektedir. İslamofobi, İslam dininden ve Müslümanlardan korkma, nefret etme, endişe duyma veya önyargılı olma olarak tanımlanabilir. Müslümanlardan korkma veya Müslümanlara düşmanlık besleme anlamında İslamofobi’nin kökenleri İslam’ın ilk dönemlerine kadar uzanmaktadır. Tarihi kökenlere bakıldığında bu düşmanlığın Kudüs’ün, Endülüs’ün ve Hristiyan dünyanın hüküm sürdüğü diğer toprakların Müslümanlar tarafından fethedilme süreçlerine kadar gittiği gözlemlenmektedir. Bu çalışmada ise son 20-30 yıllık süreç içerisinde görülen İslamofobi’nin artışındaki etkenlere odaklanılmıştır. Burada dönüm noktasını Amerika’da bir grup terörist tarafından gerçekleştirilen 11 Eylül 2001 saldırıları oluşturmaktadır. Bu saldırı, İslamofobi endüstrisi tarafından bütün Müslümanların terörizm ile ilişkilendirilmesine sebebiyet vermiştir. Almanya, İslamofobi’nin en belirgin şekilde arttığı ülkelerden biridir. İslamofobi’nin artışı, Müslüman toplulukların günlük yaşamlarında karşılaştıkları ayrımcılık ve dışlanmayı artırırken, toplumsal uyum ve entegrasyon çabalarını zayıflatmaktadır. Bu olgunun Almanya’daki etkilerini anlamak, sadece Müslüman toplulukların karşılaştıkları zorlukları değil, aynı zamanda toplumun geneline yayılan etkileri de ortaya koymak açısından önemlidir.Bu çalışma ile amaçlanan, batılı ülkeler genelinde İslamofobi’nin artışındaki etkileri tespit ederken, özellikle Almanya ölçeğinde bu etkenler içerisinde yer aldığını düşündüğümüz Neo-selefî grupların nefret ve şiddet içeren eylem ve söylemlerinin bu artıştaki etkisinin tespit edilmesidir. Bu selefî söylemlerin kelâmî bağlamı da konu çerçevesinde önem arz etmektedir. Neo-selefî grupların söylem, eylem ve faaliyetlerinin, batılı ülkelerde yaşayan yerel halklar nezdinde nasıl karşılandığı, korku ve endişe uyandırıp uyandırmadığı, Müslümanların ötekileştirilip dışlanarak potansiyel bir düşman olarak algılanıp algılanmadığı gibi hususlar analiz edilmeye çalışılmıştır. Bu çalışmada, literatür taraması ve analitik yöntemler kullanılmıştır. İslamofobi, Selefîlik ve Neo-selefîlik kavramları kelâmî perspektifle detaylı bir şekilde ele alınmış, sonrasında ise İslamofobi’nin artışındaki temel etkenler tespit edilmeye çalışılmıştır. Tespit edilen etkenlerin analiz ve değerlendirmesi yapıldıktan sonra, özel olarak Almanya’da faaliyet gösteren radikal Neo-selefî grupların liderlerinin / vaizlerinin söylemleri çerçevesinde, toplumda bu gruplara yönelik algı tespit edilmeye çalışılmış ve bu olumsuz İslam imajının İslamofobi’nin artışındaki etkileri ortaya konulmuştur.Çalışmada, Almanya’da Neo-selefî grupların söylem ve faaliyetlerinin, İslamofobi’nin artışında önemli bir rol oynadığı tespit edilmiştir. Neo- selefî yapıların radikal ve dışlayıcı söylemleri, Almanya’da yaşayan Müslümanların toplumsal entegrasyonunu zorlaştırmakta ve yerel halk arasında Müslümanlara yönelik korku ve önyargıları beslemektedir. Özellikle Pierre Vogel ve Hasan Dabbağ gibi Neo-Selafi vaizlerin, sosyal medya ve diğer platformlar üzerinden yürüttükleri nefret söylemleri, Almanya’da İslamofobi’nin artmasına katkıda bulunmaktadır. Bu durum, Müslümanların genel olarak terörizmle ilişkilendirilmesine ve toplumsal kutuplaşmanın artmasına neden olmaktadır.

Siyi Guo, Negar Mokhberian, Kristina Lerman

Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life. As interest in language and morality has grown, several ground truth datasets with moral annotations have been released. However, these datasets vary in the method of data collection, domain, topics, instructions for annotators, etc. Simply aggregating such heterogeneous datasets during training can yield models that fail to generalize well. We describe a data fusion framework for training on multiple heterogeneous datasets that improve performance and generalizability. The model uses domain adversarial training to align the datasets in feature space and a weighted loss function to deal with label shift. We show that the proposed framework achieves state-of-the-art performance in different datasets compared to prior works in morality inference.

Shamik Roy, Nishanth Sridhar Nakshatri, Dan Goldwasser

Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge. As a result, few-shot identification of these concepts is desirable. Few-shot in-context learning using pre-trained Large Language Models (LLMs) has been recently applied successfully in many NLP tasks. In this paper, we study few-shot identification of a psycho-linguistic concept, Morality Frames (Roy et al., 2021), using LLMs. Morality frames are a representation framework that provides a holistic view of the moral sentiment expressed in text, identifying the relevant moral foundation (Haidt and Graham, 2007) and at a finer level of granularity, the moral sentiment expressed towards the entities mentioned in the text. Previous studies relied on human annotation to identify morality frames in text which is expensive. In this paper, we propose prompting-based approaches using pretrained Large Language Models for identification of morality frames, relying only on few-shot exemplars. We compare our models' performance with few-shot RoBERTa and found promising results.

RS Devandiran, Akoijam Brogen Singh, Pukhrambam Romola et al.

Introduction: The medical fraternity now has more control over the processes of life and death due to advances in medical technology and equipment. Euthanasia has been debated around the world for more than half a century and it continues to raise important questions in medical ethics, moral theology, civil rights and liberty. Physicians’ attitudes to life and death emerge to relate their end-of-life decision-making, although usually carried out at the request of ailing person. Physicians’ contemplation on euthanasia is a vital building block in the path towards any change, in the euthanasia situation in a country. Aim: To determine perception and attitude towards passive euthanasia among doctors in Regional Institute of Medical Sciences, Manipur, India and to evaluate the association between attitude and variables favourable to passive euthanasia. Materials and Methods: A cross-sectional survey in the Regional Institute of Medical Sciences (tertiary care hospital), Manipur, India, between October 2018 and September 2020, in Northeast was carried out among 673 doctors. A self-administered questionnaire was designed and approved by three specialists with expertise in palliative care and medical ethics. The questionnaire had a total of 46 questions in English language, of which 15 questions were on socio-demographic profile, 13 were attitude questions and 18 were questions on perception towards passive euthanasia. Attitude questions were scored using 5-point Likert scale from strongly disagree (-2) to strongly agree (+2). Total attitude score ranges from -26 to +26. Score above zero was considered to have positive attitude and vice-versa. There was no scoring to determine perception towards euthanasia. The questionnaires were given to the doctors and postgraduate trainees of clinical and non clinical specialities in a tertiary care hospital in North Eastern India during their work hours. Data were summarised using descriptive statistics. Chi-square test was used to assess factors favouring attitudes toward passive euthanasia. Results: Age of the respondents ranged from 24 years to 63 years, with a mean age of 37.1±10.7 years and mean duration of experience was eight years. Out of 577 respondents, 368 (63.8%) were postgraduate trainees and 209 (36.2%) were doctors. Majority (463, 80.2%) of the respondents had positive attitude, 97 (16.9%) had negative attitude and 17 (2.9%) had neutral attitude. Total 543 (94.1%) respondents agreed that declaration from patient/family members must be obtained before the act of passive euthanasia. Also, the quality of life as viewed by the patient himself (452, 78.3%) and humanitarian basis (372, 64.4%) were the important factors in influencing decision making regarding passive euthanasia on a terminally ill patient. There was no significant association between sex, religion, specialisation, Intensive Care Unit (ICU) experience and attitude towards passive euthanasia. Conclusion: Majority of the respondents had positive attitude towards passive euthanasia in the face of intractable suffering and terminal illness. Hastened death looks easier to the patients and family because of physical suffering and financial burdens they are subjected to. The doctors got request for euthanasia by the patients and relatives which reflects the public awareness on euthanasia.

Aydın Fındık

Bu çalışma Mehmet Kenan Şahin’in kaleme aldığı “İmâm Mâtürîdî’nin İman ve Ahlâk Anlayışı (Te’vilâtü’l-Kur’ân ve Kitâbü’t-Tevhîd Bağlamında)” isimli eserini içermektedir. Şahin, Mâtürîdî’nin iman, amel, insan ve ahlâk anlayışı hakkındaki görüşlerini onun Kitâbü’t-Tevhîd ve Te’vîlâtü’l-Ku’rân adlı iki önemli eserini dikkate alarak belirlemeye çalışmıştır. Şahin, İmâm Mâtürîdî’nin iman tanımını yaşadığı dönemdeki iman anlayışlarından daha farklı bir yaklaşımla izah ettiğini savunmaktadır. Bununla birlikte inceleyeceğimiz bu çalışmada yazar, Mâtürîdî’nin eserlerinde sıklıkla imân ile ahlâkî yaşam arasında çok sıkı bir bağ kurduğuna dikkat çekmiştir.

Mark Graves, Noreen Herzfeld

and characterize its historically contextualized moral norms. As an initial foray into development of an integrative framework, I describe an AI system that could plausibly be constructed with effort comparable to other major AI initiatives, and that would have the capacity to consider itself as a moral actor (a precursor to moral agency).2 Constructing such a system would open up new possibilities for moral AI, enable sophisticated modeling of human morality, and lead to new insights into ethics and moral behavior. Closer at hand, my proposal identifies issues in AI and morality that require both computational and ethical expertise to resolve and are not well known and understood across the necessary disciplines.