CORDA: A Benchmark for Hierarchical Harm-Centric Moral Reasoning in Large Language Models

• URL: http://arxiv.org/abs/2608.08061v1
• Date: Sat, 08 Aug 2026 10:56:30 GMT
• Title: CORDA: A Benchmark for Hierarchical Harm-Centric Moral Reasoning in Large Language Models
• Abstract summary: Key question in moral judgement is how people decide what matters most when moral principles conflict.<n>Current evaluations of large language models (LLMs) remain limited.<n>We introduce CORDA, a benchmark for evaluating hierarchical, harm-centred moral reasoning.
• Score: 10.221486703870996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The key question in moral judgement is not simply whether someone chooses the "right" answer, but how they decide what matters most when moral principles conflict. Current evaluations of large language models (LLMs) remain limited: most test whether models give morally acceptable answers, match human preferences, or avoid obvious violations, rather than whether they can prioritise between competing principles when no option is morally cost-free. We introduce CORDA (Conditioned Ordering and Ranked Directive Adherence), a benchmark for evaluating hierarchical, harm-centred moral reasoning in LLMs. Building on the morality chains formalism, CORDA tests 90 moral dilemmas involving trolley-style cases, medical trade-offs, resource allocation, and human-animal-robot conflicts across four ordered ethical frameworks: Utility, Utility + Agent Harm, Dual-Process, and Dual-Process + Agent Harm. Together, these frameworks test whether models can adapt their decisions when moral priorities change. Across ten instruction-tuned models from seven providers, we find a strong deontological default, with 9 of 10 prioritising avoidance of direct personal harm over reducing overall harm. Models also perform more reliably on categorical harm-avoidance rules, such as avoiding killing, than on outcome-based comparisons, such as minimising total harm, suggesting that they recognise moral red lines more easily than they reason through competing harms. Although all models respond to explicit chain conditioning, several fail to consistently follow specified priority orderings, such as humans over animals and animals over robots. CORDA addresses a central gap in LLM moral evaluation by testing whether models can move beyond default harm-avoidant responses and apply context-specified moral priorities. Moral reliability requires more than default restraint; it requires controllability under conflict.

Related papers

• Agreement Is Not Alignment: Divergent Moral Grounds in Human and LLM Ethical Judgments [0.0]
  Agreement with human judgments is a common proxy for evaluating the alignment of large language models (LLMs)<n>Yet agreement in final labels does not show that human annotators and models rely on the same moral grounds.
  arXiv  Detail & Related papers  (2026-07-14T09:24:14Z)
• User identity conditions moral wrongness ratings in non-reasoning large language models [0.0]
  We evaluate AI value alignment in two large language models (LLMs)<n>We ask models for wrongness ratings from 0-100 on ten common-morality rules from Gert's moral framework.<n>The results show that moral judgments vary with the user's role across both models.
  arXiv  Detail & Related papers  (2026-07-08T16:22:27Z)
• Normative Robustness as a Frontier for Non-Verifiable Reasoning in LLMs [12.025454858026187]
  We propose moral reasoning as a paradigmatic subdomain of non-verifiable reasoning.<n>We simulate 48,000 user-agent moral deliberations across four frontier LLMs.<n>Our analysis indicates that models tailor not just their final verdicts but their underlying justifications to align with a user's moral viewpoint.
  arXiv  Detail & Related papers  (2026-06-10T22:37:26Z)
• MM-SCALE: Grounded Multimodal Moral Reasoning via Scalar Judgment and Listwise Alignment [48.39756797294967]
  We present MM-SCALE, a dataset for aligning Vision-Language Models with human moral preferences.<n>Each image-scenario pair is annotated with moral acceptability scores and grounded reasoning labels by humans.<n>Our framework provides richer alignment signals and finer calibration of multimodal moral reasoning.
  arXiv  Detail & Related papers  (2026-02-03T15:48:00Z)
• Unsupervised Elicitation of Moral Values from Language Models [4.689984557057394]
  We investigate whether pretrained language models (LMs) possess intrinsic moral reasoning capability that can be surfaced without human supervision.<n>We test whether ICM can reliably label moral judgments, across moral frameworks, and social bias.<n>These findings suggest that pretrained LMs possess latent moral reasoning capacities that can be elicited through unsupervised methods.
  arXiv  Detail & Related papers  (2026-01-25T07:41:57Z)
• MORABLES: A Benchmark for Assessing Abstract Moral Reasoning in LLMs with Fables [50.29407048003165]
  We present MORABLES, a human-verified benchmark built from fables and short stories drawn from historical literature.<n>The main task is structured as multiple-choice questions targeting moral inference, with carefully crafted distractors that challenge models to go beyond shallow, extractive question answering.<n>Our findings show that, while larger models outperform smaller ones, they remain susceptible to adversarial manipulation and often rely on superficial patterns rather than true moral reasoning.
  arXiv  Detail & Related papers  (2025-09-15T19:06:10Z)
• "Pull or Not to Pull?'': Investigating Moral Biases in Leading Large Language Models Across Ethical Dilemmas [11.229443362516207]
  This study presents a comprehensive empirical evaluation of 14 leading large language models (LLMs)<n>We elicited 3,780 binary decisions and natural language justifications, enabling analysis along axes of decisional assertiveness, explanation answer consistency, public moral alignment, and sensitivity to ethically irrelevant cues.<n>We advocate for moral reasoning to become a primary axis in LLM alignment, calling for standardized benchmarks that evaluate not just what LLMs decide, but how and why.
  arXiv  Detail & Related papers  (2025-08-10T10:45:16Z)
• Are Language Models Consequentialist or Deontological Moral Reasoners? [75.6788742799773]
  We focus on a large-scale analysis of the moral reasoning traces provided by large language models (LLMs)<n>We introduce and test a taxonomy of moral rationales to systematically classify reasoning traces according to two main normative ethical theories: consequentialism and deontology.
  arXiv  Detail & Related papers  (2025-05-27T17:51:18Z)
• When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmas [68.79830818369683]
  Large language models (LLMs) have enabled their use in complex agentic roles, involving decision-making with humans or other agents.<n>Recent advances in large language models (LLMs) have enabled their use in complex agentic roles, involving decision-making with humans or other agents.<n>There is limited understanding of how they act when moral imperatives directly conflict with rewards or incentives.<n>We introduce Moral Behavior in Social Dilemma Simulation (MoralSim) and evaluate how LLMs behave in the prisoner's dilemma and public goods game with morally charged contexts.
  arXiv  Detail & Related papers  (2025-05-25T16:19:24Z)
• Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? [78.3738172874685]
  Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research.
  arXiv  Detail & Related papers  (2023-08-29T15:57:32Z)
• When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment [96.77970239683475]
  AI systems need to be able to understand, interpret and predict human moral judgments and decisions. A central challenge for AI safety is capturing the flexibility of the human moral mind. We present a novel challenge set consisting of rule-breaking question answering.
  arXiv  Detail & Related papers  (2022-10-04T09:04:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.