Accounting for Context: Shaping Moral Credences for Value Alignment

• URL: http://arxiv.org/abs/2606.06972v1
• Date: Fri, 05 Jun 2026 07:01:36 GMT
• Title: Accounting for Context: Shaping Moral Credences for Value Alignment
• Abstract summary: This paper argues that one needs to account for contextual factors when aggregating moral evaluations.<n>We formalise agent decision making under moral uncertainty, while also accounting for these kinds of contextual factors.<n>We argue that this apparent problem is, in fact, a variation of Simpson's paradox, and hence reveals the limitations of aggregation mechanisms.
• Score: 3.7527745397166767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring that agent behaviours are aligned with human moral values inevitably raises the problem of how to account for the plurality of moral perspectives that societies -- and even individuals -- typically adopt. Work on moral uncertainty proposes mechanisms to fairly and democratically aggregate evaluations of actions across different moral theories. However, this paper argues that one needs to account for contextual factors when aggregating moral evaluations. For example, consequentialist perspectives assume an ability to accurately determine how an agent's actions change the world; an assumption that often does not hold in real world settings. We, therefore, formalise agent decision making under moral uncertainty, while also accounting for these kinds of contextual factors. We thereby show that a seemingly commonsensical property -- the weak Pareto principle -- is violated. We argue that this apparent problem is, in fact, a variation of Simpson's paradox, and hence reveals the limitations of aggregation mechanisms that ignore the impact of contextual factors.

Related papers

• How do LLMs Evaluate Perceived Moral Agency? Investigating Moral Decision-Making in Human-Artificial Agents Interactions [66.41429744325532]
  Humans possess moral agency, the capacity to make ethically guided decisions and bear responsibility for their consequences.<n>As artificial agents (AAs) such as robots, drones, and disembodied AI systems become embedded in smart city environments, the question of whether and how moral agency is attributed to them takes on new urgency.<n>This paper presents, to the best of our knowledge, the first empirical study comparing how humans and LLMs evaluate perceived moral agency (PMA) across human and autonomous artificial agents varying in embodiment.
  arXiv  Detail & Related papers  (2026-09-04T11:56:59Z)
• Incoherent by Design? On the Moral Self-Consistency of LLMs [1.396799748514919]
  LLMs are increasingly used in morally sensitive contexts.<n>It is unclear whether they apply ethical principles consistently across situations.<n>A model that can state a moral principle may still violate it when the same scenario is rephrased or reframed.
  arXiv  Detail & Related papers  (2026-08-15T18:16:52Z)
• 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)
• Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions [14.29792672073981]
  We characterize machine behavior using the Whistleblower's Dilemma by varying two experimental dimensions: crime severity and relational closeness.<n>Our study evaluates three distinct perspectives: (1) moral rightness (prescriptive norms), (2) predicted human behavior (descriptive social expectations), and (3) autonomous model decision-making.
  arXiv  Detail & Related papers  (2026-04-23T17:14:18Z)
• "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)
• What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations [48.686872351114964]
  Moral or ethical judgments rely heavily on the specific contexts in which they occur. We introduce defeasible moral reasoning: a task to provide grounded contexts that make an action more or less morally acceptable. We distill a high-quality dataset of 1.2M entries of contextualizations and rationales for 115K defeasible moral actions.
  arXiv  Detail & Related papers  (2023-10-24T00:51:29Z)
• 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)
• ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations [81.70195684646681]
  We present ClarifyDelphi, an interactive system that learns to ask clarification questions. We posit that questions whose potential answers lead to diverging moral judgments are the most informative. Our work is ultimately inspired by studies in cognitive science that have investigated the flexibility in moral cognition.
  arXiv  Detail & Related papers  (2022-12-20T16:33:09Z)
• Morality, Machines and the Interpretation Problem: A value-based, Wittgensteinian approach to building Moral Agents [0.0]
  We argue that the attempt to build morality into machines is subject to what we call the Interpretation problem. We argue that any rule we give the machine is open to infinite interpretation in ways that we might morally disapprove of.
  arXiv  Detail & Related papers  (2021-03-03T22:34:01Z)
• On the Moral Justification of Statistical Parity [0.0]
  A crucial but often neglected aspect of fairness is the question of how we justify enforcing a certain fairness metric from a moral perspective. Our aim in this paper is to consider the moral aspects associated with the statistical fairness criterion of independence.
  arXiv  Detail & Related papers  (2020-11-04T00:26:15Z)
• Reinforcement Learning Under Moral Uncertainty [13.761051314923634]
  An ambitious goal for machine learning is to create agents that behave ethically. While ethical agents could be trained by rewarding correct behavior under a specific moral theory, there remains widespread disagreement about the nature of morality. This paper proposes two training methods that realize different points among competing desiderata, and trains agents in simple environments to act under moral uncertainty.
  arXiv  Detail & Related papers  (2020-06-08T16:40:12Z)

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.