Accounting for Context: Shaping Moral Credences for Value Alignment
Jazon Szabo, Sanjay Modgil
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.
AI Impact Assessments
(1 models)Scientific Impact Assessment
Core Contribution
This paper argues that when aggregating moral evaluations across different ethical theories for AI value alignment, one must account for contextual factors that differentially affect the reliability or appropriateness of those theories. The authors formalize this idea by introducing credence profiles (action-specific credence functions), contextual features (functions mapping action-theory pairs to reliability scores), and adjustment functions (`prod` and `mini`) that update initial moral credences based on context. The central formal result is that Maximising Expected Choiceworthiness (MEC), combined with context-surjective adjustment functions, violates the weak Pareto principle. The authors interpret this not as a deficiency but as an instance of Simpson's paradox, arguing it reveals the limitations of context-insensitive aggregation.
Methodological Rigor
The formalization is clean and well-structured. The definitions build naturally from the moral uncertainty framework of MacAskill, Bykvist, and Ord (2020), extending it with credence profiles and contextual features. The proofs of Theorems 13–16 and the supporting Lemma 18 are complete and appear correct. The proof strategy is straightforward: showing MEC is inter-theoretically responsive and that both adjustment functions are context-surjective, then combining these to derive the Pareto violation.
However, there are concerns about the strength and novelty of the formal results:
1. Context surjectivity is extremely permissive. The property essentially says that context can transform any initial credence function into any credence profile. This is almost trivially satisfied by multiplicative or min-based adjustments when contextual features can take arbitrary values in (0,1]. The Pareto violation then follows somewhat mechanically — if context can produce *any* credence profile, it can certainly produce one that reverses unanimous preferences. The result is formally correct but may overstate the practical significance: real-world contextual adjustments would presumably be constrained.
2. The Simpson's paradox analogy is intuitive and well-motivated through the Berkeley admissions example, but it functions more as an interpretive frame than a deep structural insight. The parallel is suggestive but informal — no formal mapping between the structures is provided.
3. The running FROBO example effectively illustrates the concepts but is somewhat contrived, particularly the numerical values chosen for contextual features. The paper would benefit from discussion of how such values would be determined in practice.
Potential Impact
The paper opens a genuinely interesting conceptual direction: the idea that moral credences should not be static but dynamically adjusted based on deployment context is practically important for AI systems operating in diverse real-world environments. This has implications for:
The future work directions — particularly the integration with argumentation-based dialogues and "thick" ethical representations — are compelling and could lead to richer, more practically useful frameworks. The PAL personal assistant scenario is an engaging illustration of how this work could connect to LLM-based systems.
However, the practical impact is currently limited by the gap between the formal framework and implementation. The paper provides no empirical validation, no computational experiments, and no concrete methodology for determining contextual feature values in practice.
Timeliness & Relevance
The paper is highly timely. With the rapid deployment of LLMs and autonomous systems, pluralistic value alignment is a pressing concern. The paper connects to active research threads including social choice for AI alignment (Conitzer et al. 2024), pluralistic alignment (Sorensen et al. 2024), and moral uncertainty (MacAskill et al. 2020; Szabo et al. 2024). The observation that simulation-derived preferences may not transfer well to deployment contexts is practically important and underexplored.
Strengths
Limitations
Overall Assessment
This paper makes a valuable conceptual contribution by highlighting the importance of context in moral credence aggregation for AI alignment, and provides a clean formal framework. However, the formal results, while correct, are somewhat straightforward given the permissiveness of context surjectivity. The paper would be significantly strengthened by exploring restricted classes of contextual adjustments, providing empirical grounding, or developing the Simpson's paradox connection more formally. It is best understood as a position/framework paper that opens a research direction rather than one that delivers deep technical results.
Generated Jun 8, 2026
Comparison History (19)
Paper 1 likely has higher near-term scientific impact: it proposes a concrete, modular generative framework for dynamic OD flow synthesis, validated on large-scale real datasets with reported gains and released code, enabling reproducibility and adoption in transportation, urban computing, and spatiotemporal ML. Its plug-and-play design and cross-city transfer claims broaden practical applicability. Paper 2 offers a valuable conceptual/formal critique in moral uncertainty and alignment, but its impact may be more niche and slower-moving without empirical validation or widely deployable artifacts.
Paper 1 addresses a foundational and highly timely challenge in AI safety: value alignment under moral uncertainty. By integrating contextual factors into moral aggregation and identifying a novel variation of Simpson's paradox, it offers deep theoretical insights with broad implications across AI ethics, philosophy, and decision theory. While Paper 2 presents a rigorous and practical solution for traffic prediction, Paper 1's focus on shaping the ethical behavior of autonomous agents has a profoundly wider potential impact on the long-term societal integration of artificial intelligence.
Paper 1 addresses a fundamental theoretical problem in AI value alignment—how contextual factors affect moral aggregation under uncertainty—connecting to Simpson's paradox and the weak Pareto principle. This has broad implications across AI ethics, social choice theory, and multi-agent systems. Paper 2, while technically competent, presents an engineering optimization framework for a specific motor design domain with narrower impact. Paper 1's theoretical contributions are more likely to influence multiple research communities and shape foundational thinking in the growing field of AI alignment.
Paper 1 addresses the rapidly growing and highly applicable field of autonomous web agents. By introducing a novel, cognitively grounded architecture that achieves state-of-the-art results on a difficult benchmark, it offers practical methodologies (like the perception-cognition-action triad and structured Ledger) that are highly likely to be adopted by researchers and industry practitioners. Paper 2 offers an interesting theoretical perspective on AI value alignment, but Paper 1's concrete, high-performing system provides more immediate real-world utility and methodological advancements, suggesting a broader and faster scientific impact.
Paper 2 tackles a foundational and highly timely issue in AI safety: value alignment and moral uncertainty. Its theoretical contribution, demonstrating how contextual factors in moral aggregation relate to Simpson's paradox, has broad implications for AI ethics and decision theory. In contrast, while Paper 1 presents a practical and rigorous framework for compliance reasoning, its focus on Answer Set Programming and specific ADAS regulations limits its broader scientific impact compared to the overarching relevance of Paper 2.
Paper 1 addresses a highly timely and practically important problem—insuring autonomous AI systems—with a comprehensive framework spanning actuarial science, risk management, and policy. As agentic AI deployment accelerates, the insurance industry urgently needs such frameworks, giving this paper broad real-world impact across finance, law, regulation, and AI governance. Paper 2 makes a theoretically interesting contribution connecting moral uncertainty to Simpson's paradox, but its scope is narrower and more incremental within the existing value alignment literature. Paper 1's interdisciplinary breadth and immediate practical relevance give it higher potential impact.
Paper 2 has higher potential impact: it introduces a novel formal critique of moral-uncertainty aggregation by incorporating contextual reliability assumptions, yielding a substantive theoretical result (weak Pareto violation) linked to Simpson’s paradox. This is timely for AI value alignment and could influence decision-theoretic foundations across AI ethics, philosophy, and mechanism design. Paper 1 is valuable and applied, but is narrower (headache-literature summarization), with limited methodological scale (10 questions) and likely incremental impact relative to rapidly evolving LLM evaluation literature.
Paper 2 has higher likely impact due to a concrete, reusable artifact (open-source skill library) plus a benchmarked evaluation across multiple widely used SciVis tools, making it immediately actionable for scientific data analysis workflows. Its methodological rigor is strengthened by multi-step expert-designed tasks and comparative results on multiple agent backends, and its applications span many scientific domains that rely on visualization. Paper 1 is conceptually novel for value alignment under moral uncertainty, but is more theoretical with narrower near-term applicability and less empirical validation.
Paper 2 addresses a highly pressing challenge in modern AI: improving the reasoning capabilities of Large Vision-Language Models via reinforcement learning. By offering a scalable, empirically validated solution (PTD-PO) that avoids shortcut learning while providing dense guidance, it has immense potential for immediate real-world application in state-of-the-art model training. While Paper 1 provides valuable theoretical insights into AI value alignment, Paper 2's methodological rigor and direct relevance to rapid, high-impact advancements in LLM post-training give it a higher estimated scientific impact.
Paper 2 has higher likely impact: it tackles timely AI value alignment, introducing a formal framework for moral uncertainty that incorporates contextual epistemic limitations, and identifies a paradoxical violation (weak Pareto) linked to Simpson’s paradox—an insight with broad implications for aggregation theory, AI governance, ethics, and decision theory. Paper 1 is a useful incremental methodological refinement of TOPSIS for MCDM, with clearer near-term applications but narrower cross-field reach and likely smaller conceptual novelty.