Beyond Binary Moral Judgment: Modeling Ethical Pluralism in AI

Problem
This preprint addresses the inadequacy of existing AI systems in handling moral decision-making, which typically rely on binary or scalar judgments. Such approaches fail to capture the complexity of ethical reasoning required in socially consequential contexts, lacking the necessary explanatory power and accountability. The authors propose a novel framework that models moral reasoning as a distribution over normative ethical theories, thereby addressing the gap in the literature regarding ethical pluralism in AI.

Method
The authors introduce a normative ethics simplex that integrates multiple ethical theories, specifically focusing on three broad categories: consequentialism, virtue ethics, and deontology, along with 15 fine-grained subtheories. They construct a benchmark dataset comprising 450 ethical dilemmas expressed in natural language, enriched with extracted contextual features. The core technical contribution is a two-stream normative-semantic architecture that fuses normative information with semantic embeddings. This is followed by a stacking ensemble approach that learns to classify the ethical dilemmas according to the identified theories. The training process leverages contextual and normative priors, enhancing the model’s ability to perform nuanced moral reasoning.

Results
The proposed framework achieves an accuracy of 88.89% on the benchmark dataset, significantly outperforming traditional binary classification methods. The authors conducted ablation studies demonstrating that the structured ethical representations improve performance beyond mere analogical reasoning. The stacking architecture is shown to be effective in gradually learning the granularity of ethical classifications, indicating a robust integration of contextual and normative information. The results suggest that the model can effectively capture ethical pluralism, as evidenced by analyses of entropy, confidence, and visualizations of the decision-making process.

Limitations
The authors acknowledge that their framework may not encompass all ethical theories and that the reliance on a predefined set of theories could limit its applicability in more diverse moral contexts. Additionally, the dataset, while comprehensive, may not cover all possible ethical dilemmas encountered in real-world scenarios. The authors do not address potential biases in the dataset or the implications of training on a limited set of ethical frameworks, which could affect generalizability.

Why it matters
This work has significant implications for the development of AI systems that require ethical reasoning capabilities. By modeling moral judgment as a probabilistic distribution over multiple ethical theories, the framework supports more human-like decision-making processes in AI. This approach not only enhances accountability and transparency in AI systems but also facilitates the analysis of ethical disagreements, which is crucial for future alignment efforts in AI. The integration of ethical pluralism into AI systems could lead to more nuanced and context-aware applications in critical areas such as autonomous vehicles, healthcare, and legal decision-making.

Authors: Aisha Aijaz, Rahul Goel, Arnav Batra, Raghava Mutharaju
Source: arXiv:2605.28707
URL: https://arxiv.org/abs/2605.28707v1