Contextualized moral inference

TL;DR

This paper introduces a text-based method leveraging contextualized language models to predict human moral judgments in everyday situations, demonstrating improved inference over traditional methods across multiple datasets.

Contribution

It presents a novel approach using contextualized language models for automated moral inference, advancing the field of AI moral reasoning.

Findings

Contextualized representations outperform word embeddings in moral judgment inference.

The approach shows significant accuracy improvements across three moral psychology datasets.

Discussion of the potential and limitations of automated textual moral reasoning.

Abstract

Developing moral awareness in intelligent systems has shifted from a topic of philosophical inquiry to a critical and practical issue in artificial intelligence over the past decades. However, automated inference of everyday moral situations remains an under-explored problem. We present a text-based approach that predicts people's intuitive judgment of moral vignettes. Our methodology builds on recent work in contextualized language models and textual inference of moral sentiment. We show that a contextualized representation offers a substantial advantage over alternative representations based on word embeddings and emotion sentiment in inferring human moral judgment, evaluated and reflected in three independent datasets from moral psychology. We discuss the promise and limitations of our approach toward automated textual moral reasoning.