# Scaling Laws for Moral Machine Judgment in Large Language Models

arXiv: https://arxiv.org/abs/2601.17637 · HTML: https://mlsanity.com/paper/2601.17637/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2026.

## Built on by (most cited first, 3 of 3)

- [Benchmarking the Safety of Large Language Models for Robotic Health Attendant Control](https://mlsanity.com/paper/2604.26577/) (2026)
- [The Evolutionary Origin of Values: implications for AI alignment, sentience and existential risk](https://mlsanity.com/paper/2608.03361/) (2026)
- [Multiple latent orderings better predict language model preferences](https://mlsanity.com/paper/2609.22170/) (2026)

## Abstract (arXiv)

Autonomous systems increasingly require moral judgment capabilities, yet whether these capabilities scale predictably with model size remains unexplored. We systematically evaluate 75 large language model configurations (0.27B--1000B parameters) using the Moral Machine framework, measuring alignment with human preferences in life-death dilemmas. We observe a consistent power-law relationship with distance from human preferences (D) decreasing as D propto S-0.10±0.01 (R2=0.50, p<0.001) where S is model size. Mixed-effects models confirm this relationship persists after controlling for model family and reasoning capabilities. Extended reasoning models show significantly better alignment, with this effect being more pronounced in smaller models (size×reasoning interaction: p = 0.024). The relationship holds across diverse architectures, while variance decreases at larger scales, indicating systematic emergence of more reliable moral judgment with computational scale. These findings extend scaling law research to value-based judgments and provide empirical foundations for artificial intelligence governance.