# Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence

arXiv: https://arxiv.org/abs/2310.12994 · HTML: https://mlsanity.com/paper/2310.12994/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Relative representations enable zero-shot latent space communication](https://mlsanity.com/paper/2209.15430/) (2022)
- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [Understanding Human Intelligence through Human Limitations](https://mlsanity.com/paper/2009.14050/) (2020)
- [Alignment with human representations supports robust few-shot learning](https://mlsanity.com/paper/2301.11990/) (2023)
- [Words are all you need? Language as an approximation for human similarity judgments](https://mlsanity.com/paper/2206.04105/) (2022)

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

- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [Learning Human-like Representations to Enable Learning Human Values](https://mlsanity.com/paper/2312.14106/) (2023)
- [Representational Alignment Supports Effective Machine Teaching](https://mlsanity.com/paper/2406.04302/) (2024)

## Abstract (arXiv)

The increasing prevalence of artificial agents creates a correspondingly increasing need to manage disagreements between humans and artificial agents, as well as between artificial agents themselves. Considering this larger space of possible agents exposes an opportunity for furthering our understanding of the nature of disagreement: past studies in psychology have often cast disagreement as two agents forming diverging evaluations of the same object, but disagreement can also arise from differences in how agents represent that object. AI research on human-machine alignment and recent work in computational cognitive science have focused on this latter kind of disagreement, and have developed tools that can be used to quantify the extent of representational overlap between agents. Understanding how divergence and misalignment interact to produce disagreement, and how resolution strategies depend on this interaction, is key to promoting effective collaboration between diverse types of agents.