# Understanding and Estimating Domain Complexity Across Domains

arXiv: https://arxiv.org/abs/2312.13487 · HTML: https://mlsanity.com/paper/2312.13487/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [Data, Power and Bias in Artificial Intelligence](https://mlsanity.com/paper/2008.07341/) (2020)
- [Toward Defining a Domain Complexity Measure Across Domains](https://mlsanity.com/paper/2303.04141/) (2023)
- [Measuring the Complexity of Domains Used to Evaluate AI Systems](https://mlsanity.com/paper/2010.01985/) (2020)

## Abstract (arXiv)

Artificial Intelligence (AI) systems, trained in controlled environments, often struggle in real-world complexities. We propose a general framework for estimating domain complexity across diverse environments, like open-world learning and real-world applications. This framework distinguishes between intrinsic complexity (inherent to the domain) and extrinsic complexity (dependent on the AI agent). By analyzing dimensionality, sparsity, and diversity within these categories, we offer a comprehensive view of domain challenges. This approach enables quantitative predictions of AI difficulty during environment transitions, avoids bias in novel situations, and helps navigate the vast search spaces of open-world domains.