# The Value of Context: Human versus Black Box Evaluators

arXiv: https://arxiv.org/abs/2402.11157 · HTML: https://mlsanity.com/paper/2402.11157/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [AI-Assisted Decision Making with Human Learning](https://mlsanity.com/paper/2502.13062/) (2025)
- [Designing Algorithmic Delegates: The Role of Indistinguishability in Human-AI Handoff](https://mlsanity.com/paper/2506.03102/) (2025)

## Abstract (arXiv)

Machine learning algorithms are now capable of performing evaluations previously conducted by human experts (e.g., medical diagnoses). How should we conceptualize the difference between evaluation by humans and by algorithms, and when should an individual prefer one over the other? We propose a framework to examine one key distinction between the two forms of evaluation: Machine learning algorithms are standardized, fixing a common set of covariates by which to assess all individuals, while human evaluators customize which covariates are acquired to each individual. Our framework defines and analyzes the advantage of this customization -- the value of context -- in environments with high-dimensional data. We show that unless the agent has precise knowledge about the joint distribution of covariates, the benefit of additional covariates generally outweighs the value of context.