# Measurement to Meaning: A Validity-Centered Framework for AI Evaluation

arXiv: https://arxiv.org/abs/2505.10573 · HTML: https://mlsanity.com/paper/2505.10573/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2025.

## Built on by (most cited first, 30 of 57)

- [Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluations](https://mlsanity.com/paper/2601.17087/) (2026)
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- [The Benchmarking Epistemology: Validity Theory for Evaluating Machine Learning Models](https://mlsanity.com/paper/2510.23191/) (2025)
- [Train-before-Test Harmonizes Language Model Rankings](https://mlsanity.com/paper/2507.05195/) (2025)
- [Doubly-Robust LLM-as-a-Judge: Externally Valid Estimation with Imperfect Personas](https://mlsanity.com/paper/2509.22957/) (2025)
- [Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor](https://mlsanity.com/paper/2506.14652/) (2025)
- [Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead](https://mlsanity.com/paper/2507.23009/) (2025)
- [Establishing Construct Validity in LLM Capability Benchmarks Requires Nomological Networks](https://mlsanity.com/paper/2603.15121/) (2026)
- [ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents](https://mlsanity.com/paper/2605.20251/) (2026)
- [Measuring What Matters: Connecting AI Ethics Evaluations to System Attributes, Hazards, and Harms](https://mlsanity.com/paper/2510.10339/) (2025)
- [Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations](https://mlsanity.com/paper/2511.05613/) (2025)
- [Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting](https://mlsanity.com/paper/2606.09809/) (2026)
- [Scalable and Reliable Evaluation of AI Knowledge Retrieval Systems: RIKER and the Coherent Simulated Universe](https://mlsanity.com/paper/2601.08847/) (2025)
- [Overseeing Agents Without Constant Oversight: Challenges and Opportunities](https://mlsanity.com/paper/2602.16844/) (2026)
- [Leaderboard Incentives: Model Rankings under Strategic Post-Training](https://mlsanity.com/paper/2603.08371/) (2026)
- [AI Evaluation Should Require Standardized Item-Level Data Releases](https://mlsanity.com/paper/2604.03244/) (2026)
- [AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems](https://mlsanity.com/paper/2605.25272/) (2026)
- [Aggregation Hides Out-of-Distribution Generalization Failures from Spurious Correlations](https://mlsanity.com/paper/2510.24884/) (2025)
- [CIRCLE: A Framework for Evaluating AI from a Real-World Lens](https://mlsanity.com/paper/2602.24055/) (2026)
- [No-Knowledge Alarms for Misaligned LLMs-as-Judges](https://mlsanity.com/paper/2509.08593/) (2025)
- [SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing](https://mlsanity.com/paper/2603.01630/) (2026)
- [Making AI Evaluation Deployment Relevant Through Context Specification](https://mlsanity.com/paper/2603.06811/) (2026)
- [BenchBrowser: Retrieving Evidence for Evaluating Benchmark Validity](https://mlsanity.com/paper/2603.18019/) (2026)
- [Efficient Detection of Bad Benchmark Items with Novel Scalability Coefficients](https://mlsanity.com/paper/2603.24999/) (2026)
- [FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition](https://mlsanity.com/paper/2604.19357/) (2026)
- [When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels](https://mlsanity.com/paper/2605.06652/) (2026)
- [Quality Is Not a Safety Proxy Under Quantization](https://mlsanity.com/paper/2606.10154/) (2026)

## Abstract (arXiv)

While the capabilities and utility of AI systems have advanced, rigorous norms for evaluating these systems have lagged. Grand claims, such as models achieving general reasoning capabilities, are supported with model performance on narrow benchmarks, like performance on graduate-level exam questions, which provide a limited and potentially misleading assessment. We provide a structured approach for reasoning about the types of evaluative claims that can be made given the available evidence. For instance, our framework helps determine whether performance on a mathematical benchmark is an indication of the ability to solve problems on math tests or instead indicates a broader ability to reason. Our framework is well-suited for the contemporary paradigm in machine learning, where various stakeholders provide measurements and evaluations that downstream users use to validate their claims and decisions. At the same time, our framework also informs the construction of evaluations designed to speak to the validity of the relevant claims. By leveraging psychometrics' breakdown of validity, evaluations can prioritize the most critical facets for a given claim, improving empirical utility and decision-making efficacy. We illustrate our framework through detailed case studies of vision and language model evaluations, highlighting how explicitly considering validity strengthens the connection between evaluation evidence and the claims being made.