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Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework

Olivier Binette, Jerome P. Reiter

TL;DR

The paper addresses construct validity in AI/ML evaluations, where benchmark performance often misrepresents real-world capability. It adapts the estimands framework from clinical trials to ML, providing a structured, target-focused approach that links evaluation objectives to data collection, estimation, uncertainty, and reporting. Through three rank-reversal examples—cross-validation, clustering, and LLM benchmarking—it demonstrates how ill-defined targets can produce misleading model rankings and shows how estimand-based reasoning clarifies causes and points to actionable remedies, including subgroup-specific estimands, unbiased population-based estimators, and multi-criteria decision-making. The proposed framework promises more meaningful, cost-efficient, and governance-ready evaluations that better support decision-makers and model users in deploying AI systems.

Abstract

Commonly, AI or machine learning (ML) models are evaluated on benchmark datasets. This practice supports innovative methodological research, but benchmark performance can be poorly correlated with performance in real-world applications -- a construct validity issue. To improve the validity and practical usefulness of evaluations, we propose using an estimands framework adapted from international clinical trials guidelines. This framework provides a systematic structure for inference and reporting in evaluations, emphasizing the importance of a well-defined estimation target. We illustrate our proposal on examples of commonly used evaluation methodologies - involving cross-validation, clustering evaluation, and LLM benchmarking - that can lead to incorrect rankings of competing models (rank reversals) with high probability, even when performance differences are large. We demonstrate how the estimands framework can help uncover underlying issues, their causes, and potential solutions. Ultimately, we believe this framework can improve the validity of evaluations through better-aligned inference, and help decision-makers and model users interpret reported results more effectively.

Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework

TL;DR

The paper addresses construct validity in AI/ML evaluations, where benchmark performance often misrepresents real-world capability. It adapts the estimands framework from clinical trials to ML, providing a structured, target-focused approach that links evaluation objectives to data collection, estimation, uncertainty, and reporting. Through three rank-reversal examples—cross-validation, clustering, and LLM benchmarking—it demonstrates how ill-defined targets can produce misleading model rankings and shows how estimand-based reasoning clarifies causes and points to actionable remedies, including subgroup-specific estimands, unbiased population-based estimators, and multi-criteria decision-making. The proposed framework promises more meaningful, cost-efficient, and governance-ready evaluations that better support decision-makers and model users in deploying AI systems.

Abstract

Commonly, AI or machine learning (ML) models are evaluated on benchmark datasets. This practice supports innovative methodological research, but benchmark performance can be poorly correlated with performance in real-world applications -- a construct validity issue. To improve the validity and practical usefulness of evaluations, we propose using an estimands framework adapted from international clinical trials guidelines. This framework provides a systematic structure for inference and reporting in evaluations, emphasizing the importance of a well-defined estimation target. We illustrate our proposal on examples of commonly used evaluation methodologies - involving cross-validation, clustering evaluation, and LLM benchmarking - that can lead to incorrect rankings of competing models (rank reversals) with high probability, even when performance differences are large. We demonstrate how the estimands framework can help uncover underlying issues, their causes, and potential solutions. Ultimately, we believe this framework can improve the validity of evaluations through better-aligned inference, and help decision-makers and model users interpret reported results more effectively.
Paper Structure (35 sections, 6 equations, 2 figures, 3 tables)

This paper contains 35 sections, 6 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Definitions and Related Work
  4. ML Evaluation as a Discipline
  5. Three Examples Leading to Rank Reversals
  6. Defining Performance Rank Reversals
  7. Rank Reversals With Cross-Validation
  8. Background on Cross-Validation
  9. Example: The California Housing Dataset
  10. Rank Reversals in Clustering Evaluation
  11. Background on Clustering Evaluation
  12. Example: Identity Clustering Based on Face Images
  13. Rank Reversals in LLM Benchmarking
  14. Background on Benchmarking Large Language Models
  15. Example: The Format-Following Benchmark Dataset
  16. ...and 20 more sections

Figures (2)

  • Figure 1: Estimands framework adapted from ICH2019 for ML model evaluation, as described in Section \ref{['sec:estimand-framework']}. An evaluation objective is translated to an estimand. An estimand is characterized by (A) a metric or choice of measurement, (B) a specific scope (a population) to contextualize the metric, (C) a data acquisition strategy (including how missing data, data annotation inconsistencies, and other data issues are handled), and (D) an aggregation/summarization of the metric values over the given scope/population. Next, a main estimator is chosen to provide a sufficiently accurate estimate at minimal cost. Uncertainty regarding the estimation procedure can be separately or jointly estimated, accounting for sensitivity to the choice of the main estimator and its underlying assumptions.
  • Figure 2: Comparing the distribution of features between the full California Housing Dataset and a training dataset of $2,000$ random examples. Notice the four heavy-tailed features: the average number of rooms, the average number of bedrooms, the block group population, and the average occupation. Census block groups with outlying average occupation numbers are not represented in the training dataset.

Theorems & Definitions (2)

  • Definition 1: Rank Reversals
  • Remark 1