The Benchmarking Epistemology: Construct Validity for Evaluating Machine Learning Models

2025-10-27

AI Summary

Machine learning benchmarks commonly assume that empirical scores directly support scientific claims—e.g., about image classification capability or policy-effect prediction—yet such inferences rely on unstated theoretical assumptions. Method: This paper introduces, for the first time, the psychometric framework of construct validity to ML benchmarking, systematically formalizing the theoretical conditions under which benchmark scores can substantiate distinct levels of scientific claims: engineering improvement, causal inference, and human behavioral modeling. Contribution/Results: Through philosophical analysis and cross-validated empirical case studies—ImageNet (computer vision), WeatherBench (scientific forecasting), and the Fragile Families Challenge (social science)—the framework exposes latent assumptions underlying performance rankings. It thereby enhances the epistemic rigor and explanatory power of ML benchmarks, enabling more principled interpretation of scores as evidence for domain-specific theoretical assertions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Predictive benchmarking, the evaluation of machine learning models based on predictive performance and competitive ranking, is a central epistemic practice in machine learning research and an increasingly prominent method for scientific inquiry. Yet, benchmark scores alone provide at best measurements of model performance relative to an evaluation dataset and a concrete learning problem. Drawing substantial scientific inferences from the results, say about theoretical tasks like image classification, requires additional assumptions about the theoretical structure of the learning problems, evaluation functions, and data distributions. We make these assumptions explicit by developing conditions of construct validity inspired by psychological measurement theory. We examine these assumptions in practice through three case studies, each exemplifying a typical intended inference: measuring engineering progress in computer vision with ImageNet; evaluating policy-relevant weather predictions with WeatherBench; and examining limitations of the predictability of life events with the Fragile Families Challenge. Our framework clarifies the conditions under which benchmark scores can support diverse scientific claims, bringing predictive benchmarking into perspective as an epistemological practice and a key site of conceptual and theoretical reasoning in machine learning.

Problem

• Establish construct validity for ML benchmark evaluation
• Clarify assumptions behind scientific inferences from benchmarks
• Define conditions for valid benchmark-based scientific claims

Innovation

• Construct validity conditions from psychological measurement
• Case studies validating benchmark inference assumptions
• Framework clarifying scientific claims from benchmark scores