The Benchmarking Epistemology: Construct Validity for Evaluating Machine Learning Models

TL;DR

This paper develops a framework based on construct validity to better understand the epistemological implications of benchmarking in machine learning, emphasizing the assumptions needed for scientific inference from benchmark scores.

Contribution

It introduces conditions of construct validity for evaluating machine learning models and applies them through case studies to clarify when benchmark scores support scientific claims.

Findings

Benchmark scores alone are insufficient for scientific inference without explicit assumptions.

The framework clarifies the conditions needed for benchmarks to support diverse scientific claims.

Case studies demonstrate the application of the validity framework in different ML contexts.

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.