AI as a Sport: On the Competitive Epistemologies of Benchmarking

Abstract

Artificial Intelligence (AI) systems are evaluated using competitive methods that rely on benchmark datasets to determine performance. These benchmark datasets, however, are often constructed through arbitrary processes that fall short in encapsulating the depth and breadth of the tasks they are intended to measure. In this paper, we interrogate the naturalization of benchmark datasets as veracious metrics by examining the historical development of benchmarking as an epistemic practice in AI research. Specifically, we highlight three key case studies that were crucial in establishing the existing reliance on benchmark datasets for evaluating the capabilities of AI systems: (1) the sharing of Highleyman's OCR dataset in the 1960s, which solidified a community of knowledge production around a shared benchmark dataset, (2) the Common Task Framework (CTF) of the 1980s, a state-led project to standardize benchmark datasets as legitimate indicators of technical progress; and (3) the Netflix Prize which further solidified benchmarking as a competitive goal within the ML research community. This genealogy highlights how contemporary dynamics and limitations of benchmarking developed from a longer history of collaboration, standardization, and competition. We end with reflections on how this history informs our understanding of benchmarking in the current era of generative artificial intelligence.