BenchmarkingBias

BenchmarkingBias is a form of evaluation bias that arises when the process of benchmarking—such as the selection of benchmarks, datasets, metrics, and evaluation procedures—systematically distorts judgments about a system's capabilities. It can lead to overestimation or underestimation of performance relative to real-world tasks and to unfair comparisons between competing approaches.

Causes include selective benchmark choice that favors certain methods, overfitting to benchmarks through extensive tuning, data leakage from test to training data, metric design that incentivizes particular outcomes, and incentives from leaderboards or publication venues that reward incremental gains on narrow tasks.

Consequences include distorted conclusions about relative strengths, poor generalization to new data or domains, reduced reproducibility, misallocation of research effort, and erosion of trust in reported results.

Examples: in machine learning, models tuned to maximize performance on a fixed benchmark may underperform on novel datasets; in software and hardware benchmarking, tests may be biased toward certain architectures or workloads; in finance or economics, benchmark indices can shape research questions and reported performance.

Mitigation strategies include using diverse and representative benchmarks, preregistering evaluation plans, holding out true test sets, performing cross-domain validation, reporting multiple metrics and confidence intervals, conducting blind or independent evaluations, and making evaluation code and data openly available to enable replication.