5. Assumptions about dataset accuracy are risky
Leaderboards inherently assume the datasets they use are accurate and relevant. Yet, benchmark data often contains outdated information, inaccuracies or inherent biases. Take healthcare AI as an example — medical knowledge evolves rapidly, and a dataset from several years ago might be obsolete when it comes to current standards of care. Despite this, outdated benchmarks continue to be used because of their widespread integration into testing pipelines, leading to evaluations based on outdated criteria.
6. Real-world considerations are often ignored
A high leaderboard score doesn’t tell you how well a model will perform in production environments. Critical factors such as system latency, resource consumption, data security, compliance with legal standards and licensing terms are often overlooked. It’s not uncommon for teams to adopt a high-ranking model, only to later discover it’s based on restricted datasets or incompatible licenses. These deployment realities play a huge role in determining a model’s viability in practice far more than a leaderboard ranking does.
While leaderboards provide useful signals, especially for academic benchmarking, they should be considered just one part of a larger evaluation framework. A more comprehensive approach should include testing with real-world, domain-specific datasets; assessing robustness against edge cases and unexpected inputs; auditing for fairness, accountability and ethical alignment; measuring operational efficiency and scalability; and engaging domain experts for human-in-the-loop evaluation.
