Mapping global dynamics of benchmark creation and saturation in artificial intelligence [* Cross-Reference *]

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Simon Ott; Adriano Barbosa-Silva; Kathrin Blagec; Jan Brauner; Matthias Samwald, 2026, "Mapping global dynamics of benchmark creation and saturation in artificial intelligence [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.FIBANO, LIST Open Repository, V1

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Description	Benchmarks are crucial to measuring and steering progress in artificial intelligence (AI). However, recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, we introduce methodologies for creating condensed maps of the global dynamics of benchmark creation and saturation. We curate data for 3765 benchmarks covering the entire domains of computer vision and natural language processing, and show that a large fraction of benchmarks quickly trends towards near-saturation, that many benchmarks fail to find widespread utilization, and that benchmark performance gains for different AI tasks are prone to unforeseen bursts. We analyze attributes associated with benchmark popularity, and conclude that future benchmarks should emphasize versatility, breadth and real-world utility. (2022-11-10) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1038/s41467-022-34591-0 for the original and latest version of the publication* (2026-07-01)
Subject	Computer and Information Science; Physics
Keyword	Benchmarking, Benchmark (surveying), Computer science, Popularity, Artificial intelligence, Overfitting, Machine learning, Artificial neural network, Data science, Data mining
License/Data Use Agreement	CC BY 4.0


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Citation Metadata

Persistent Identifier	perma:LIST.FIBANO
Publication Date	2026-07-06
Title	Mapping global dynamics of benchmark creation and saturation in artificial intelligence [* Cross-Reference *]
Other Identifier	https://doi.org/10.1038/s41467-022-34591-0 OpenAlex ID: https://openalex.org/W4308885736
Author	Simon Ott (Austrian Research Institute for Artificial Intelligence, Medical University of Vienna) - ORCID: https://orcid.org/0000-0002-9415-3969 Adriano Barbosa-Silva (Austrian Research Institute for Artificial Intelligence, Luxembourg Institute of Science and Technology, Arbed (Luxembourg), Medical University of Vienna) - ORCID: https://orcid.org/0000-0002-5260-2607 Kathrin Blagec (Austrian Research Institute for Artificial Intelligence, Medical University of Vienna) - ORCID: https://orcid.org/0000-0001-6261-5260 Jan Brauner (University of Oxford) - ORCID: https://orcid.org/0000-0002-1588-5724 Matthias Samwald (Austrian Research Institute for Artificial Intelligence, Medical University of Vienna) - ORCID: https://orcid.org/0000-0002-4855-2571
Point of Contact	Use email button above to contact. LIST QDKM (LIST)
Description	Benchmarks are crucial to measuring and steering progress in artificial intelligence (AI). However, recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, we introduce methodologies for creating condensed maps of the global dynamics of benchmark creation and saturation. We curate data for 3765 benchmarks covering the entire domains of computer vision and natural language processing, and show that a large fraction of benchmarks quickly trends towards near-saturation, that many benchmarks fail to find widespread utilization, and that benchmark performance gains for different AI tasks are prone to unforeseen bursts. We analyze attributes associated with benchmark popularity, and conclude that future benchmarks should emphasize versatility, breadth and real-world utility. (2022-11-10) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1038/s41467-022-34591-0 for the original and latest version of the publication* (2026-07-01)
Subject	Computer and Information Science; Physics
Keyword	Benchmarking Benchmark (surveying) Computer science Popularity Artificial intelligence Overfitting Machine learning Artificial neural network Data science Data mining
Topic Classification	Explainable Artificial Intelligence (XAI) Adversarial Robustness in Machine Learning Domain Adaptation and Few-Shot Learning
Deposit Date	2022-11-10
Data Type	Article
Data Source	Nature Communications

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