A Cartel of Influential Datasets Is Dominating Machine Learning Research, New Study Suggests

A new paper from the University of California and Google Research has found that a small number of ‘benchmark’ machine learning datasets, largely from influential western institutions, and frequently from government organizations, are increasingly dominating the AI research sector.

The researchers conclude that this tendency to ‘default’ to highly popular open source datasets, such as ImageNet, brings up a number of practical, ethical and even political causes for concern.

Among their findings – based on core data from the Facebook-led community project Papers With Code (PWC) –  the authors contend that ‘widely-used datasets are introduced by only a handful of elite institutions’, and that this ‘consolidation’ has increased to 80% in recent years.

‘[We] find that there is increasing inequality in dataset usage globally, and that more than 50% of all dataset usages in our sample of 43,140 corresponded to datasets introduced by twelve elite, primarily Western, institutions.’

Read more

Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.