Method and server for managing a dataset in the context of artificial intelligence

A server for managing a dataset, the dataset comprising data items, labelling tasks associated to the data items and labels corresponding to answers to the labelling tasks, the server comprising:

• a cluster manager;
• and
• a processing module for:
• determining an artificial intelligence (AI) model to be used on the dataset;
• creating a data mask describing a labeling status of the data items of the dataset;
• repeating a loop, until patience parameters are satisfied:
• receiving one or more trusted labels provided by one or more trusted data labelers;
• updating the data mask by changing the labeling status of the data items for which a trusted label is received;
• from a labelled data items subset obtained using the data mask, training the AI model;
• cloning the trained AI model into a local AI model on the processing nodes;
• from an unlabelled data items subset obtained using the data mask, creating a randomized unlabeled subset having fewer members than the unlabelled data items subset;
• chunking, using the cluster manager, the randomized unlabeled subset into a plurality of data subsets for dispatching to one or more of the processing nodes;
• and
• receiving, through the cluster manager, an indication that one or more predicted label answers have been inferred by the one or more processing nodes using the local AI model;
• computing a model uncertainty measurement from statistical analysis of the one or more predicted label answers;
• wherein the patience parameters include one or more of:
• a threshold value on the model uncertainty measurement and information gain between different training cycles.