Abstract: Method comprising: receiving a trust level requirement for a service; translating the trust level requirement into a requirement for at least one of a fairness, an explainability, and a robustness of a calculation performed by an artificial intelligence pipeline related to the service; providing the requirement for the at least one of the fairness, the explainability, and the robustness to a trust manager of the artificial intelligence pipeline.

Abstract: Disclosed are various example embodiments which may be configured to: receive, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node, assign a score to the distributed node and/or determine whether the distributed node is a potential malicious distributed node based on the local dataset information, determine whether to select the distributed node for training a local model for managing a network in a federated learning mechanism based on the score assigned to the distributed node and/or whether the distributed node is a potential malicious distributed node, and send, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism.

Abstract: Example embodiments of the present disclosure relate to abnormal model behavior detection. A first apparatus obtains a machine learning model and expected behavior information of the machine learning model. The first apparatus monitors behavior information of the machine learning model during execution of the machine learning model; and determines occurrence of an abnormal behavior of the machine learning model during the execution by comparing the monitored behavior information with the expected behavior information.

Abstract: Methods and apparatus are disclosed for. A method comprises, collecting information on messages exchanged between a first mobile network and a second mobile network during a time period; and determining a trust indication of the first mobile network at least based on the collected information. The trust indication of the first mobile network indicates a level of trustworthiness of the first mobile network.

Abstract: According to an example aspect of the present invention, there is provided an apparatus configured to obtain reliability values for each user equipment in a group of user equipments, obtain, for each user equipment in the group, a reliability value for a training data set stored in the user equipment, each user equipment storing a distinct training data set, and direct a subset of the group of user equipments to separately perform a machine learning training process in the user equipments in the subset, wherein the apparatus is configured to select the subset based on the reliability values for the user equipments and the reliability values for the training data sets.

Abstract: There are provided measures for trust related management of artificial intelligence or machine learning pipelines. Such measures exemplarily include, at a first network entity managing artificial intelligence or machine learning trustworthiness in a network, transmitting a first artificial intelligence or machine learning trustworthiness related message towards a second network entity managing artificial intelligence or machine learning trustworthiness in an artificial intelligence or machine learning pipeline in the network, and receiving a second artificial intelligence or machine learning trustworthiness related message from the second network entity, where the first artificial intelligence or machine learning trustworthiness related message includes at least one criterion related to an artificial intelligence or machine learning trustworthiness aspect.

Abstract: There are provided measures for trust related management of artificial intelligence or machine learning pipelines in relation to the trustworthiness factor “explainability”. Such measures exemplarily comprise, at a first network entity managing artificial intelligence or machine learning trustworthiness in a network, transmitting a first artificial intelligence or machine learning trustworthiness related message towards a second network entity managing artificial intelligence or machine learning trustworthiness in an artificial intelligence or machine learning pipeline in said network, and receiving a second artificial intelligence or machine learning trustworthiness related message from said second network entity.