Abstract: The present disclosure relates to a computer-implemented method for generating a machine learning module (ML-module). The method comprises providing training data comprising a first set of data corresponding to a first feature and a second set of data corresponding to a second feature and generating a further set of data, wherein the further set of data corresponds to a further feature. A first correlation metric as a measure of a correlation between a selected feature of the first and the second feature and the further feature is calculated. Furthermore, a bias metric is determined indicating a strength of a bias of the trained ML-module towards a first subset of values of the further feature or a second subset of values of the further feature if the first correlation metric is greater than a first threshold. If the bias metric satisfies the bias constraint, then the ML-module is released for usage.

Abstract: A system may be configured to perform operations to select a machine learning model. The operations may include training machine learning models with training data of a training data set and obtaining a first value representing a first required runtime for training each machine learning model. The operations may include evaluating, based on the first value, a second value representing a second required runtime for training the machine learning model with a complete training data set. The operations may include calculating a final score for each machine learning model in a group of machine learning models, wherein the calculating is performed on a basis of the second values for the machine learning models, ranking the machine learning models based on the final score to obtain ranks, and selecting the machine learning model that has obtained a highest rank in the ranking.

Abstract: A method, a computer system and a computer program product train machine learning models. The method includes coupling the machine learning system to a network and receiving. by the machine learning system via the network, a new estimator not included in the list of estimators and a respective documentation. The method also includes adding the new estimator to the list stored in memory. The method further includes reading the documentation and providing the machine learning process tool with respective extracted data and adapting, by the machine learning process tool, at least one training data set out of the group of training data sets to the new estimator on the basis of the extracted data. Lastly, the method includes training at least a subset of the machine learning models by using the new estimator, with the at least one training data set as an input, the training resulting in an output.

Abstract: Disclosed herein is a method of training an artificial intelligence model with adjustable parameters that is trained to provide an analysis result in response to receiving an input data set comprising one or more chosen variables. The method comprises: receiving a training data set comprising multiple groups of training input data paired with a training analysis result, receiving a trial analysis result from the artificial intelligence model in response to inputting the multiple groups of training input data into the artificial intelligence model, calculating an accuracy metric descriptive of a comparison between the trial analysis result and the training analysis result, calculating a fairness score metric by comparing the one or more chosen variables to the trial analysis result, calculating a combined metric from the fairness score metric and the accuracy metric, and modifying the adjustable parameters using a training algorithm that receives at least the combined metric.