Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for machine learning model fairness and explainability. In some implementations, a method includes obtaining data relating to a plurality of potential borrowers; providing the data to the trained machine learning model; obtaining, by the trained machine learning model’s processing of the provided data, the one or more outputs of the trained machine learning model; and automatically generating a report that explains the one or more outputs of the trained machine learning model with respect to one or more fairness metrics and one or more accuracy metrics; and providing the automatically generated report for display on a user device.

Abstract: An actuator includes a brushless DC motor, an output device, a reduction system coupled between the brushless DC motor and the output device, and a contactless position sensor configured to sense a position of the output device.

Abstract: A pump health monitoring system for an aircraft fuel pump. The health monitoring system compares environmental data to a reference environment and captures pump data when the environmental data matches the reference environment. The pump data captured is therefore normalized to the reference environment. A pump remaining useful life is calculated from the normalized pump data.

Abstract: An actuator includes a brushless DC motor, an output device, a reduction system coupled between the brushless DC motor and the output device, and a contactless position sensor configured to sense a position of the output device.

Abstract: A pump health monitoring system for an aircraft fuel pump. The health monitoring system compares environmental data to a reference environment and captures pump data when the environmental data matches the reference environment. The pump data captured is therefore normalised to the reference environment. A pump remaining useful life is calculated from the normalised pump data.