The disclosure relates to method and system for determining correctness of predictions performed by deep learning model. The method includes extracting a neuron activation pattern of a layer of the deep learning model with respect to the input data, and generating an activation vector based on the extracted neuron activation pattern. The method further includes determining the correctness of the prediction performed by the deep learning model with respect to the input data using a prediction validation model and based on the activation vector. The prediction validation model is a machine learning model that has been generated and trained using training activation vectors derived from correctly predicted test dataset and incorrectly predicted test dataset of the deep learning model. The method further includes providing the correctness of the prediction performed by the deep learning model with respect to the input data for subsequent rendering or subsequent processing.

Fair deep reinforcement learning is provided. A microstate of an environment and reaction of items in a plurality of microstates within the environment are observed after an agent performs an action in the environment. Semi-supervised training is utilized to determine bias weights corresponding to the action for the microstate of the environment and the reaction of the items in the plurality of microstates within the environment. The bias weights from the semi-supervised training are merged with non-bias weights using an artificial neural network. Over time, it is determined where bias is occurring in the semi-supervised training based on merging the bias weights with the non-bias weights in the artificial neural network. A deep reinforcement learning model that decreases reliance on the bias weights is generated based on determined bias to increase fairness.

Systems and methods are provided for estimating arrival time associated with a ride order. An exemplary method may comprise: inputting transportation information to a trained machine learning model. The transportation information may comprise an origin and a destination associated with the ride order, and the trained machine learning model may comprise a wide network, a deep neural network, and a recurrent neural network all coupled to a multilayer perceptron network. The method may further comprise, based on the trained machine learning model, obtaining an estimated time for arriving at the destination via a route connecting the origin and the destination.

In an approach for building a machine learning model that predicts the appropriate action to resolve a malfunction or system error, a processor receives an alert that a malfunction or a system error has occurred. A processor creates a workspace on a ChatOps platform integrated with a chatbot and one or more tools. A processor inputs data relating to the alert in a natural language format. A processor processes the data using a natural language processing algorithm. Responsive to determining a pre-set threshold for outputting the appropriate action is not met, a processor establishes a conversation between two or more support service agents in the workspace. A processor monitors the conversation using the natural language processing algorithm. A processor analyzes a transcript of the conversation using text analytics or pattern matching. A processor creates and trains a machine learning model to predict the appropriate action in future iterations.